173 results about "Attack model" patented technology

Embodiments are directed to monitoring network traffic using network monitoring computers (NMCs). Anomalous events may be classified based on the monitored network traffic and attack models such that the classification determines that targets of the anomalous events may be currently subject to attacks by entities communicating on the networks. A honeypot trap may be provided in the networks based on the classified events such that the honeypot trap mimics characteristics of the targets. The portions of the network traffic associated with the honeypot trap may be monitored. Characteristics of the attacks may be determined based on the monitored portions of network traffic. Reports that include information based on the characteristics of the attacks may be generated.

The invention provides a large model attack method and device, electronic equipment and a storage medium, and the method is applied to a red team attack model, and comprises the steps: obtaining a first seed problem for a to-be-tested model, and carrying out the rewriting of the first seed problem, and obtaining a plurality of variable second seed problems; expanding the second seed problem by using a preset model jailbreak template and a preset adversarial sample to obtain an expanded adversarial sample; screening out target confrontation samples with aggressiveness meeting a preset aggressiveness requirement from the confrontation samples; and attacking the to-be-tested model by using the target confrontation sample. According to the method, the seed problem is gradually rewritten for multiple times, so that the problem of difficulty in cold start of the red team attack model is solved; and meanwhile, a high-aggressiveness confrontation sample is obtained for model attack training, so that the model attack success rate can be effectively improved, and the performance of the to-be-tested model is improved.

The invention discloses an elastic distributed Nash equilibrium search method and device under FDI attack, and relates to the technical field of non-cooperative game decision, the method comprises the following steps: constructing a multi-agent network, forming a non-cooperative game, and defining an objective function of each agent in the multi-agent network; establishing a malicious FDI attack model; the malicious FDI attack model maps false data injection attack to control input of an agent; an elastic self-adaptive distributed Nash equilibrium search algorithm is designed based on the target function, so that behaviors of other intelligent agents are estimated under the condition that the intelligent agents cannot obtain global information and attack information; the elastic self-adaptive distributed Nash equilibrium search algorithm comprises a leader-follower consistent consensus estimation-based identifier signal designed by an observer and a self-adaptive dynamic gain part. The elastic self-adaptive distributed Nash equilibrium search method solves the problem of elastic self-adaptive distributed Nash equilibrium search when the non-cooperative game is attacked by unknown FDI.

The invention discloses a hybrid attack defense control method and system for a heterogeneous cluster in an urban confrontation environment, terminal equipment and a medium, and relates to the technical field of unmanned platform cluster control, and the method comprises the steps: constructing an ideal system model of an unmanned platform cluster comprising a leader and a plurality of followers, constructing an information physical hybrid attack model based on the model; using the attack model to simulate an attack behavior of an attacker on an ideal system to obtain an attacked system model; based on an attacked system model, constructing a distributed elastic security estimator with a compensation mechanism, compensating attack influence for each follower and estimating an expected position; and based on the expected position of the follower, constructing a distributed elastic safety controller with a compensation mechanism, and controlling the follower. By designing the estimator and the controller, attack interference is counteracted, control input is generated, and safe collaboration of the heterogeneous nonlinear unmanned cluster under the cyber-physical hybrid attack is guaranteed.

The invention relates to the technical field of artificial intelligence, and discloses a large model security protection method, device and equipment and a storage medium, the method comprises the following steps: generating an attack data set through a preset attack model, the preset attack model being a model pre-trained based on a true harmful query statement, the true harmful query statements are query statements obtained by screening the query statements to be distinguished through an evaluation model, and the evaluation model is a large language model used for distinguishing whether the query statements are true harmful or not; constructing a security protection rule based on the attack data set, and performing security protection on the large model according to the security protection rule; the attack data set is generated by the preset attack model, the preset attack model is obtained by training the true harmful query statements, and the true harmful query statements are screened from the to-be-distinguished query statements through the large language model, so that the quality and the coverage range of the attack data set can be improved; and the safety protection effect of the large model can be improved.

Embodiments are directed to monitoring network traffic using network monitoring computers (NMCs). Anomalous events may be classified based on the monitored network traffic and attack models such that the classification determines that targets of the anomalous events may be currently subject to attacks by entities communicating on the networks. A honeypot trap may be provided in the networks based on the classified events such that the honeypot trap mimics characteristics of the targets. The portions of the network traffic associated with the honeypot trap may be monitored. Characteristics of the attacks may be determined based on the monitored portions of network traffic. Reports that include information based on the characteristics of the attacks may be generated.

