A routing method for ensuring security of high dynamic flight ad hoc network
By combining trust assessment and hash chain verification, the problem of black hole attack identification and overhead in FANET is solved, thereby improving the communication performance and robustness of FANET.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SOUTHEAST UNIV
- Filing Date
- 2026-03-13
- Publication Date
- 2026-06-05
AI Technical Summary
In FANET, existing technologies struggle to effectively identify and isolate black hole attacks in highly dynamic environments, while also avoiding excessive overhead from security mechanisms.
A secure AODV method based on trust assessment and behavior verification is adopted. The trust level of nodes is assessed by listening to packet forwarding behavior, and the authenticity of routing information is ensured by combining hash chain verification. Differentiated security processing strategies are adopted.
It enables efficient identification of black hole attacks in complex threat environments, reduces computational and communication overhead, and improves the communication performance and robustness of FANET.
Smart Images
Figure CN122160773A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of wireless communication network technology, specifically relating to a routing method that ensures the security of a highly dynamic flying ad-hoc network (FANET), and more particularly to a secure on-demand distance vector routing method based on a trust value mechanism and hash chain verification technology. Background Technology
[0002] FANET consists of multiple high-speed moving unmanned aerial vehicle (UAV) nodes. Due to its self-organizing and open nature, it is vulnerable to various network attacks, such as the BlackHole Attack. For the classic Ad hoc On-Demand Distance Vector Routing (AODV) protocol, the BlackHole Attack exploits its route freshness mechanism to forge routing information, setting the destination sequence number in the route response as high as possible and the hop count as low as possible. In highly dynamic environments, the high-speed movement of UAV nodes and frequent topology changes make it difficult to distinguish between link interruptions and malicious packet loss. Attackers can use this to conceal their actions, increasing the difficulty of detection.
[0003] To enhance the resilience of UAV ad hoc network routing protocols against black hole attacks, existing research primarily focuses on the following technical approaches: first, behavior monitoring mechanisms based on trust assessment, which model node reputation using historical interaction data; second, information integrity protection based on cryptographic verification, such as using digital signatures or hash chains to ensure the authenticity of routing information; and third, fault-tolerant routing based on multipath probing, which reduces the risk of single-point failure through redundant paths. However, in the high-speed, resource-constrained FANET environment, these methods often face challenges such as high overhead, slow response, or complex deployment: trust models may fail due to rapid topology changes; cryptographic operations introduce significant computational and communication overhead; and multipath probing further exacerbates control flooding. Summary of the Invention
[0004] Technical problem: The technical problem to be solved by this invention is to minimize the overhead of security mechanisms while ensuring effective identification and isolation of black hole attacks, thereby maintaining efficient and reliable communication performance in complex threat environments.
[0005] Technical Solution: To solve the above technical problems, this invention proposes a secure AODV method based on trust assessment and behavior verification. This method not only models node reputation through historical interaction data, but also uses cryptographic methods to ensure the immutability of routing information, thereby making a comprehensive judgment on both the credibility of behavior and the authenticity of information.
[0006] Specifically as follows:
[0007] Step 1: Node records whether the data packets sent to neighboring nodes are successfully forwarded. When node A sends a data packet that needs to be forwarded to neighboring node B, it confirms by listening to the wireless channel whether node B forwards the data packet to the next hop node within the specified time. Successful forwarding is recorded as a positive action, and failure to forward is recorded as a negative action.
[0008] Step 2: Trust values are represented by discrete levels, divided into three levels: high, medium, and low, each corresponding to different security handling strategies; the evaluation of trust values is based on behavioral statistics within a sliding time window.
[0009] Step 3: Implement differentiated security strategies based on the trust level of each node:
[0010] Step 4: To ensure the timeliness of trust assessment and adapt to dynamic changes in the network, design an update and recovery mechanism.
[0011] in,
[0012] In the second step, the trust value is evaluated based on behavioral statistics within the sliding time window, and the specific calculation method is as follows:
[0013] Step 2.1, Initialization: The initial trust level of newly discovered neighbor nodes is set to medium to avoid premature trust judgment;
[0014] Step 2.2, Parameter Settings: Set the high trust threshold Low trust threshold is This is used for subsequent trust level classification; and The setting depends on different application scenarios and security sensitivities, but the value must be between 0 and 1. ;
[0015] Step 2.3, Behavior Recording and Statistics: Within a fixed sliding time window ω, continuously monitor neighbor node i and accumulate the number of positive behaviors for each node. Number of negative behaviors ;
[0016] Step 2.4, Forwarding Success Rate Calculation: Based on the statistical results, calculate the forwarding success rate of node i within this time window using the following formula. ,
[0017] ,
[0018] Step 2.5, Trust Level Determination: Based on forwarding success rate The final trust level of node i is determined by comparing it with a preset threshold. :
[0019] (2).
