A SRV6 path obfuscation method based on pseudo-random curvature diffusion

Abstract

The present invention discloses a SRV6 path obfuscation method based on pseudo-random curvature diffusion, comprising the following steps: by real-time monitoring of network status and path topology changes, adjusting the path selection strategy, using dynamically updated path topology and pseudo-random curvature diffusion algorithm, making the data transmission path unpredictable during execution, by combining the bandwidth, delay, load and other parameters of the link, calculating the concealment index of each link, and preferentially selecting paths with higher concealment for data transmission, thereby avoiding potential security threats. In cross-domain data transmission, the method further combines cross-domain characteristics, optimizes the path topology structure, ensures that the path is difficult to identify and resists deep traffic analysis attacks. The present invention realizes the security, stability and unpredictability of the network path by dynamically adjusting the connection relationship and topology of the path, and ensures the privacy and security of data transmission.

Description

Technical Field

[0001] The present invention relates to the field of computer network technology, and in particular to an SRV6 path obfuscation method based on pseudo-random curvature diffusion. Background Art

[0002] With the continuous development of Internet technology, the complexity of cross-domain data transmission and path selection in the network is increasing, especially the data transmission and path optimization problems in multi-domain environments, which have become an important direction of network optimization and security research. Traditional path selection algorithms are usually based on static topology structures and fixed network protocols. These methods are difficult to cope with complex network status changes in dynamic network environments, and often cannot effectively improve the concealment and anti-traffic analysis capabilities of path selection. Especially in the face of deep traffic analysis attacks and other network security threats, the path selection mechanism of existing technologies seems to be powerless and cannot guarantee the security and privacy of data transmission.

[0003] In the prior art, although the SRv6-based path selection method can achieve flexible routing and efficient traffic forwarding in IPv6 networks, its path selection relies on static topology and link status, lacks dynamic adaptability, and cannot be optimized for different attack methods. When dealing with cross-domain data transmission, the existing path selection algorithm fails to fully consider the concealment and randomness of the path and cannot effectively resist deep traffic analysis attacks. Specifically, the prior art has the following defects:

[0004] 1. Lack of dynamic adaptability in the path selection process: Existing path selection methods are usually based on predefined link status and topology structures. When faced with real-time changing network environments, they are unable to dynamically adjust path selection strategies and are unable to effectively respond to traffic analysis attacks and network status changes.

[0005] 2. Insufficient path concealment: Traditional path selection algorithms fail to fully optimize the concealment of paths, making the paths easy to identify and vulnerable to deep traffic analysis attacks. These attacks can predict the transmission path of data by analyzing the characteristics of transmitted data, thereby affecting data security.

[0006] 3. Lack of optimization of cross-domain data transmission characteristics: In the process of cross-domain data transmission, the existing technology fails to effectively consider the different transmission characteristics between domains, resulting in the path selection optimization being unable to take into account both global security and transmission efficiency, and unable to resist potential network attacks and interference.

[0007] 4. Unable to effectively control the nonlinear adjustment of path topology: Existing methods fail to introduce sufficient randomness and nonlinear characteristics in the process of adjusting path topology, which makes the path unpredictable during execution and easy to be identified by malicious attackers.

[0008] Therefore, how to provide an SRV6 path obfuscation method based on pseudo-random curvature diffusion is a problem that technicians in this field need to solve urgently. Summary of the invention

[0009] One purpose of the present invention is to propose an SRV6 path obfuscation method based on pseudo-random curvature diffusion. The present invention introduces a pseudo-random curvature diffusion algorithm, combines real-time network status and cross-domain data transmission characteristics, dynamically optimizes the path selection strategy, enhances the concealment and anti-flow analysis capabilities of the path, and ensures the safe transmission of data in a complex network environment. The method can effectively avoid deep flow analysis attacks, improve the unpredictability of the path, optimize the network load, and has the advantages of strong dynamic adaptability, high security and strong anti-interference ability.

[0010] According to an embodiment of the present invention, a SRV6 path obfuscation method based on pseudo-random curvature diffusion includes the following steps:

[0011] S1. Initialize the pseudo-random curvature diffusion model, analyze the topological structure in the SRV6 network, and construct an initial path sequence. The path sequence is generated based on the physical properties and connection relationships of nodes and links in the network.

[0012] S2, dynamically adjust the path using a pseudo-random curvature diffusion algorithm, generate a nonlinear path topology by controlling the change of path curvature, and make the path present pseudo-random characteristics when executed in the network;

[0013] S3, dynamically calculate and select the data transmission path according to the change of path topology, and adjust the path selection strategy between nodes based on the calculation results of the pseudo-random diffusion model to make the path selection process random;

[0014] S4. During the network path transmission process, the path topology changes are monitored in real time, the path node connection mode is adjusted according to the curvature diffusion calculation results, the path concealment is optimized, and the path topology structure is updated according to the dynamic changes of the path;

[0015] S5. In the process of cross-domain data transmission, the path concealment algorithm is combined to optimize the data transmission path, resist deep traffic analysis attacks, make the path difficult to identify, and optimize the risk of path exposure;

[0016] S6. According to the real-time changes of network status and path topology, the path selection strategy and pseudo-random curvature diffusion algorithm parameters are adjusted, and the unpredictability of the path during execution is achieved by dynamically updating the path topology structure and path curvature.

[0017] Optionally, the S1 specifically includes:

[0018] S11, obtaining SRV6 network topology data, extracting the identifier of each node in the network, the bandwidth, delay, load, link type and physical location attributes of the link between nodes as the basic data for initializing the network topology;

[0019] S12. Construct a network graph model, where nodes in the graph represent devices in the network, and links represent physical connections between devices. The network graph is constructed by obtaining node and link data, and represents the physical connection relationship between nodes and link properties.

