423 results about "Penetration test" patented technology

The present application provides a network testing method and apparatus, an electronic device, a storage medium, and a program product. The method comprises: obtaining target network node information of a target network node, and determining a virtual network topology on the basis of the target network node information; by using a vulnerability knowledge graph, determining target patch information and target vulnerability information corresponding to the target network node; on the basis of preset network node rewards and attack operation success rates, performing a virtual network penetration test on the virtual network topology where the target network node is located, so as to obtain a penetration test result; on the basis of the penetration test result, determining an exploited network node, and on the basis of the vulnerability knowledge graph, determining remediation patch information, so as to remediate the exploited network node by using the remediation patch information; performing a virtual network penetration test on the exploited network node and the target network node, so as to obtain a post-remediation penetration test result; and on the basis of a comprehensive score corresponding to the post-remediation penetration test result and a comprehensive score corresponding to the penetration test result, respectively obtaining a vulnerability report and a defense report for the target network node.

The invention discloses an unmanned inspection and intelligent fault judgment method. A security threat model is constructed through deep learning, potential risks are identified, a strategy is made, security analysis modeling based on a comparison technology is utilized, and system state abnormity is detected. In data transmission, confidentiality and integrity are guaranteed by integrating a plurality of security means, a multi-level protection framework is designed, and all-round defense is carried out from a network boundary, a host terminal and an application system. By means of anomaly detection monitoring index data, anomaly and risk are subjected to correlation analysis, and a basis is provided for decision making. And regularly carrying out security assessment and penetration testing, repairing vulnerabilities, and carrying out optimization and reinforcement. The method covers multiple aspects such as threat modeling, safety analysis, data protection, protection framework construction, anomaly detection and evaluation reinforcement, the safety and reliability of the unmanned inspection system are effectively improved, network attacks and illegal access are resisted, and stable operation of the system is guaranteed.

The invention relates to the technical field of automobile network security, in particular to an intelligent networked automobile penetration test path generation method and system based on a knowledge graph, and the method comprises the steps: constructing an intelligent networked automobile intranet penetration test security knowledge graph through fusing multi-source heterogeneous data based on an intranet E / E architecture; generating a potential attack path based on the knowledge graph by using a depth-first search algorithm of dynamic pruning optimization; a multi-dimensional space-time risk assessment framework is utilized to quantify path threat intensity and execution feasibility, and quantitative assessment on the generated attack path is realized; and according to an evaluation result, selecting a high-risk and feasible attack path to carry out an actual penetration test, and discovering security vulnerabilities and weaknesses in the system. According to the method, the attack surface is fully covered, the high-value attack path is efficiently generated, the threat intensity and the execution feasibility are accurately and quantitatively evaluated, and the safety test efficiency and accuracy of the intelligent network connection automobile intranet are remarkably improved.

The invention discloses an information scanning and penetration test path planning method in a Windows domain based on reinforcement learning. The method comprises the following steps: carrying out information detection on a target Windows domain environment, surveying and mapping a network topology of the target Windows domain environment, establishing connection with a target machine by utilizing protocols such as LDAP (Lightweight Directory Access Point) and the like, and collecting related information in the domain; the method comprises the following steps of: collecting, sorting and abstracting detected related information, solving a multi-stage sequence decision problem by utilizing a Markov decision process, realizing environment simulation, constructing a reinforcement learning agent, generating a penetration test path by performing interactive training with a simulation environment, and performing adjustment according to a reward strategy so as to obtain a penetration test result. And searching an optimal penetration test path by accumulating rewards, constructing a Windows domain penetration test model, and finally generating the optimal penetration test path. According to the method, collection of information in the Windows domain and planning of the penetration test path are realized, and finally, the environmental safety of the Windows domain is evaluated and maintained.