The invention relates to the technical field of network detection, and provides a dynamic event trigger fault detection method under DoS network attack, which comprises the following steps: establishing a linear state space model of a network control system; defining a non-attack interval and an attack interval of system operation, and constraining attack frequency and duration; the observer gain is switched according to the current non-attack interval or attack interval of the system; a dynamic event triggering mechanism is constructed, and the triggering condition depends on the output state and the internal dynamic variable of the full-order switching observer and is used for dynamically adjusting the data transmission frequency; establishing a closed-loop switching system model; and analyzing system index stability according to the closed-loop switching system model, and cooperatively designing observer gain, controller gain and event triggering parameters. According to the invention, through quantification of the DoS attack model, the dynamic event triggering mechanism and collaborative optimization design, the effects of effectively detecting the system fault and improving the utilization rate of the network channel are achieved.

In a method for detecting an attack compromise window in a CMS website for which a temporal sequence of a plurality of snapshots of website backups have been stored, a temporally ordered set of spatial elements from each snapshot is constructed. Spatial metrics are computed for each individual snapshot's elements. The collected spatial metrics are temporally correlated and queried against attack models to recover an attack timeline. Attack events in the attack timeline are labelled. A sequence of assigned attack labels is verified. The compromise window is extracted from the plurality of snapshots.

The invention discloses an attack resisting method based on reconstructed data and a substitution model. The method comprises the following steps: 1) constructing a generative model: improving the performance of the generative model; 2) selecting a substitution model; 3) generating and initializing random noise and a pseudo label; 4) constructing a loss function for updating the generation model: combining the cross entropy loss function and the information entropy loss function to construct a new combined loss function; 5) constructing a distillation process loss function: combining the distillation loss function and the boundary loss function to construct a B-D combined loss function; 6) training the collaborative substitution model to generate a model; 7) performing knowledge distillation training on the substitution model by the collaborative generation model; 8) carrying out collaborative iteration updating on the generation model and the substitution model; according to the method, the quality of reconstructed data can be effectively improved, the success rate of attack resistance is better improved with low query cost, and an effective scheme is provided for the fields of attack resistance, model security evaluation and the like.

Disclosure are a backdoor attack method and system for a classification task in a code model, the method includes: S1. collecting and preprocessing clean samples to obtain importance variable names; S2. classifying the variable names of the clean samples according to label categories to obtain a plurality of trigger sets; and selecting target labels from the clean samples; S3. performing score calculation on the variable names in the trigger sets corresponding to the target labels; replacing one importance variable name with the variable name having a maximum C score in the clean samples to obtain poisoned samples, and repeating the above process until the labels are changed into the target labels; and S4. randomly inserting the triggers in the poisoned samples into the clean samples to form negative samples; and performing an attack by using an attack model obtained based on the negative, poisoned and clean samples.

Systems, devices, and methods are disclosed that may be used for identifying potential insider attacks on a computer network.

The invention discloses a classification model privacy disclosure risk black box detection method and device, and relates to the technical field of machine learning security, and the method comprises the steps: constructing an auxiliary data set; generating an auxiliary model covering different privacy risk levels; training a shadow model and a black box member reasoning attack model corresponding to the shadow model; evaluating the privacy risk score of each auxiliary model by using the attack model, and labeling a high-risk / low-risk label; calculating a correction prediction entropy difference, and screening the first k samples with the maximum difference to form a query data set; extracting a prediction result of each auxiliary model on the query set, calculating and correcting a prediction entropy and a mean value, a variance, a kurtosis and a skewness thereof, and splicing into a k + 4-dimensional risk feature vector; training a risk detection classifier; similar features of the model to be detected are extracted and input into the classifier, and the classifier performs five-level privacy disclosure risk level mapping according to the final risk probability prediction value and outputs the five-level privacy disclosure risk level mapping. The method is mainly applied to risk assessment of classification models in high-privacy sensitive fields such as financial credit investigation and medical diagnosis.

The invention belongs to the technical field of artificial intelligence security, and particularly relates to a federal learning data poisoning attack system and method based on privacy reasoning, a program and a storage medium. The invention provides an actual data poisoning attack model, attack hypotheses are reduced, and poisoning attack quality is ensured. Effective poisoning attack can be realized under the condition that an attacker is not required to have a verification data set with the same distribution as a benign client. According to the method, data acquisition is realized through a privacy reasoning technology, data diversity of attackers is enriched through data enhancement, poisoning attacks are finally realized by modifying the corresponding relation between data and real labels, the effectiveness of a federated learning model is greatly destroyed, the method can be deployed in a framework based on federated learning, and the method is suitable for popularization and application. And the method is suitable for various distributed machine learning scenes.