[0020] In step 3, the differentiated security handling strategy is specifically as follows:
[0021] Step 3.1, High Trust Node: Fully trusted, no additional verification is performed; data packets and routing messages from high trust nodes are directly accepted and processed, reducing the overhead of security verification;
[0022] Step 3.2, Medium Trust Nodes: Conditional trust is established; the hash chain verification method is used for routing response packets.
[0023] Step 3.3, Low Trust Nodes: Untrusted, regarded as black hole nodes, added to the local blacklist. All messages from blacklisted nodes are discarded and do not participate in any routing decisions.
[0024] The hash chain verification method is calculated as follows:
[0025] Step 3.2.1: The destination node D generates an initial seed value. Current seed value Set as Calculate the TopHash value at the tail of the chain:
[0026] ,
[0027] in, Hash is a cryptographic hash function. mhc This indicates the case where k=mhc. Where mhc is the maximum number of hops. Indicates to Applying the hash function k times consecutively:
[0028] ,
[0029] Step 3.2.2: Extract hash chain related information from the RREP packet, including: the current seed value. The hop count to the destination node, the maximum hop count (mhc) set by the network, and the hash value of the chain tail. ;
[0030] Step 3.2.3: Based on the extracted information, recalculate the hash value of the chain tail. :
[0031] ,
[0032] Step 3.2.4, will and Compare: If If the hash chain verification passes, it indicates that the RREP data packet has not been tampered with during transmission, the path information is reliable, and the trust level of the sending node will be increased. Update to advanced, and update the current seed value using the following formula. Otherwise, if verification fails, indicating potential forgery or tampering, the message will be discarded, and the trust level of the sending node will be lowered. Updated to low level.
[0033] ,
[0034] * The seed value is before the input. The new seed value is updated using the function described above;
[0035] This strategy strikes a balance between security and efficiency, preventing malicious behavior without excessively increasing costs.
[0036] In the fourth step, the design of the update and recovery mechanism includes:
[0037] Step 4.1, Real-time Update: After each time the behavior of an adjacent node is detected, immediately update the behavior record of that node and recalculate its forwarding success rate and trust level.
[0038] Step 4.2, Periodic Recovery: To prevent nodes from being unfairly treated for extended periods due to non-malicious factors such as temporary channel fluctuations or accidental packet loss, a fixed recovery cycle is set. At the end of each recovery cycle, the trust level of all nodes is reset to medium.
[0039] In step 2.2, Set between 0.7 and 0.9.
[0040] The It is usually set between 0.4 and 0.6.
[0041] In step 2.3, the fixed sliding time window ω is set to within 5 seconds.
[0042] Beneficial effects:
[0043] (1) Accurate identification of black hole attacks: This invention integrates behavioral trust assessment and hash chain verification mechanisms to comprehensively judge potential attacks from two dimensions: node behavior trustworthiness and routing information authenticity. By monitoring packet forwarding behavior to identify abnormal packet loss, and using hash chain verification to ensure the integrity of RREP, it effectively resists black hole attacks and significantly improves the routing security of FANET in malicious environments.
[0044] (2) Achieving a dynamic balance between overhead and security: Based on a differentiated processing strategy according to trust levels, high-trust nodes are exempt from additional verification, medium-trust nodes are verified using hash chains, and low-trust nodes are directly isolated, avoiding the waste of resources caused by uniformly implementing cryptographic operations across the entire network. This design significantly reduces computation, communication, and energy overhead while ensuring detection accuracy, adapting to the resource-constrained characteristics of UAV nodes.
[0045] (3) Enhanced environmental robustness: A sliding time window is used to statistically analyze node behavior and update the trust level in real time, enabling trust assessment to respond quickly to changes in node status and adapt to the dynamic characteristics of high-speed movement and frequent topology changes in FANET. At the same time, a periodic recovery cycle is set to eliminate misjudgments caused by instantaneous channel fluctuations or occasional packet loss, avoid long-term unfair treatment of nodes, and improve the fairness and practicality of the method. Attached Figure Description
[0046] Figure 1 This is a flowchart for detecting black hole attacks. Detailed Implementation
[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings. The parameter values involved in the following embodiments can be determined through simulation experiments to find their preferred ranges.