The invention discloses a method based on a large language model and ATTamp; the invention discloses a CK framework penetration test automation method and device, and the method comprises the steps: firstly carrying out the structural analysis of multi-source input information and tool output, and guaranteeing that key fields are not discarded; then combining a retrieval enhancement generation technology and a network security knowledge base to provide domain knowledge support for the large language model, so as to generate a model with ATTamp; a penetration test task tree marked by CK tactics, technologies and sub-technologies; on the basis, an optimal tool is automatically selected through a tool resource library and a multi-dimensional screening mechanism, an execution instruction is generated, and finally an execution result is returned to the input analysis module to form a self-adaptive optimization test closed loop. According to the method, semantic fidelity compression and standardization processing of long information can be realized aiming at the problems of large output format difference, more information redundancy and the like of different penetration testing tools, and efficient, explainable and auditory technical support can be provided for automatic penetration testing in a complex network environment.

The invention provides an attack fault tree security risk assessment system based on deep reinforcement learning, and relates to the technical field of information security. The system specifically comprises a network asset data fingerprint scanner module, a deep reinforcement learning network model generation and training module and an automatic penetration test tool set module. The invention aims to determine the optimal attack path from the system attack graph by using deep reinforcement learning. By combining the screened attack paths with various resources and time costs encountered in the actual penetration test, the failure rates of various security risks in the attack fault tree, the success probabilities of various attacks and other indexes can be determined. And finally, establishing an attack fault tree of a power system scene in the embodiment, comparing the attack lethality and the detection capability level of each attack path in a game phase, and qualitatively and quantitatively analyzing the final security capability level of the target system and the damage degree caused by attacks.

Systems and method for detecting vulnerabilities in code are provided herein. A pre-trained artificial intelligence (AI) model is engaged, and a plurality of prompts and the source code are provided to the AI model. A plurality of detected vulnerabilities and a plurality of code locations in the source code are identified using the AI model. Each of the plurality of code locations corresponds to at least one of the plurality of detected vulnerabilities. One or more false positive vulnerabilities in the plurality of detected vulnerabilities are identified. A plurality of augmented prompts is generated, based on the one or more false positive vulnerabilities. The plurality of augmented prompts is outputted to a database of prompts for use in future code analyses, without necessarily having to retrain the AI model.

The invention provides an efficient multi-dimensional network penetration testing method based on RAG, which comprises the following steps: firstly, identifying sub-domain names possibly existing in a target website, and sequentially expanding attack surfaces of penetration testing to obtain vulnerability information pairs; secondly, a design model generated based on retrieval enhancement is adopted, vulnerability information pairs are extracted from the queue to be utilized, knowledge items related to local knowledge base retrieval and network intelligent search retrieval are utilized, and finally, a large model generates vulnerability utilization information according to the knowledge items; acquiring target machine permission for the previously acquired vulnerability information pair construction command injector, and further scanning other hosts of the intranet accessed by the target skipping machine; finally, combining vulnerability information obtained by penetration testing, utilizing a large model to sort vulnerabilities existing in different assets, and outputting penetration testing reports for different assets. According to the method and the system, comprehensive penetration testing of cross-network and cross-system is realized, security experts are helped to quickly identify, verify and repair vulnerabilities in a complex and changeable network environment, and high-efficiency and low-cost network security maintenance is realized.

Aspects described herein may automatically generate threat models using large language model (LLM). A computing device may send, to LLM, one or more software modules associated with a computing system. The computing device may request the LLM to generate a threat model of the computing system. The computing device may receive, from the LLM, a first output based on the first prompt comprising first information for a first version of the threat model and a penetration test script for the computing system. The computing device may input, to the LLM, the result of the penetration test together with the LLM's previous output, to facilitate the LLM to generate a refined version of the threat model.

The invention provides a Web automatic penetration test method and device, electronic equipment and a storage medium, and belongs to the technical field of website vulnerability tests.The method comprises the steps that a web crawler collects target Web information to form a basic set; detecting vulnerability acquisition information; matching the adaptive script, and generating an intelligent load to verify vulnerability availability; constructing a knowledge graph to determine vulnerability association, and evaluating success rate and risk; planning an optimal attack route by using reinforcement learning, and determining attack depth and range, namely attack path risk features; and evaluating the risk by the dynamic scoring model, and generating a visual report containing detailed information, results and repair and improvement suggestions. According to the automatic penetration testing method, manual intervention is reduced in the whole process, dependence on professional experience is reduced, efficiency is improved, and the problems that traditional testing is tedious and low in efficiency are solved.