[0020] S13. Assign a pseudo-random curvature value to each link , which is generated by a pseudo-random number generation algorithm based on the bandwidth of the link ,Delay and load The calculation results are:

[0021] ;

[0022] in, Indicates link The pseudo-random curvature value of , function g is a pseudo-random number generation algorithm;

[0023] S14, based on the pseudo-random curvature value of each link , calculate the path feasibility value between each pair of nodes in the network , the path feasibility value takes into account link bandwidth, delay, load and pseudo-random curvature factors:

[0024] ;

[0025] in, represents the path feasibility value between node i and node j, and function h is the path feasibility calculation function;

[0026] S15. Based on the path feasibility value The path selection process starts from the source node, gives priority to the link with higher path feasibility value, and gradually extends it until it reaches the target node to generate a preliminary path sequence.

[0027] S16. According to the topological relationship between nodes in the network, a path topology is gradually constructed. The construction of the path topology takes into account the physical connection and topological structure of the link, so that each path meets the actual physical connection requirements in the network and forms a complete data transmission path;

[0028] S17, perform pseudo-random curvature diffusion adjustment on the initially generated path sequence, based on the curvature values ​​of the links in the path sequence ,The order of path selection and link selection are adjusted through the path optimization algorithm to generate an optimized path sequence.

[0029] Optionally, the S2 specifically includes:

[0030] S21. Initialize the link curvature value of each path according to the generated optimized path sequence , and set the initial range and step size parameters of link curvature adjustment. By dynamically adjusting the link curvature value, the nonlinear characteristics of the path topology are realized. The curvature adjustment process follows the preset diffusion law;

[0031] S22, in each time step t, the link is calculated by pseudo-random curvature diffusion algorithm Adjust the curvature value :

[0032] ;

[0033] in, represents the link at time t Curvature value, For Link The change in curvature value at time t, is the adjustment factor for controlling the change amplitude of link curvature;

[0034] S23, introduce pseudo-random perturbation factor Link curvature value Perform random perturbations, with the perturbation factor based on the bandwidth of the link ,Delay ,load and the current curvature value Make a weighted adjustment:

[0035] ;

[0036] in, is the disturbance factor at time t, is the disturbance amplitude adjustment coefficient, For Link The curvature value at the previous moment;

[0037] S24, the disturbance factor In the process of adding link curvature value adjustment, the curvature value of each link Update the path topology so that the curvature of the path topology at each moment presents nonlinear change characteristics, and dynamically generate a path topology with pseudo-random characteristics;

[0038] S25. Update curvature value based on each link , recalculate the path feasibility value between any two nodes i and j in the path topology , the path feasibility value takes into account the impact of link bandwidth, delay, load and curvature adjustment:

[0039] ;

[0040] in, is the path feasibility value at time t, The current link The curvature value of

[0041] S26, based on the updated path feasibility value ,dynamically select links with feasibility values, prioritize updating the path sequence, so that the path selection presents random characteristics. The path selection process is based on the dynamic sorting of feasibility values ​​to generate a path sequence that conforms to nonlinear random characteristics;

[0042] S27. According to the real-time monitoring results of path topology and curvature value changes, dynamically adjust the link curvature adjustment parameters and path selection strategy, and make the path topology unpredictable by changing the control factor of the curvature diffusion model. At the same time, optimize the path update strategy in time to prevent the path information from being leaked in advance.

[0043] Optionally, the S3 specifically includes:

[0044] S31. According to the dynamic changes of the path topology, the feasible path set between each pair of nodes i and j is updated in real time. The path set contains all feasible paths that meet the current network conditions. In the path selection process, links that meet the network constraints are given priority, while ensuring that the nonlinear characteristics of the path topology are preserved;

[0045] S32, based on the calculation results of the pseudo-random diffusion model, combined with the current state parameters of the network, dynamically calculate the path weight , the weight reflects the priority of path selection:

[0046] ;

[0047] in, is the path weight at time t, For Link bandwidth, is the link delay, is the load of the link, For Link The curvature value at time t, and is the weight adjustment factor;

[0048] S33, according to the dynamically calculated path weight, adopt the priority selection principle of maximizing the path weight, select the optimal path from the feasible path set, in the path selection process, give priority to the link with a higher weight value, and build a transmission path from the source node to the target node;

[0049] S34. In the process of path selection, the disturbance factor introduced by the pseudo-random diffusion model , randomize the path selection, and the disturbance factor is weighted according to the link bandwidth, delay, load and current curvature value, and added to the decision-making process of path selection, so that the path selection presents random characteristics:

[0050] ;

[0051] in, is the disturbance factor at time t, is the disturbance adjustment coefficient, For Link The curvature value of , and They are the bandwidth, delay and load of the link respectively;

[0052] S35, according to the introduction of the disturbance factor, the path selection result is adjusted to make the path selection process more random, the path weight is updated through random disturbance, a new path sequence is generated, and the paths in the path sequence are preferentially selected with the links having higher weights than other links after the disturbance adjustment, so as to introduce pseudo-randomness in the path selection process;

[0053] S36. By dynamically updating the path selection strategy and adjusting the path topology in the path sequence in real time, the path selection process always remains random and unpredictable. The path selection strategy is updated based on the real-time monitoring of network load and path topology changes, and the weight factors and disturbance factors in the path selection algorithm are adjusted in a timely manner to optimize the distribution and transmission efficiency of the path.

[0054] Optionally, the S4 specifically includes:

[0055] S41. During the network path transmission process, real-time monitoring of path topology changes is performed to collect information about each link. Status data, including link bandwidth ,Delay ,load , current curvature value , as well as the transmission efficiency and stability of the link. Based on the real-time monitoring data, a path topology change trend model is generated to predict the link status change between each pair of nodes i and j;

[0056] S42. Based on the real-time monitoring data and the path topology change trend model, combined with the defined curvature diffusion model, the change of the link curvature value is calculated, and the connection mode between the path nodes is dynamically adjusted. The adjustment process is based on the diffusion characteristics of the link curvature. The goal is to achieve nonlinear adjustment of the path topology structure by controlling the evolution of the link curvature, so that the topology always adapts to the current network status and needs;

[0057] S43. For each pair of nodes i and j, calculate the path concealment index according to the link data obtained in real time , the concealment index reflects the security, concealment and unpredictability of the path:

[0058] ;

[0059] in, is the path concealment index at time t, and is the concealment weighting factor, For Link bandwidth, is the link delay, is the load of the link, For Link The curvature value at time t;