[0048] Combination Figure 1 The detection process for black hole attacks includes the following steps:
[0049] Step 1: When a node receives an RREP packet, it first performs a preliminary screening based on trust values. The specific steps are as follows:
[0050] (1) Identifying the sending node: Extract the address information of the sending node from the RREP message. This node may be the final destination node or an intermediate node with a valid route.
[0051] (2) Trust value query: Based on the sending node address, query the locally maintained trust value table to obtain the current trust level of the node. The trust level calculation process for a node is as follows:
[0052] (a) The initial trust level of newly discovered neighbor nodes is set to medium to avoid premature trust judgment.
[0053] (b) Set a high trust threshold With low trust threshold This is used for subsequent trust level classification. and The setting depends on different application scenarios and security sensitivities, but the value must be between 0 and 1. In this method, Set to 0.8, Set it to 0.5.
[0054] (c) within a fixed sliding time window (This method takes into account the high-speed dynamics of FANET, taking 5 seconds) Within this timeframe, it continuously monitors neighbor node i and accumulates the number of positive actions taken by each node. Number of negative behaviors .
[0055] (d) Based on the statistical results, calculate the forwarding success rate of node i within the time window using the following formula. .
[0056] ,
[0057] (e) Based on forwarding success rate The final trust level of node i is determined by comparing it with a preset threshold. :
[0058] ,
[0059] Step 2: For RREP packets marked as suspicious in the initial screening, a hash chain-based secondary verification mechanism is enabled to ensure the authenticity of the routing information. This includes the following steps:
[0060] (1) The destination node D generates the initial seed value. Current seed value Set as Calculate the TopHash value at the tail of the chain:
[0061] ,
[0062] in, For cryptographic hash functions (such as SHA-256). Indicates to Applying the hash function k times consecutively:
[0063] ,
[0064] (2) Extract hash chain related information from the RREP message, including: the current seed value. The hop count to the destination node, the maximum hop count (mhc) set by the network, and the hash value of the chain tail. .
[0065] (3) Based on the extracted information, recalculate the hash value of the chain tail. :
[0066] ,
[0067] (4) and Compare:
[0068] (a) If If the hash chain verification passes, it indicates that the RREP data packet has not been tampered with during transmission, the path information is reliable, and the current seed value is updated. :
[0069] ,
[0070] (b) If If the verification fails, it indicates that there may be forgery or tampering.
[0071] (5) Verification result processing:
[0072] (a) Validation successful: The RREP packet enters the final routing decision phase and the trust level of the sending node is determined. Updated to high.
[0073] (b) Verification Failure: The RREP packet is determined to be maliciously forged, the message is discarded, and the trust level of the sending node is reduced. Updated to low.
[0074] Step 3: The RREP message verified in the first two stages proceeds to the final decision-making stage.
[0075] (1) When there are multiple RREP packets corresponding to the same destination node, select the RREP packet with the smallest hop count.
[0076] (2) Update the local routing table and record the best path that has been verified. If this node is the final destination (i.e., the source node) of RREP, then trigger the sending of the data packet; if this node is an intermediate forwarding node, then continue to forward the RREP packet.
[0077] Step 4: To ensure the timeliness of trust assessment and adapt to dynamic changes in the network, the following update and recovery mechanisms are designed:
[0078] (1) Real-time update: After each time the behavior of an adjacent node is detected, the behavior record of that node is updated immediately. and And recalculate its forwarding success rate. Trust Level .
[0079] (2) Periodic recovery: To prevent nodes from being unfairly treated for a long time due to non-malicious factors such as temporary channel fluctuations and accidental packet loss, a fixed recovery period is set. At the end of each recovery period, the trust level of all nodes is reset to medium.
[0080] The specific embodiments described herein are merely illustrative examples of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to replace them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.
Claims
1. A routing method for ensuring the network security of highly dynamic flight self-organizing networks, characterized in that... This method not only models node reputation using historical interaction data but also employs cryptographic methods to ensure the immutability of routing information, thereby making a comprehensive judgment based on both behavioral credibility and information authenticity; specifically as follows: Step 1: Node records whether the data packets sent to neighboring nodes are successfully forwarded. When node A sends a data packet that needs to be forwarded to neighboring node B, it confirms by listening to the wireless channel whether node B forwards the data packet to the next hop node within the specified time. Successful forwarding is recorded as a positive action, and failure to forward is recorded as a negative action. Step 2: Trust values are represented by discrete levels, divided into three levels: high, medium, and low, each corresponding to different security handling strategies; the evaluation of trust values is based on behavioral statistics within a sliding time window. Step 3: Implement differentiated security strategies based on the trust level of each node: Step 4: To ensure the timeliness of trust assessment and adapt to dynamic changes in the network, design an update and recovery mechanism.