The invention is suitable for the technical field of payment data system vulnerability assessment, and provides a payment data security assessment method and system.The method comprises the steps that after vulnerability scanning and penetration testing operation is conducted on a payment data storage system, a plurality of scanned security defects are determined, and acquiring a preliminary risk priority evaluation index allocated to each security defect, and acquiring a historical operation log of the payment data storage system. According to the method, the mechanism of double correction factors is introduced, so that the repair priority evaluation of the potential risk vulnerabilities in the payment data storage system is optimized. Firstly, in combination with a hysteresis effect correction factor and a secret key updating frequency deviation correction factor, the accuracy and timeliness of vulnerability repair are effectively improved;

The invention discloses a graph structure-based domain penetration attack path generation method, which belongs to the technical field of network security, and comprises the following steps of: constructing a multi-level relation graph comprising a host node, a service node and a user node through automatic detection by taking any host in a domain as a starting point; assigning a weight attribute to the atlas edge based on a vulnerability library and an attack pattern library; an improved heuristic graph search algorithm is adopted, all feasible attack paths and threat scores thereof are generated by integrating the path length, attack difficulty and permission improvement effect, and the problem that the threat scores of all the feasible attack paths are influenced in various scenes and dynamic change domain environments is solved. The technical problems of realizing comprehensive automatic penetration testing, accurately identifying potential attack paths and establishing a systematic threat assessment mechanism are solved, the automation, intelligence and high-efficiency level of domain penetration testing is remarkably improved, and the method has good adaptability, expansibility and practical value and is suitable for popularization and application. And attack path discovery and risk early warning work in a dynamic network environment with high security requirements can be effectively supported.

The invention discloses a multi-model collaborative penetration testing method and device and electronic equipment, and relates to the field of data processing. In the method, target asset information and external knowledge data of a target system are obtained, and the target system is a to-be-permeated test object; analyzing the target asset information in combination with external knowledge data to obtain corresponding structured penetration data; obtaining the current state of the target system, inputting the current state and the structured penetration data into a preset large language model, and generating a penetration test strategy; based on the penetration test strategy, adopting a preset reinforcement learning algorithm to generate an attack path; the attack path corresponds to the original; inputting the attack code into a preset generative adversarial network to generate a confusion attack code; and performing penetration test on the target system based on the attack path and the obfuscated attack code to obtain a penetration test result. By implementing the technical scheme provided by the invention, the penetration test coverage rate is improved.

The invention discloses a concrete waterproof coating performance testing method and device, belongs to the technical field of waterproof material performance detection, and aims to improve and optimize a sample detection mode and a detection method based on a conventional water pressure penetration testing principle. Samples forming an annular structure can be conveniently placed in a detection cylinder, multiple groups of waterproof coatings on the samples correspond to multiple detection cavities in a one-to-one mode, multiple samples can be detected in the same group, forward water pressure testing from outside to inside and then reverse water pressure testing from inside to outside are adopted, and the samples do not need to be replaced in the testing process; in the successive pressurization gap of the positive and negative water pressure test, the waterproof coating is repeatedly dried through the heating plate, the repeated waterproof environment of the waterproof coating in the actual engineering is simulated, and the detection accuracy is improved.

The invention discloses a medical network collaborative penetration test system and method based on artificial intelligence, and belongs to the technical field of medical information security, and the method comprises the steps: integrating a hospital intranet, medical equipment, cloud data center data and historical network delay data to construct a medical network data center; analyzing association between network vulnerabilities and delay data through a graph neural network, constructing an attack path analysis model containing delay weights, and quantifying risk scores to generate a test target priority list; defining a protocol vulnerability and a delay threshold, designing a test scene and generating an attack vector, matching an attack tool to perform a simulation test, calculating a vulnerability triggering success rate and evaluating a risk; generating a penetration test case according to the test target priority list and the risk level, executing multiple rounds of attack tests and generating a report; and monitoring the change of the vulnerability triggering success rate by dynamically adjusting the delay parameter, setting an early warning threshold value, and monitoring and sending a risk early warning notification in real time.