[0060] S44. Based on the concealment index ,Optimize the path selection, the path selection gives priority to the paths with higher concealment than others.,When selecting a path, it relies on the concealment index value calculated in real time, so that the path selection can avoid potential security threats;

[0061] S45. In the process of path selection, combine the concealment index The real-time status of the path links is used to adjust the connection order of the path nodes, giving priority to links with higher concealment than others for data transmission, and by adjusting the connection order of the path nodes, the path topology can maximize the concealment at each path selection;

[0062] S46, in the path selection strategy, combining the real-time concealment index and the link status information, dynamically adjusting the path selection, adjusting the weight coefficient and the concealment index in the path selection strategy, so that the security and concealment of the path topology are guaranteed in the dynamic network environment;

[0063] S47, based on real-time monitoring of network load, link status and path topology changes, combined with concealment indicators , dynamically adjust the connection relationship of path nodes and the decision logic of the path selection algorithm, and the optimization strategy of path selection is adjusted as the network load and topology changes, so that the path topology always maintains its concealment.

[0064] Optionally, the S5 specifically includes:

[0065] S51. In the process of cross-domain data transmission, the data transmission path is optimized by combining the path concealment algorithm and the cross-domain characteristics of the network to resist deep traffic analysis attacks and make the path difficult to identify in a multi-domain environment. The optimization strategy includes adjusting the structure of the path topology to make it present a high degree of uncertainty and randomness in different network domains;

[0066] S52, calculate each link according to the cross-domain network status monitored in real time Hidden indicators ,The concealment index considers the bandwidth, delay, load, path topology curvature value of the cross-domain link, as well as the link stability and transmission risk in the cross-domain transmission environment;

[0067] S53, based on cross-domain concealment indicators ,Adjust the data transmission path, give priority to the path with higher concealment than others for data transmission, and the path selection is based on the concealment value calculated in real time, avoiding the path that is easy to be identified and attacked;

[0068] S54. In the process of path selection, by introducing cross-domain optimization strategies and combining inter-domain transmission characteristics, the connection relationship of the path is adjusted in real time, making the data transmission path difficult to predict and having the ability to resist traffic analysis;

[0069] S55, dynamically adjust the optimization of the cross-domain data transmission path, combine the real-time status information of the cross-domain link, the concealment index and the network load, adjust the weight coefficient and the concealment index in the path selection strategy, so that the path topology always maintains concealment and anti-interference capabilities;

[0070] S56. Dynamically adjust the path selection decision for cross-domain data transmission based on real-time cross-domain link load and transmission efficiency, optimize the path topology, and resist deep traffic analysis attacks and other potential network security threats.

[0071] Optionally, the S6 specifically includes:

[0072] S61, in the process of network path selection, based on the real-time monitoring of network status information and changes in path topology, obtain the information of each link in the network Key performance indicators, including link bandwidth ,Delay ,load , curvature value As well as the stability and transmission efficiency of the path, the path topology model is updated in real time, and the path selection strategy is dynamically adjusted according to the changing trend of the network status;

[0073] S62, according to the real-time network status and topology changes, combined with the pseudo-random curvature diffusion model, adjust the curvature diffusion parameters in the path topology structure to adapt to the dynamic changes of the network, by adjusting the curvature diffusion coefficient and disturbance factor , so that the diffusion process of path curvature conforms to the state of the current network environment:

[0074] ;

[0075] in, is the link at time t The curvature value of For Link The curvature diffusion coefficient, is the disturbance factor at time t, For Link Real-time disturbance factor of

[0076] S63, after the curvature diffusion parameter is adjusted, the path curvature between each pair of nodes i and j is calculated in real time, and the transmission efficiency and feasibility of the path are updated according to the new curvature value. In the path selection process, according to the dynamically calculated path curvature value, the path with a lower curvature change than other paths is preferentially selected. The path selection strategy depends on the curvature value calculated in real time to ensure the stability and unpredictability of path selection;

[0077] S64, based on the curvature diffusion model and the dynamically adjusted path curvature, the path topology is updated in real time, and the path selection process is made uncertain and unpredictable by adjusting the connection order of the path nodes and the path topology;

[0078] S65. Combine the dynamic update of the path topology and the curvature value calculated in real time to adjust the weight factor in the path selection strategy and , so that the path selection is more in line with the real-time changes of the network status. The weight factor is adjusted according to the dynamically calculated curvature value and network load, and the path with lower curvature change and load than others is preferentially selected for data transmission;

[0079] S66. By continuously monitoring the network status, path topology and curvature diffusion parameters, the path selection decision is updated in real time. During each path selection process, the path topology structure is dynamically adjusted according to the changes in path curvature and network load. The path selection optimization strategy is adaptively adjusted according to changes in network topology and link status, optimizing the security of the path and preventing potential network attacks.

[0080] The beneficial effects of the present invention are:

[0081] (1) The present invention uses an SRv6 path obfuscation method based on pseudo-random curvature diffusion, combined with real-time network status and dynamic changes in path topology, to achieve optimal selection of data transmission paths, making the paths more concealed and resistant to traffic analysis. This method effectively improves the security of cross-domain data transmission by dynamically adjusting the connection mode and path topology of path nodes, and can resist deep traffic analysis attacks, thereby ensuring privacy and security during data transmission.

[0082] (2) The present invention introduces a pseudo-random curvature diffusion algorithm to make the path selection process highly random and unpredictable, thus avoiding the risk of path exposure and being able to adjust the path selection strategy in real time according to the network status. This method can dynamically adapt to changes in network load, link status, and topology, effectively optimize path transmission efficiency, improve network security defense capabilities, and reduce potential security threats.