The invention discloses a multi-agent system safety convergence control method based on dual-channel adaptive event triggering, and the method comprises the steps: firstly building a multi-agent system mathematical model, and designing a controller of each agent in a multi-agent system based on the multi-agent system mathematical model; secondly, establishing a sequence scaling attack model, and combining the sequence scaling attack model to a controller of an intelligent agent; and then designing an event trigger controller based on the controller of the intelligent agent containing the sequence scaling attack. And then, based on the event triggering controller, safety convergence control is carried out, and multi-agent control in the multi-agent system is completed. According to the method, malicious network attacks can be effectively resisted, the problem of limited bandwidth resources can be solved, and the applicability and the application range are improved and expanded.

The invention discloses a black box attack confrontation sample generation method, and belongs to the technical field of artificial intelligence security. The invention discloses a black box attack confrontation sample generation method. The method comprises the following steps: S1, obtaining label information; s2, generating a first adversarial disturbance and tampering adversarial sample; s3, constructing and optimizing an adversarial attack model; and S4, generating and verifying a second confrontation sample. The problems that an existing black box attack method depends on model information, a large number of query requests need to be sent to the target model, a large number of computing resources are consumed, and the quality of generated images is low are solved. Through joint optimization of the PSO algorithm and the PID controller, the method does not depend on specific model information, significantly reduces query times, avoids redundant disturbance, is small in difference between the generated adversarial sample and the original image, is high in concealment, greatly improves the attack success rate and efficiency, and enhances the flexibility and universality of black box attacks.

The invention relates to the technical field of intelligent power distribution networks, and discloses a node coupling degree-based power grid continuous information attack defense method, system and device and a medium, and the method comprises the steps: through the representation and analysis of the coupling degree, the damage degree of various fault conditions to a power distribution network can be evaluated more accurately. In the aspect of attack defense, a continuous information attack model is constructed, the model not only considers division of a security area and configuration of electronic security perimeter equipment, but also combines related factors such as defense equipment types and the number of operation and maintenance personnel, and comprehensively depicts the process and strength of information attack. Therefore, formulation of a defense strategy is more targeted and effective, and proper defense measures can be taken according to different attack scenes and node coupling conditions. Meanwhile, by testing the defense strategy, the defense scheme can be continuously optimized and perfected, and the toughness of the power grid CPS to malicious information attacks is improved.

The invention discloses a large model output data security detection method and system based on adversarial attacks. The method comprises the following steps: constructing and optimizing a strategy space containing a plurality of attack strategies, and grading and sorting the strategies to improve the attack efficiency; generating a single-strategy antagonism prompt by the attack model according to the optimized strategy space, and performing effectiveness evaluation and feedback correction on the prompt by the judgment model; inputting a prompt passing the evaluation into the target large model to obtain a response, and performing malicious degree scoring on the response by the judgment model; and if the single-strategy attack is not successful, introducing an optimization mechanism based on a genetic algorithm, generating a more complex multi-strategy antagonism prompt through strategy variation and crossover, and carrying out iterative attack until the target large model is successfully broken into the prison. According to the method, the security defects of the large model can be efficiently and comprehensively detected in a self-adaptive and multi-strategy attack mode.

The invention discloses a coding encryption method for industrial information physical system spoofing attack detection. The coding encryption method comprises the following steps: step 1, establishing an industrial information physical system model; step 2, establishing a coding encryption mechanism, realizing a data encryption and decryption process without spoofing attack, and generating a detection residual error; 3, according to the coding encryption mechanism and the residual error in the step 2, introducing an information physical system spoofing attack model, and analyzing the residual error change under the attack; 4, based on the residual change, selecting a detection statistic, setting a detection strategy and a detection threshold, and completing spoofing attack detection; and step 5, introducing a coding and decoding restarting mechanism to guarantee the normal operation of the system after the attack is ended and under the condition that a coding error exists. By encoding, decoding and encrypting the transmission data, spoofing attack detection is realized, certain confidentiality is provided, and the problems of limitation, difficulty in implementation and the like of the existing spoofing attack detection scheme can be effectively solved.