The invention provides a penetration test system and method based on multi-agent collaboration, and the system comprises an interaction module, and a plan generation agent, a plan planning agent and a plan execution agent which comprise a large language model LLM, and the interaction module is used for receiving a to-be-solved problem inputted by a user; the plan generation agent is used for generating a penetration test task plan according to a to-be-solved problem; the plan planning agent is used for splitting the penetration test task plan to generate a plurality of sub-plans; a reinforcement learning technology is utilized to screen out an optimal adaptive API of an optimal tool corresponding to the current plan description, parameters required by the current API are extracted from the subtasks, and a final penetration test command is generated; and the plan execution agent is used for receiving the penetration test command transmitted by the plan planning agent, executing the command and recording a generated execution result. According to the method, a user only needs to give a penetration test target and purpose, a penetration test scheme can be automatically given, a penetration test task is carried out, and a result is finally given.

The invention discloses a graph-driven self-attention compressible memory management method, and belongs to the technical field of penetration testing. According to the graph-driven self-attention compressible memory management method, an attention graph is constructed, semantic dependence and attention flow directions among multiple Agent nodes are accurately described by utilizing edge weights, the relevance and the accessibility of information are improved, and in the aspects of screening and loading, the expandability of the memory is improved. According to the method, key memory fragments are screened on the basis of concerned graph edge weights and task related features, only the loaded information is loaded, redundancy is effectively eliminated, the data volume processed by a system is reduced, and the system operation efficiency is improved; the semantic compression module can perform abstract processing on historical information based on various elements, and on the premise of ensuring that key semantic information is not lost, the abstract length is controlled within the window limit, so that the model can normally process information, and the adaptability and stability of the system to different data volumes are improved.

A system for automated penetration testing using artificial intelligence (AI) is described, which aims to enhance cybersecurity by simulating attacks on software and computer systems in order to measure and identify vulnerability. The platform employs AI agents to perform tasks including script execution for vulnerability testing, social engineering simulations, comprehensive security assessments, and more, thereby reducing costs, time, potential risks associated with traditional penetration testing methods, and providing a more complete and available solution than humans can produce alone.

Systems and methods for testing, evaluating, and scoring IT products (e.g., software) and product updates from a technology provider are disclosed herein. More specifically, organizational assessment may be performed to evaluate the provider's development lifecycle processes and generate organization maturity scores. Architecture assessment may be performed to evaluate the system-level and software-level architectures associated with the application and generate architecture maturity scores. Product verification may be performed via automated testing and penetration testing to generate verification maturity scores. The organization maturity scores, architecture maturity scores, and verification maturity scores may be used to provide recommendations to the provider and also combined into an overall maturity score, which may serve as a comprehensive summary of the evaluation. These generated scores inform and expedite testing of future iterations of the product.

The invention discloses a Windows domain penetration test system and method based on deep reinforcement learning. The system comprises an information collection module, a vulnerability detection module, an agent generation module and an agent training module. A host in an intranet domain is used as a springboard machine, an information collection module is deployed, and all network nodes in the domain are subjected to comprehensive information collection; generating a configuration file for constructing an intelligent agent according to the information in the domain, and constructing the deep reinforcement learning intelligent agent through the configuration file; and the intelligent agent automatically verifies all possible potential safety hazards in the domain environment by using a vulnerability detection module, learns in detection, and finally generates all possible safety problems in the domain. According to the method, the automatic penetration testing technology is utilized, full-automatic penetration testing and risk assessment of the domain environment are achieved, deep reinforcement learning is combined, various safety problems in the Windows domain are found more accurately, resources are allocated efficiently, high-risk path areas are defended in a centralized mode, and therefore the overall network safety is enhanced.