[0083] (3) The present invention optimizes the selection of data transmission paths by combining the path concealment algorithm and cross-domain network characteristics, ensuring that the paths are difficult to identify in a multi-domain environment. This method not only has excellent performance in resisting deep traffic analysis attacks, but also can dynamically adjust the path selection strategy to adapt to the ever-changing network environment, thereby improving the flexibility and effectiveness of the network in responding to emerging security threats. At the same time, the concealment optimization in the path selection process provides a strong security guarantee for cross-domain data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0085] Figure 1 This is a flow chart of an SRV6 path obfuscation method based on pseudo-random curvature diffusion proposed by the present invention. DETAILED DESCRIPTION

[0086] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0087] refer to Figure 1 , a SRV6 path obfuscation method based on pseudo-random curvature diffusion, comprising the following steps:

[0088] S1. Initialize the pseudo-random curvature diffusion model, analyze the topological structure in the SRV6 network, and construct an initial path sequence. The path sequence is generated based on the physical properties and connection relationships of nodes and links in the network.

[0089] S2, dynamically adjust the path using a pseudo-random curvature diffusion algorithm, generate a nonlinear path topology by controlling the change of path curvature, and make the path present pseudo-random characteristics when executed in the network;

[0090] S3, dynamically calculate and select the data transmission path according to the change of path topology, and adjust the path selection strategy between nodes based on the calculation results of the pseudo-random diffusion model to make the path selection process random;

[0091] S4. During the network path transmission process, the path topology changes are monitored in real time, the path node connection mode is adjusted according to the curvature diffusion calculation results, the path concealment is optimized, and the path topology structure is updated according to the dynamic changes of the path;

[0092] S5. In the process of cross-domain data transmission, the path concealment algorithm is combined to optimize the data transmission path, resist deep traffic analysis attacks, make the path difficult to identify, and optimize the risk of path exposure;

[0093] S6. According to the real-time changes of network status and path topology, the path selection strategy and pseudo-random curvature diffusion algorithm parameters are adjusted, and the unpredictability of the path during execution is achieved by dynamically updating the path topology structure and path curvature.

[0094] In this implementation manner, the S1 specifically includes:

[0095] S11, obtaining SRV6 network topology data, extracting the identifier of each node in the network, the bandwidth, delay, load, link type and physical location attributes of the link between nodes as the basic data for initializing the network topology;

[0096] S12. Construct a network graph model, where nodes in the graph represent devices in the network, and links represent physical connections between devices. The network graph is constructed by obtaining node and link data, and represents the physical connection relationship between nodes and link properties.

[0097] S13. Assign a pseudo-random curvature value to each link , which is generated by a pseudo-random number generation algorithm based on the bandwidth of the link ,Delay and load The calculation results are:

[0098] ;

[0099] in, Indicates link The pseudo-random curvature value of , function g is a pseudo-random number generation algorithm;

[0100] S14, based on the pseudo-random curvature value of each link , calculate the path feasibility value between each pair of nodes in the network , the path feasibility value takes into account link bandwidth, delay, load and pseudo-random curvature factors:

[0101] ;

[0102] in, represents the path feasibility value between node i and node j, and function h is the path feasibility calculation function;

[0103] S15. Based on the path feasibility value The path selection process starts from the source node, gives priority to the link with higher path feasibility value, and gradually extends it until it reaches the target node to generate a preliminary path sequence.

[0104] S16. According to the topological relationship between nodes in the network, a path topology is gradually constructed. The construction of the path topology takes into account the physical connection and topological structure of the link, so that each path meets the actual physical connection requirements in the network and forms a complete data transmission path;

[0105] S17, perform pseudo-random curvature diffusion adjustment on the initially generated path sequence, based on the curvature values ​​of the links in the path sequence ,The order of path selection and link selection are adjusted through the path optimization algorithm to generate an optimized path sequence.

[0106] In this implementation, S2 specifically includes:

[0107] S21. Initialize the link curvature value of each path according to the generated optimized path sequence , and set the initial range and step size parameters of link curvature adjustment. By dynamically adjusting the link curvature value, the nonlinear characteristics of the path topology are realized. The curvature adjustment process follows the preset diffusion law;

[0108] S22, in each time step t, the link is calculated by pseudo-random curvature diffusion algorithm Adjust the curvature value :

[0109] ;

[0110] in, represents the link at time t Curvature value, For Link The change in curvature value at time t, is the adjustment factor for controlling the change amplitude of link curvature;

[0111] S23, introduce pseudo-random perturbation factor Link curvature value Perform random perturbations, with the perturbation factor based on the bandwidth of the link ,Delay ,load and the current curvature value Make a weighted adjustment:

[0112] ;

[0113] in, is the disturbance factor at time t, is the disturbance amplitude adjustment coefficient, For Link The curvature value at the previous moment;

[0114] S24, the disturbance factor In the process of adding link curvature value adjustment, the curvature value of each link Update the path topology so that the curvature of the path topology at each moment presents nonlinear change characteristics, and dynamically generate a path topology with pseudo-random characteristics;

[0115] S25. Update curvature value based on each link , recalculate the path feasibility value between any two nodes i and j in the path topology , the path feasibility value takes into account the impact of link bandwidth, delay, load and curvature adjustment:

[0116] ;

[0117] in, is the path feasibility value at time t, The current link The curvature value of

[0118] S26, based on the updated path feasibility value ,dynamically select links with feasibility values, prioritize updating the path sequence, so that the path selection presents random characteristics. The path selection process is based on the dynamic sorting of feasibility values ​​to generate a path sequence that conforms to nonlinear random characteristics;

[0119] S27. According to the real-time monitoring results of path topology and curvature value changes, dynamically adjust the link curvature adjustment parameters and path selection strategy, and make the path topology unpredictable by changing the control factor of the curvature diffusion model. At the same time, optimize the path update strategy in time to prevent the path information from being leaked in advance.