The invention discloses a method and device for determining an initial fault line combination of a power system considering cascading failure propagation, electronic equipment and a storage medium, and belongs to the technical field of power system fault recognition. The method comprises the following steps: constructing an attack and defense double-layer optimization model comprising an upper-layer attack model and a lower-layer defense model, wherein the upper-layer attack model aims at maximizing the load loss value, and the lower-layer defense model aims at minimizing the load loss value; the method comprises the following steps of: randomly generating a plurality of candidate fault line combinations containing a preset number of fault lines, respectively simulating a cascading fault propagation process caused by the candidate fault line combinations, obtaining a power grid working condition after a fault is ended, and calculating a load loss value of each candidate combination by utilizing a lower-layer defense model; and selecting the candidate fault line combination with the maximum load loss value as an initial fault line combination of the power system. By implementing the method, the problem that the initial fault line combination of the power system is not accurately determined in the prior art can be solved.

The invention discloses a gateway test method, system and equipment based on artificial intelligence, and the method comprises the steps: building and training a reinforcement learning attack model according to a test demand, and enabling the reinforcement learning attack model to take a test target as input and a DDoS attack scheme for the test target as output; according to a received DDoS defense test request of a target gateway, obtaining a target DDoS attack scheme for the target gateway through the reinforcement learning attack model; performing a DDoS attack test on the target gateway according to the target DDoS attack scheme to obtain test data of the target gateway; and analyzing the test data according to the test data to obtain a test result of the target gateway. The problems that in the prior art, diversified and highly-real DDoS attack testing means are lacked, and more intelligent defense evaluation cannot be carried out for a complex network environment are at least solved.

The invention relates to the field of power distribution network system protection, and discloses a power distribution network cyber-physical system network attack optimization method which comprises the following steps: S1, establishing an upper-layer attack model which is used for simulating a decision of an attacker for causing the maximum load loss under the constraint of limited attack resources; s2, establishing a lower-layer rescheduling model, wherein the lower-layer rescheduling model is used for simulating a response decision taking minimization of load shedding loss and scheduling cost as targets after a power distribution network dispatcher is attacked; and S3, constructing an attack and defense double-layer optimization model combining the upper-layer attack model and the lower-layer rescheduling model. Through sequential logic based on attack and defense, the first stage starts from the perspective of attackers to maximize attack consequences and minimize attack cost, and the second stage minimizes scheduling and operation cost from the perspective of dispatchers to guarantee power supply of key users, so that the influence of network attacks on the power distribution network is comprehensively and accurately evaluated.

Methods and systems for identifying cybersecurity threats construct a graph from a CISA security alert and traverse the graph to identify a cybersecurity threat by monitoring data in the system and updating the state of the graph. Tactics in the CISA alert are represented by nodes in the graph, techniques in the CISA security alert represented by edges in the graph Traversing the graph is performed by receiving information from the system and for each graph representing an attack model comparing the attack model graph to the received information to identify a match and updating the graph state based on the match. Updated graphs may be displayed to a user showing the state of an attack vector in the graph. The state of the attack vectors may be updated with information from the system and displayed in a color corresponding to a traffic signal.

The invention relates to the technical field of power system data processing, in particular to an optimal false data injection attack method and system for load-frequency control in a power system, and proposes time domain formalized modeling to find an OFDIA, minimize successful attack time, construct an OFDIA model and optimize load attack quantity; load measurement values of the whole power grid are considered in a compromise mode, the influence of power system parameters on successful attacks is analyzed, and a load frequency model of the power grid is modeled; modeling the dynamic behavior of the power system in the time domain optimization framework; and establishing a false data injection attack model, constructing an OFDIA model, and optimizing the load attack quantity. According to the method, under the condition that an attacker completely accesses the data of the power system, the damaged measurement number and the starting time are reduced, meanwhile, the attack concealment is ensured, consideration is provided for a defense mechanism of a control center, and safe operation of a power grid is ensured.

The invention provides a network intrusion detection method, device and equipment, belongs to the technical field of network security, and solves the problems that a traditional network intrusion detection method is insufficient in cross-modal feature fusion, weak in complex attack modeling capability and poor in adaptive capability. The method comprises the following steps: acquiring network flow data; preprocessing the network traffic data to obtain a traffic data packet; performing feature extraction on the traffic data packet to obtain a multi-modal feature set; performing point-line analysis processing on the multi-modal feature set to obtain heterogeneous graph data; inputting the heterogeneous graph data into a detection model for processing to obtain a graph-level feature vector; and determining intrusion type data according to the graph-level feature vector. According to the scheme, the detection accuracy of diversified attacks is improved, the structure perception capability of complex attacks is enhanced, and the robustness and generalization are improved.