A time sequence post-synchronization quantum key extraction penetration test method comprises the following steps: A) under the condition of penetration test, a QKD system operates normally, and a sending end and a receiving end smoothly complete key distribution; b) the man-in-the-middle selects an initial original key exchange period to establish synchronization and correlation so as to realize clock synchronization with a receiver; c) analyzing quantum bit error rate information obtained from a public channel by a man-in-the-middle, deducing a corresponding relation between a receiver detector and each quantum state, and establishing a mapping model of the quantum states and bit values; d) determining a quantum state type responded by the receiver in each response time slot in a subsequent original key exchange period by the middleman in combination with path information obtained by other sub penetration tests; and E) the intermediary obtains a final key which is the same as the two communication parties by implementing an error correction and privacy amplification process, the quantum bit error rate between the intermediary and the sender is maintained at a relatively low level and is lower than a security threshold, and a security alarm is not triggered in a penetration test process.

The embodiment of the invention provides an intelligent penetration testing method and device based on deep reinforcement learning, and the method comprises the steps: constructing a network information environment, setting basic parameters, initializing the network information environment, and constructing an act-critic structure with an independent strategy network and an independent value network; modeling the Markov decision process of the penetration test into a quaternion group lt; s, A, R, Pgt; a Markov model is constructed; constructing a priority playback experience pool; performing penetration testing based on a Markov model, calculating and outputting an attack action to a network information environment through a strategy network based on a current penetration state to obtain a next penetration state, rewarding the attack action through a value network, and storing data in a priority playback experience pool; and calculating and determining a mode of preferentially selecting sampling according to the priority of experience, repeating the penetration test operation of the Markov model until the function is converged, and outputting an optimal penetration strategy.

The invention relates to an automatic penetration testing method and device based on large model driving. The method comprises the following steps: acquiring environment information of a target host through a trained large language model according to a received penetration test instruction aiming at the target host; wherein the environment information at least comprises multiple pieces of service information of the target host, and each piece of service information is used for representing one service in the target host; according to a pre-established vulnerability database and the large language model, obtaining vulnerabilities of a plurality of services in the target host and vulnerability utilization key information of each vulnerability; screening out target vulnerability utilization key information of each vulnerability from each vulnerability utilization key information according to the environment information; and performing penetration testing on the target host by using key information according to the environment information and each target vulnerability, and generating a penetration testing report. By adopting the method, the penetration test efficiency can be improved.

A penetration test large model agent based on ATT and CK attack knowledge enhancement comprises the following steps: step 1, constructing an attack knowledge combining ATT and CK attack knowledge, extracting attack tactical sequences and attack cases from a large number of threat intelligence documents, and forming an attack tactical tree; 2, constructing an attack tactical transfer graph: analyzing a transfer flow and a control flow of an attack tactical based on the attack tactical tree, and constructing the attack tactical transfer graph; and step 3, automatic penetration testing based on the large model intelligent agent: constructing a supervisor intelligent agent and a tactical intelligent agent, and completing the automatic penetration testing based on the large model under the guidance of the attack tactical transfer graph.

The invention relates to the technical field of automobile part information security evaluation, and discloses an automobile part information security evaluation system based on a multi-dimensional penetration test, and the system comprises a data collection module, a vulnerability analysis module, an event monitoring module, a parameter matching module, a response execution module and a self-learning module. The data acquisition module acquires test data in real time, and generates a dynamic adjustment set of vulnerability severity feature values and security parameters; the vulnerability analysis module generates a personalized security parameter and a dynamic penetration control strategy based on the feature value; the event monitoring module screens abnormal event data; the parameter matching module determines a target adjustment parameter through multi-dimensional matching; the response execution module drives the safety equipment to correct parameters; and the self-learning module updates the security parameters by using the feedback data. The system realizes dynamic evaluation and self-adaptive protection of automobile part information safety, improves the safety protection level, and is suitable for the technical field of automobile part information safety evaluation.