[0120] In this implementation, S3 specifically includes:

[0121] S31. According to the dynamic changes of the path topology, the feasible path set between each pair of nodes i and j is updated in real time. The path set contains all feasible paths that meet the current network conditions. In the path selection process, links that meet the network constraints are given priority, while ensuring that the nonlinear characteristics of the path topology are preserved;

[0122] S32, based on the calculation results of the pseudo-random diffusion model, combined with the current state parameters of the network, dynamically calculate the path weight , the weight reflects the priority of path selection:

[0123] ;

[0124] in, is the path weight at time t, For Link bandwidth, is the link delay, is the load of the link, For Link The curvature value at time t, and is the weight adjustment factor;

[0125] S33, according to the dynamically calculated path weight, adopt the priority selection principle of maximizing the path weight, select the optimal path from the feasible path set, in the path selection process, give priority to the link with a higher weight value, and build a transmission path from the source node to the target node;

[0126] S34. In the process of path selection, the disturbance factor introduced by the pseudo-random diffusion model , randomize the path selection, and the disturbance factor is weighted according to the link bandwidth, delay, load and current curvature value, and added to the decision-making process of path selection, so that the path selection presents random characteristics:

[0127] ;

[0128] in, is the disturbance factor at time t, is the disturbance adjustment coefficient, For Link The curvature value of , and They are the bandwidth, delay and load of the link respectively;

[0129] S35, according to the introduction of the disturbance factor, the path selection result is adjusted to make the path selection process more random, the path weight is updated through random disturbance, a new path sequence is generated, and the paths in the path sequence are preferentially selected with the links having higher weights than other links after the disturbance adjustment, so as to introduce pseudo-randomness in the path selection process;

[0130] S36. By dynamically updating the path selection strategy and adjusting the path topology in the path sequence in real time, the path selection process always remains random and unpredictable. The path selection strategy is updated based on the real-time monitoring of network load and path topology changes, and the weight factors and disturbance factors in the path selection algorithm are adjusted in a timely manner to optimize the distribution and transmission efficiency of the path.

[0131] In this implementation, S4 specifically includes:

[0132] S41. During the network path transmission process, real-time monitoring of path topology changes is performed to collect information about each link. Status data, including link bandwidth ,Delay ,load , current curvature value , as well as the transmission efficiency and stability of the link. Based on the real-time monitoring data, a path topology change trend model is generated to predict the link status change between each pair of nodes i and j;

[0133] S42. Based on the real-time monitoring data and the path topology change trend model, combined with the defined curvature diffusion model, the change of the link curvature value is calculated, and the connection mode between the path nodes is dynamically adjusted. The adjustment process is based on the diffusion characteristics of the link curvature. The goal is to achieve nonlinear adjustment of the path topology structure by controlling the evolution of the link curvature, so that the topology always adapts to the current network status and needs;

[0134] S43. For each pair of nodes i and j, calculate the path concealment index according to the link data obtained in real time , the concealment index reflects the security, concealment and unpredictability of the path:

[0135] ;

[0136] in, is the path concealment index at time t, and is the concealment weighting factor, For Link bandwidth, is the link delay, is the load of the link, For Link The curvature value at time t;

[0137] S44. Based on the concealment index ,Optimize the path selection, the path selection gives priority to the paths with higher concealment than others.,When selecting a path, it relies on the concealment index value calculated in real time, so that the path selection can avoid potential security threats;

[0138] S45. In the process of path selection, combine the concealment index The real-time status of the path links is used to adjust the connection order of the path nodes, giving priority to links with higher concealment than others for data transmission, and by adjusting the connection order of the path nodes, the path topology can maximize the concealment at each path selection;

[0139] S46, in the path selection strategy, combining the real-time concealment index and the link status information, dynamically adjusting the path selection, adjusting the weight coefficient and the concealment index in the path selection strategy, so that the security and concealment of the path topology are guaranteed in the dynamic network environment;

[0140] S47, based on real-time monitoring of network load, link status and path topology changes, combined with concealment indicators , dynamically adjust the connection relationship of path nodes and the decision logic of the path selection algorithm, and the optimization strategy of path selection is adjusted as the network load and topology changes, so that the path topology always maintains its concealment.

[0141] In this implementation manner, S5 specifically includes:

[0142] S51. In the process of cross-domain data transmission, the data transmission path is optimized by combining the path concealment algorithm and the cross-domain characteristics of the network to resist deep traffic analysis attacks and make the path difficult to identify in a multi-domain environment. The optimization strategy includes adjusting the structure of the path topology to make it present a high degree of uncertainty and randomness in different network domains;

[0143] S52, calculate each link according to the cross-domain network status monitored in real time Hidden indicators ,The concealment index considers the bandwidth, delay, load, path topology curvature value of the cross-domain link, as well as the link stability and transmission risk in the cross-domain transmission environment;

[0144] S53, based on cross-domain concealment indicators ,Adjust the data transmission path, give priority to the path with higher concealment than others for data transmission, and the path selection is based on the concealment value calculated in real time, avoiding the path that is easy to be identified and attacked;

[0145] S54. In the process of path selection, by introducing cross-domain optimization strategies and combining inter-domain transmission characteristics, the connection relationship of the path is adjusted in real time, making the data transmission path difficult to predict and having the ability to resist traffic analysis;

[0146] S55, dynamically adjust the optimization of the cross-domain data transmission path, combine the real-time status information of the cross-domain link, the concealment index and the network load, adjust the weight coefficient and the concealment index in the path selection strategy, so that the path topology always maintains concealment and anti-interference capabilities;

[0147] S56. Dynamically adjust the path selection decision for cross-domain data transmission based on real-time cross-domain link load and transmission efficiency, optimize the path topology, and resist deep traffic analysis attacks and other potential network security threats.

[0148] In this implementation manner, S6 specifically includes:

[0149] S61, in the process of network path selection, based on the real-time monitoring of network status information and changes in path topology, obtain the information of each link in the network Key performance indicators, including link bandwidth ,Delay ,load , curvature value As well as the stability and transmission efficiency of the path, the path topology model is updated in real time, and the path selection strategy is dynamically adjusted according to the changing trend of the network status;

[0150] S62, according to the real-time network status and topology changes, combined with the pseudo-random curvature diffusion model, adjust the curvature diffusion parameters in the path topology structure to adapt to the dynamic changes of the network, by adjusting the curvature diffusion coefficient and disturbance factor , so that the diffusion process of path curvature conforms to the state of the current network environment:

[0151] ;

[0152] in, is the link at time t The curvature value of For Link The curvature diffusion coefficient, is the disturbance factor at time t, For Link Real-time disturbance factor of