2. The routing method for ensuring network security of highly dynamic flight self-organizing networks according to claim 1, characterized in that, In the second step, the trust value is evaluated based on behavioral statistics within the sliding time window, and the specific calculation method is as follows: Step 2.1, Initialization: The initial trust level of newly discovered neighbor nodes is set to medium to avoid premature trust judgment; Step 2.2, Parameter Settings: Set the high trust threshold Low trust threshold is This is used for subsequent trust level classification; and The setting depends on different application scenarios and security sensitivities, but the value must be between 0 and 1. ; Step 2.3, Behavior Recording and Statistics: Within a fixed sliding time window ω, continuously monitor neighbor node i and accumulate the number of positive behaviors for each node. Number of negative behaviors ; Step 2.4, Forwarding Success Rate Calculation: Based on the statistical results, calculate the forwarding success rate of node i within this time window using the following formula. , ， Step 2.5, Trust Level Determination: Based on forwarding success rate The final trust level of node i is determined by comparing it with a preset threshold. : (2)。 3. The routing method for ensuring network security of highly dynamic flight self-organizing networks according to claim 2, characterized in that, In step 3, the differentiated security handling strategy is specifically as follows: Step 3.1, High Trust Node: Fully trusted, no additional verification is performed; data packets and routing messages from the high trust node are directly accepted and processed, reducing the overhead of security verification; Step 3.2, Medium Trust Nodes: Conditional trust is established; the hash chain verification method is used for routing response packets. Step 3.3, Low Trust Nodes: Untrusted, regarded as black hole nodes, added to the local blacklist. All messages from blacklisted nodes are discarded and do not participate in any routing decisions.
4. The routing method for ensuring network security of highly dynamic flight self-organizing networks according to claim 3, characterized in that, The hash chain verification method is calculated as follows: Step 3.2.1: The destination node D generates an initial seed value. Current seed value Set as Calculate the TopHash value at the tail of the chain: ， in, Hash is a cryptographic hash function. mhc This indicates the case where k=mhc. Where mhc is the maximum number of hops. Indicates to Applying the hash function k times consecutively: ， Step 3.2.2: Extract hash chain related information from the RREP packet, including: the current seed value. The hop count to the destination node, the maximum hop count (mhc) set by the network, and the hash value of the chain tail. ; Step 3.2.3: Based on the extracted information, recalculate the hash value of the chain tail. : ， Step 3.2.4, will and Compare: If If the hash chain verification passes, it indicates that the RREP data packet has not been tampered with during transmission, the path information is reliable, and the trust level of the sending node will be increased. Update to advanced, and update the current seed value using the following formula. Otherwise, if verification fails, indicating potential forgery or tampering, the message will be discarded, and the trust level of the sending node will be lowered. Updated to low level. ， * The seed value is before the input. The new seed value is updated using the function described above; This strategy strikes a balance between security and efficiency, preventing malicious behavior without excessively increasing costs.
5. A routing method for ensuring network security in highly dynamic flight self-organizing networks according to claim 4, characterized in that, In the fourth step, the design of the update and recovery mechanism includes: Step 4.1, Real-time Update: After each instance of monitoring the behavior of a neighboring node, immediately update the behavior record of that node and recalculate its forwarding success rate and trust level. Step 4.2, Periodic Recovery: To prevent nodes from being unfairly treated for extended periods due to non-malicious factors such as temporary channel fluctuations or accidental packet loss, a fixed recovery cycle is set. At the end of each recovery cycle, the trust level of all nodes is reset to medium.
6. A routing method for ensuring network security in highly dynamic flight self-organizing networks according to claim 5, characterized in that, In step 2.2, Set between 0.7 and 0.
9.
7. A routing method for ensuring network security in highly dynamic flight self-organizing networks according to claim 6, characterized in that, The It is usually set between 0.4 and 0.
6.
8. A routing method for ensuring network security in highly dynamic flight self-organizing networks according to claim 7, characterized in that, In step 2.3, the fixed sliding time window ω is set to within 5 seconds.