The invention is suitable for the technical field of computer network security penetration testing, and relates to a post penetration testing remote control management system and method for team multi-person collaboration, and the system comprises a user management module which is used for creating, managing and distributing accounts and permissions of penetration testing team members, supporting the distribution and adjustment of different permission levels, and providing a user management module; each member is ensured to execute operation within an authorization range; the attack load generation module is used for generating an executable attack load according to the architecture and the network environment of the target system and providing an encryption communication mechanism; the unified Shell management module is used for managing interactive sessions of controlled ends in a centralized manner, supporting multiple protocols to remotely execute commands, and providing session maintenance, batch operation and command history recording functions; the log auditing module is used for recording and storing all operation logs on the remote control management platform and supporting log query, auditing analysis and exporting functions; the working efficiency of the management platform and the post-penetration test quality are effectively improved.

According to the penetration test system and method for the vehicle-mounted controller, various types of communication interfaces and equipment interfaces are integrated in the industrial personal computer, so that the rigid requirements for communication configuration in various test scenes are met. Furthermore, through division of a bus security test module, a system security test module and a communication security test module, each module can independently construct connection with a target vehicle-mounted controller based on a Vector tool chain, an ADB / SSH, a wireless network card and a Bluetooth adapter, parallel execution of multiple test cases is supported, and the test cases do not interfere with one another; and synchronous promotion of safety tests such as bus diagnosis, system login, wireless communication and the like is realized. The upper computer serves as a control core and can automatically drive any module or multiple modules to work cooperatively according to the target test case, and test instructions are issued in a unified mode through the VT test system, so that the test preparation time and the execution period are greatly shortened, and the effect of improving the penetration test efficiency of the vehicle-mounted controller is achieved.

The invention discloses a hypergravity field penetration simulation test device, which comprises an air cannon, a launching bullet, a support mechanism, a penetration test mechanism and an acceleration mechanism, and is characterized in that the launching bullet is arranged in the air cannon, the air cannon is arranged in the support mechanism, the bottom of the support mechanism is connected with the top of the penetration test mechanism, and the acceleration mechanism is arranged in the support mechanism; high-pressure gas is filled into the air cannon to push the launching bomb to realize launching; the acceleration mechanism comprises a centrifugal machine and a hanging basket, and the acceleration mechanism provides a super-gravity field environment for the penetration simulation test through rapid rotation; the launching bomb intrudes into the test device to simulate the penetration process. According to the hypergravity field penetration simulation test device provided by the invention, the hypergravity field is applied to the test model through the centrifugal machine, and the test model and a prototype are similar in power, motion and energy, so that a more real penetration process is simulated, various parameters can be conveniently changed, multiple repeated tests can be carried out, and the test efficiency is improved. The test efficiency and the refinement degree are greatly improved, the cost is remarkably reduced, and damage is reduced.

The invention belongs to the technical field of network security, and discloses a penetration test method based on knowledge driving. The CK framework constructs a knowledge graph ontology, forms a structured knowledge graph by defining technical and tactical nodes, technology nodes, vulnerability nodes, service nodes, asset nodes and association relationships thereof, and comprehensively shows attack chain logic. In combination with target host detection information, service, port and operating system data are dynamically mapped into a knowledge graph, a recommended attack path is generated through reasoning, and the path is adjusted by using a dynamic optimization method, so that the rationality and practical applicability of the attack path are ensured. According to the method, the vulnerability nodes and the technical nodes in the recommended attack path are combined, the vulnerability utilization method is generated according to the preset rule, and the parameter configuration is adjusted according to the operation environment of the target host, so that the accuracy and the flexibility of the utilization method are improved.

The invention discloses a full-automatic penetration testing method based on a large language model, and relates to the field of network security. The method comprises the following six core steps: 1) constructing a host asset model through multi-source data acquisition, integrating Shodan, Fofa and eagle map platform data, and performing LLM verification; 2) calling an NVD database to identify vulnerabilities, and secondarily verifying CVE validity by using LLM; 3) dynamically disassembling the penetration task based on a collaborative penetration decision tree (SPDT), and fusing ATTamp; guiding task planning by a CK technology and tactics map; (4) a multi-agent engine is adopted to execute a command, three interaction modes of Browser-use / CLI / GUI are supported, a Kali virtual machine is connected through an SSH to execute command line operation, and a graphical tool is driven based on a multi-mode model; according to the method, the problem that the automation degree of traditional penetration testing is low is solved, and the full-process automation of the penetration testing is achieved.