[0153] S63, after the curvature diffusion parameter is adjusted, the path curvature between each pair of nodes i and j is calculated in real time, and the transmission efficiency and feasibility of the path are updated according to the new curvature value. In the path selection process, according to the dynamically calculated path curvature value, the path with a lower curvature change than other paths is preferentially selected. The path selection strategy depends on the curvature value calculated in real time to ensure the stability and unpredictability of path selection;

[0154] S64, based on the curvature diffusion model and the dynamically adjusted path curvature, the path topology is updated in real time, and the path selection process is made uncertain and unpredictable by adjusting the connection order of the path nodes and the path topology;

[0155] S65. Combine the dynamic update of the path topology and the curvature value calculated in real time to adjust the weight factor in the path selection strategy and , so that the path selection is more in line with the real-time changes of the network status. The weight factor is adjusted according to the dynamically calculated curvature value and network load, and the path with lower curvature change and load than others is preferentially selected for data transmission;

[0156] S66. By continuously monitoring the network status, path topology and curvature diffusion parameters, the path selection decision is updated in real time. During each path selection process, the path topology structure is dynamically adjusted according to the changes in path curvature and network load. The path selection optimization strategy is adaptively adjusted according to changes in network topology and link status, optimizing the security of the path and preventing potential network attacks.

[0157] Embodiment 1:

[0158] In order to verify the feasibility of the present invention in implementation, the present invention is applied to a large cross-domain network environment. The environment includes multiple different types of network domains, including enterprise intranets, cloud platforms, and external public networks. The data flow between these network domains is frequent, and there is a large demand for sensitive data transmission. Therefore, network security, especially the defense against deep traffic analysis attacks and the protection of the concealment of data transmission paths, has become an important challenge for network design and security assurance. Traditional network path selection methods often lack dynamic adaptability, and the concealment of cross-domain paths is not sufficiently considered, making the data transmission path easy to be identified and attacked.

[0159] In order to solve this problem, the present invention proposes a new cross-domain data transmission path obfuscation method by combining real-time monitoring, pseudo-random curvature diffusion algorithm and path concealment optimization technology. The method aims to improve the unpredictability and concealment of the path by dynamically adjusting the path topology and path curvature, thereby effectively resisting deep traffic analysis attacks and other network security threats.

[0160] During implementation, the path optimization algorithm of the present invention is deployed in a cross-domain data transmission network, covering multiple cross-domain nodes. These nodes include the data center of the enterprise intranet, the external cloud service platform, and the communication links between multiple remote branches. The system calculates the concealment index of each link by monitoring the bandwidth, delay, load, and path curvature of these links in real time, and dynamically adjusts the path topology and curvature value of each link in combination with the pseudo-random curvature diffusion algorithm.

[0161] Specifically, the system first monitors the link status of the cross-domain path in real time and calculates the concealment index of each link. The concealment index takes into account the bandwidth, delay, load and current curvature value of the link, and introduces a pseudo-random perturbation factor to ensure the unpredictability of the link path. Then, based on the real-time monitoring data and the path concealment index, the system automatically optimizes the path selection and gives priority to paths with high concealment for data transmission. In addition, the system dynamically adjusts the path topology according to changes in the network status, further improving the concealment of the path and enhancing the defense capability against deep traffic analysis attacks.

[0162] To evaluate the performance of this method, we conducted a test in a cross-domain enterprise network environment. The test environment includes a main data center and two remote branches, and the network links span multiple different network domains. During the test, we compared the effectiveness of the method of the present invention with the traditional path selection algorithm in resisting deep traffic analysis attacks and improving path concealment. The test data is shown in Table 1:

[0163] Table 1 Comparison of cross-domain data transmission path optimization effects

[0164] ;

[0165] It can be seen from the data in Table 1 that the path optimization method of the present invention is significantly superior to the traditional path selection method in multiple indicators. In terms of transmission delay, the present invention shortens the average path transmission delay from 200 milliseconds of the traditional method to 150 milliseconds by optimizing the path topology and introducing the pseudo-random curvature diffusion algorithm, thereby improving the network transmission efficiency. The path concealment score is improved from 68.5 of the traditional method to 92.3, indicating that the present invention can significantly enhance the concealment of the path and reduce the risk of being attacked. In terms of deep traffic analysis attack defense, the present invention successfully reduces the attack success rate from 45% to 12%, effectively reducing the possibility of data theft, and the data packet loss rate is also significantly reduced from 5.1% to 1.8%, ensuring the reliability of data transmission. The path stability is improved from 70.2% of the traditional method to 96.4%, indicating that the method of the present invention makes the network path more stable and reduces the path interruption caused by changes in network status.

[0166] During the specific test process, in a data transmission task between a data center and a remote branch, the traditional path selection method lacked concealment, making the path vulnerable to attacks. The attacker identified the data transmission path through deep traffic analysis technology and interfered with it. After adopting the method of the present invention, the system dynamically adjusted the path topology and curvature value to make the path highly random and unpredictable, greatly improving the security of data transmission and avoiding the successful implementation of deep traffic analysis attacks.

[0167] In summary, the SRv6 path obfuscation method based on pseudo-random curvature diffusion of the present invention effectively improves the security and concealment of cross-domain data transmission by combining real-time network monitoring, path concealment optimization and dynamic path topology adjustment technology, and successfully solves the shortcomings of traditional path selection methods in defending against deep traffic analysis attacks and improving path unpredictability. In practical applications, it has significant performance advantages and broad application prospects.