The invention discloses a security alarm information processing method and device based on multi-agent cooperation, and relates to the technical field of honey point alarms, and the method comprises the steps: obtaining an original alarm flow from a honey point management module, and carrying out the preprocessing of the original alarm flow, and obtaining an alarm set; pushing the alarm set to an alarm noise reduction agent so as to filter false alarm information through a large language model noise reducer, then transmitting the false alarm information to a priority marker to endow the alarm set with a corresponding priority mark, and storing the marked alarm set into a historical alarm database; and pushing the alarm set to an attack model agent to generate an attack hypothesis through an attack mapping large language model, transmitting the attack hypothesis to an attack path backtracking engine, querying a historical alarm database to obtain associated historical attack data, and generating a corresponding analysis report after constructing a specific attack path. According to the invention, the problem of low safety alarm information analysis efficiency and accuracy in the prior art is solved.

The invention discloses a malicious attack detection method and device based on code rules, equipment and a medium, and relates to the field of network security. The method comprises the steps of obtaining a source code of a target program, and extracting a target program code; constructing a control flow diagram based on the target program code, and detecting whether a potential malicious behavior exists or not by analyzing a data flow in combination with a predefined rule set; simulating runtime behaviors of the programs with potential malicious behaviors, setting monitoring points to monitor resource access, network behaviors and file operation behaviors of the programs, and detecting whether abnormal behaviors occur or not; constructing a hostile attack model, training by using a known malicious code sample, extracting code features from a program with an abnormal behavior, and inputting the model to classify the program as a hostile attack program or a normal program; and in the actual execution process of the target program, monitoring the execution path and the external behavior of the code, and through comparison with the hostile attack model, identifying the hostile attack and triggering an alarm. According to the invention, the hostile attack detection accuracy is obviously improved.

The invention discloses a multi-round jailbreak attack defense training method and device for a large language model. The method comprises the steps that an attack model used for generating query statements and a victim model used for providing response statements are constructed; utilizing an attack model to extract harmful targets of the plurality of received jailbreak prompt words, and generating a clue data set; constructing a clue tree based on the clue data set, and determining a plurality of paths of the clue tree, thereby generating a plurality of inquiry chains through iteration of the plurality of paths by using an attack model; inquiring the victim model by using each inquiry chain, determining unsafe response statements in a plurality of response statements generated by the victim model, and generating a plurality of question and answer chains based on each unsafe response statement and the corresponding inquiry statement; and based on the plurality of question and answer chains and historical dialogue information extracted from the target model, constructing a security training data set, and training the target model by using the security training data set.

The application provides a kind of based on Transform and prompt guide imperceptible watermark attack method, belongs to digital information security technical field.Method includes: through existing data set and QPHFMs watermark algorithm construction training data set, adjusts the size of original watermark image and original non-watermark image containing uniformly, obtains standardization training data;Imperceptible watermark attack network TFP-WAN containing Transform encoder-decoder framework and dynamic prompt module is constructed, dynamic prompt module includes PGM and PIM;MAE is used as loss function in TFP-WAN;Standardization training data is input into TFP-WAN and iteratively trained until reaching preset iteration number, and the watermark attack model of training completion is obtained;The image after attack is output after inputting the watermark image to be attacked into watermark attack model.The application realizes the attack to robust watermark algorithm with higher error rate.

The embodiment of the invention provides a model poisoning defense method, electronic equipment and a readable storage medium. The method relates to the field of distributed training, and comprises the following steps: acquiring a first model trained by adjacent participants and first similarity information maintained by the adjacent participants; determining a second similarity between a target model trained by the target participant and the first model; according to the first similarity and the second similarity, performing credibility evaluation on whether the first model is subjected to the poisoning attack to obtain a credibility evaluation result of the first model; determining a model aggregation mode of the first model and the target model based on the credibility evaluation result; and according to the model aggregation mode, performing model aggregation processing on the target model and the first model to obtain an aggregated target model. According to the method and the device, the technical problem that the performance of the aggregated global model is reduced due to the fact that the attacked model is difficult to distinguish by related technologies for completely decentralized distributed training and then through a model aggregation process is solved.

The application discloses a kind of inference system and method for sequence recommendation system member inference attack, including label data generation module, difference feature construction module and attack model training module;Step 1, label data generation is carried out;Step 2, the difference feature construction of member and non-member is carried out;Step 3, the training of attack model is carried out.Compared with prior art, the application can guarantee the data privacy of user in a wider range of scenarios;Fill in the blank of member inference attack in more stringent scenarios;Significantly improve the attack inference effect.