[0168] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A SRV6 path obfuscation method based on pseudo-random curvature diffusion, characterized in that: The steps include: S1. Initialize the pseudo-random curvature diffusion model, analyze the topological structure in the SRV6 network, and construct an initial path sequence. The path sequence is generated based on the physical properties and connection relationships of nodes and links in the network. S2, dynamically adjust the path using a pseudo-random curvature diffusion algorithm, generate a nonlinear path topology by controlling the change of path curvature, and make the path present pseudo-random characteristics when executed in the network; S3, dynamically calculate and select the data transmission path according to the change of path topology, and adjust the path selection strategy between nodes based on the calculation results of the pseudo-random diffusion model to make the path selection process random; S4. During the network path transmission process, the path topology changes are monitored in real time, the path node connection mode is adjusted according to the curvature diffusion calculation results, the path concealment is optimized, and the path topology structure is updated according to the dynamic changes of the path; S5. In the process of cross-domain data transmission, the path concealment algorithm is combined to optimize the data transmission path, resist deep traffic analysis attacks, make the path difficult to identify, and optimize the risk of path exposure; S6. According to the real-time changes of network status and path topology, the path selection strategy and pseudo-random curvature diffusion algorithm parameters are adjusted, and the unpredictability of the path during execution is achieved by dynamically updating the path topology structure and path curvature.

2. According to claim 1, a SRV6 path obfuscation method based on pseudo-random curvature diffusion is characterized in that: The S1 specifically includes: S11, obtaining SRV6 network topology data, extracting the identifier of each node in the network, the bandwidth, delay, load, link type and physical location attributes of the link between nodes as the basic data for initializing the network topology; S12. Construct a network graph model, where nodes in the graph represent devices in the network, and links represent physical connections between devices. The network graph is constructed by obtaining node and link data, and represents the physical connection relationship between nodes and link properties. S13. Assign a pseudo-random curvature value to each link , which is generated by a pseudo-random number generation algorithm based on the bandwidth of the link ,Delay and load The calculation results are: ; in, Indicates link The pseudo-random curvature value of , function g is a pseudo-random number generation algorithm; S14, based on the pseudo-random curvature value of each link , calculate the path feasibility value between each pair of nodes in the network , the path feasibility value takes into account link bandwidth, delay, load and pseudo-random curvature factors: ; in, represents the path feasibility value between node i and node j, and function h is the path feasibility calculation function; S15. Based on the path feasibility value The path selection process starts from the source node, gives priority to the link with higher path feasibility value, and gradually extends it until it reaches the target node to generate a preliminary path sequence. S16. According to the topological relationship between nodes in the network, the path topology is gradually constructed. The construction of the path topology takes into account the physical connection and topological structure of the link, so that each path meets the actual physical connection requirements in the network and forms a complete data transmission path; S17, perform pseudo-random curvature diffusion adjustment on the initially generated path sequence, based on the curvature values ​​of the links in the path sequence ,The order of path selection and link selection are adjusted through the path optimization algorithm to generate an optimized path sequence.

3. The SRV6 path obfuscation method based on pseudo-random curvature diffusion according to claim 1, characterized in that: The S2 specifically includes: S21. Initialize the link curvature value of each path according to the generated optimized path sequence , and set the initial range and step size parameters of link curvature adjustment. By dynamically adjusting the link curvature value, the nonlinear characteristics of the path topology are realized. The curvature adjustment process follows the preset diffusion law; S22, in each time step t, the link is calculated by pseudo-random curvature diffusion algorithm Adjust the curvature value : ; in, represents the link at time t Curvature value, For Link The change in curvature value at time t, is the adjustment factor for controlling the change of link curvature; S23, introduce pseudo-random perturbation factor Link curvature value Perform random perturbations, with the perturbation factor based on the bandwidth of the link ,Delay ,load and the current curvature value Make a weighted adjustment: ; in, is the disturbance factor at time t, is the disturbance amplitude adjustment coefficient, For Link The curvature value at the previous moment; S24, the disturbance factor In the process of adding link curvature value adjustment, the curvature value of each link Update the path topology so that the curvature of the path topology at each moment presents nonlinear change characteristics, and dynamically generate a path topology with pseudo-random characteristics; S25. Update curvature value based on each link , recalculate the path feasibility value between any two nodes i and j in the path topology , the path feasibility value takes into account the impact of link bandwidth, delay, load and curvature adjustment: ; in, is the path feasibility value at time t, The current link The curvature value of S26, based on the updated path feasibility value ,dynamically select links with feasibility values, prioritize updating the path sequence, so that the path selection presents random characteristics. The path selection process is based on the dynamic sorting of feasibility values ​​to generate a path sequence that conforms to nonlinear random characteristics; S27. According to the real-time monitoring results of path topology and curvature value changes, dynamically adjust the link curvature adjustment parameters and path selection strategy, and make the path topology unpredictable by changing the control factor of the curvature diffusion model. At the same time, optimize the path update strategy in time to prevent the path information from being leaked in advance.

4. The SRV6 path obfuscation method based on pseudo-random curvature diffusion according to claim 1, characterized in that: The S3 specifically includes: S31. According to the dynamic changes of the path topology, the feasible path set between each pair of nodes i and j is updated in real time. The path set contains all feasible paths that meet the current network conditions. In the path selection process, links that meet the network constraints are given priority, while ensuring that the nonlinear characteristics of the path topology are preserved; S32, based on the calculation results of the pseudo-random diffusion model, combined with the current state parameters of the network, dynamically calculate the path weight , the weight reflects the priority of path selection: ; in, is the path weight at time t, For Link bandwidth, is the link delay, is the load of the link, For Link The curvature value at time t is, and is the weight adjustment factor; S33, according to the dynamically calculated path weight, adopt the priority selection principle of maximizing the path weight, select the optimal path from the feasible path set, in the path selection process, give priority to the link with a higher weight value, and build a transmission path from the source node to the target node; S34. In the process of path selection, the disturbance factor introduced by the pseudo-random diffusion model , randomize the path selection, and the disturbance factor is weighted according to the link bandwidth, delay, load and current curvature value, and added to the decision-making process of path selection, so that the path selection presents random characteristics: ; in, is the disturbance factor at time t, is the disturbance adjustment coefficient, For Link The curvature value of , and They are the bandwidth, delay and load of the link respectively; S35, according to the introduction of the disturbance factor, the path selection result is adjusted to make the path selection process more random, the path weight is updated through random disturbance, a new path sequence is generated, and the paths in the path sequence are preferentially selected with the links having higher weights than other links after the disturbance adjustment, so as to introduce pseudo-randomness in the path selection process; S36. By dynamically updating the path selection strategy and adjusting the path topology in the path sequence in real time, the path selection process always remains random and unpredictable. The path selection strategy is updated based on the real-time monitoring of network load and path topology changes, and the weight factors and disturbance factors in the path selection algorithm are adjusted in a timely manner to optimize the distribution and transmission efficiency of the path.

5. The SRV6 path obfuscation method based on pseudo-random curvature diffusion according to claim 1, characterized in that: The S4 specifically includes: S41. During the network path transmission process, real-time monitoring of path topology changes is performed to collect information about each link. Status data, including link bandwidth ,Delay ,load , current curvature value , as well as the transmission efficiency and stability of the link, based on the real-time monitoring data, a path topology change trend model is generated to predict the link status change between each pair of nodes i and j; S42. Based on the real-time monitoring data and the path topology change trend model, combined with the defined curvature diffusion model, the change of the link curvature value is calculated, and the connection mode between the path nodes is dynamically adjusted. The adjustment process is based on the diffusion characteristics of the link curvature. The goal is to achieve nonlinear adjustment of the path topology structure by controlling the evolution of the link curvature, so that the topology always adapts to the current network status and needs; S43. For each pair of nodes i and j, calculate the path concealment index according to the link data obtained in real time , the concealment index reflects the security, concealment and unpredictability of the path: ; in, is the path concealment index at time t, and is the concealment weighting factor, For Link bandwidth, is the link delay, is the load of the link, For Link The curvature value at time t; S44. Based on the concealment index ,Optimize the path selection, the path selection gives priority to the paths with higher concealment than others.,When selecting a path, it relies on the concealment index value calculated in real time, so that the path selection can avoid potential security threats; S45. In the process of path selection, combine the concealment index The real-time status of the path links is used to adjust the connection order of the path nodes, giving priority to links with higher concealment than others for data transmission, and by adjusting the connection order of the path nodes, the path topology can maximize the concealment at each path selection; S46, in the path selection strategy, combining the real-time concealment index and the link status information, dynamically adjusting the path selection, adjusting the weight coefficient and the concealment index in the path selection strategy, so that the security and concealment of the path topology are guaranteed in the dynamic network environment; S47, based on real-time monitoring of network load, link status and path topology changes, combined with concealment indicators , dynamically adjust the connection relationship of path nodes and the decision logic of the path selection algorithm, and the optimization strategy of path selection is adjusted as the network load and topology changes, so that the path topology always maintains its concealment.

6. The SRV6 path obfuscation method based on pseudo-random curvature diffusion according to claim 1, characterized in that: The S5 specifically includes: S51. In the process of cross-domain data transmission, the data transmission path is optimized by combining the path concealment algorithm and the cross-domain characteristics of the network to resist deep traffic analysis attacks and make the path difficult to identify in a multi-domain environment. The optimization strategy includes adjusting the structure of the path topology to make it present a high degree of uncertainty and randomness in different network domains; S52, calculate each link according to the cross-domain network status monitored in real time Hidden indicators ,The concealment index considers the bandwidth, delay, load, path topology curvature value of the cross-domain link, as well as the link stability and ,transmission risk in the cross-domain transmission environment; S53, based on cross-domain concealment indicators ,Adjust the data transmission path, give priority to the path with higher concealment than others for data transmission, and the path selection is based on the concealment value calculated in real time to avoid the path that is easy to be identified and attacked; S54. In the process of path selection, by introducing cross-domain optimization strategies and combining inter-domain transmission characteristics, the connection relationship of the path is adjusted in real time, making the data transmission path difficult to predict and having the ability to resist traffic analysis; S55, dynamically adjust the optimization of the cross-domain data transmission path, combine the real-time status information of the cross-domain link, the concealment index and the network load, adjust the weight coefficient and the concealment index in the path selection strategy, so that the path topology always maintains concealment and anti-interference capabilities; S56. Dynamically adjust the path selection decision for cross-domain data transmission based on real-time cross-domain link load and transmission efficiency, optimize the path topology, and resist deep traffic analysis attacks and other potential network security threats.

7. The SRV6 path obfuscation method based on pseudo-random curvature diffusion according to claim 1, characterized in that: The S6 specifically includes: S61, in the process of network path selection, based on the real-time monitoring of network status information and changes in path topology, obtain the information of each link in the network Key performance indicators, including link bandwidth ,Delay ,load , curvature value As well as the stability and transmission efficiency of the path, the path topology model is updated in real time, and the path selection strategy is dynamically adjusted according to the changing trend of the network status; S62, according to the real-time network status and topology changes, combined with the pseudo-random curvature diffusion model, adjust the curvature diffusion parameters in the path topology structure to adapt to the dynamic changes of the network, by adjusting the curvature diffusion coefficient and disturbance factor , so that the diffusion process of path curvature conforms to the state of the current network environment: ; in, is the link at time t The curvature value of For Link The curvature diffusion coefficient, is the disturbance factor at time t, For Link Real-time disturbance factor of S63, after the curvature diffusion parameter is adjusted, the path curvature between each pair of nodes i and j is calculated in real time, and the transmission efficiency and feasibility of the path are updated according to the new curvature value. In the path selection process, according to the dynamically calculated path curvature value, the path with a lower curvature change than other paths is preferentially selected. The path selection strategy depends on the curvature value calculated in real time to ensure the stability and unpredictability of path selection; S64, based on the curvature diffusion model and the dynamically adjusted path curvature, the path topology is updated in real time, and the path selection process is made uncertain and unpredictable by adjusting the connection order of the path nodes and the path topology; S65. Combine the dynamic update of the path topology and the curvature value calculated in real time to adjust the weight factor in the path selection strategy and , so that the path selection is more in line with the real-time changes of the network status. The weight factor is adjusted according to the dynamically calculated curvature value and network load, and the path with lower curvature change and load than others is preferentially selected for data transmission; S66. By continuously monitoring the network status, path topology and curvature diffusion parameters, the path selection decision is updated in real time. During each path selection process, the path topology structure is dynamically adjusted according to the changes in path curvature and network load. The path selection optimization strategy is adaptively adjusted according to changes in network topology and link status, optimizing the security of the path and preventing potential network attacks.

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