13 results about "Attack tree" patented technology

An exposure management system and an exposure management method for assessing exposure of assets of an organization, the assets comprising at least one host, such as a computer or a server. The method comprises creating a model of the organization controlling the assets, creating models of plurality of threat actors able to attack the assets of the organization, producing a reduced set of threat actors relevant for the organization based on the relevance of a specific threat actor to the organization in view of the created threat actor models and the created model of the organization. The method further comprises, for each threat actor of the reduced set of threat actors, determining available attack paths for the assets of the organization with an attack path simulator and combining the determined available attack paths for the assets of the organization to attack trees for a specific threat actor.

The invention discloses a vehicle safety assessment method and device based on an AI-assisted attack tree, equipment and a medium, and belongs to the technical field of computers. The method comprises the following steps: automatically generating an attack tree containing a dynamic risk level by adopting a hierarchical agent workflow based on a vehicle type architecture; extracting a threat scene from the attack tree and generating a structured description; matching a historical knowledge base by utilizing a retrieval enhancement generation technology, and generating an asset list and a threat score through a large language model; and synthesizing the attack tree and the scoring result to generate an evaluation conclusion containing security disposal suggestions. According to the technical scheme, the attack tree can be automatically generated based on the vehicle model architecture, intelligent scoring is linked, finally, the safety evaluation conclusion containing specific disposal suggestions is output, and the evaluation efficiency, accuracy and engineering practicability are remarkably improved.

The application discloses a kind of multi-round jailbreak defense methods and devices for Text-to-SQL system, it is related to network security technical field, the method includes: based on multi-round Text-to-SQL dataset Construction contains multi-class attack scene's jailbreak attack dataset, constructs and trains the attacker model based on pre-training language model, to generate multi-round natural language question with attack intention, establish multi-round jailbreak attack tree, to structured representation by attack target decomposition Multiple-step attack path, based on attack tree generation with semantic coherence multi-round attack question sequence, the sequence is input target Text-to-SQL system, execute the SQL query of its output, and according to evaluation index quantification attack effectiveness, the present application can systematize gradually progressive jailbreak attack in multi-round dialogue Simulation, realize the automatic testing and evaluation of Text-to-SQL model security, provide basis for system reinforcement and defense strategy formulation.

The present application relates to train wireless network communication technical field, specifically to a kind of train wireless network control system information security risk assessment method, including according to the object of risk assessment to establish attack tree model, the possibility of security event is assessed using triangular fuzzy number and occurs, attack path is analyzed, and the interval probability of each attack path is calculated, point probability is obtained according to attack path interval probability, the possibility of attack is calculated, the influence of security event is evaluated using fuzzy analytic hierarchy process, the possibility of security event and impact value are quantified, security event risk value is calculated, the risk level and security level of system are determined according to system risk assessment value, corresponding protection requirements are formulated.The present application effectively evaluates and manages the information security risk of train wireless network control system, provides basis for formulating effective security protection strategy, and improves the overall security capability of train wireless network.

This invention discloses an automated method for constructing vehicle-mounted attack trees based on knowledge graphs and large-scale model agents, relating to the field of vehicle-mounted information security technology. The invention includes the following steps: S1, constructing an ontology model of a vehicle-mounted attack domain knowledge graph, defining two types of entities: threat nodes and threat scenario nodes, causal relationship types, and entity attribute constraints; S2, collecting vehicle-mounted security threat data and constructing a vehicle-mounted attack knowledge graph with a directed acyclic graph structure; S3, acquiring electronic and electrical architecture asset data of the target vehicle system and extracting asset security attributes through an asset identification agent; S4, constructing an attack topology graph from system modeling data, enumerating all reachable attack paths from externally exposed nodes to the target asset. This invention, through the collaboration of large-scale model agents and vehicle-mounted attack knowledge graphs, achieves fully automated construction of vehicle-mounted attack trees, ensuring attack path integrity with quantifiable path coverage metrics, and improving construction efficiency and attack path integrity.

Embodiments of the present disclosure relates to a system (100) and method (300) for enhancing Threat and Risk Assessment (TARA) through the integration of Zero Trust Access Control (ZT-AC) principles. The system (100) utilizes a processor (104) to acquire and analyze system assets, define strict access policies based on Zero Trust, and categorize vulnerabilities by their relevance and impact. The system (100) maps threats to assets, incorporates ZT-AC policies to minimize exposure, and develops AI-powered attack tree models to simulate potential attack scenarios. The system (100) validates adherence to Zero Trust principles, simulates mitigating effects within attack trees, and scores risk based on likelihood, impact, and ZT-AC mitigations. Detailed cybersecurity risk assessments are generated, providing actionable recommendations for refining Zero Trust policies and improving overall security.

The application discloses an AI-assisted attack tree-based vehicle safety evaluation method, device, equipment and medium, and belongs to the technical field of computers. The method comprises the following steps: automatically generating an attack tree containing a dynamic risk level based on a vehicle architecture by adopting a hierarchical intelligent agent workflow; extracting a threat scenario from the attack tree and generating a structured description; matching a historical knowledge base by using a retrieval enhancement generation technology, and generating an asset list and a threat score by a large language model; and generating an evaluation conclusion containing a safety disposal suggestion by comprehensively combining the attack tree and the score result. The technical scheme can automatically generate an attack tree based on a vehicle architecture and link intelligent scoring, finally output a safety evaluation conclusion containing specific disposal suggestions, and significantly improve the evaluation efficiency, accuracy and engineering practicability.

The invention belongs to the technical field of dynamic defense, and particularly relates to a dynamic defense method and system fused with water conservancy network security situation awareness, so as to solve the technical problems that data isomerism is difficult to process, model static state lacks timeliness, threat prediction is incomplete, and defense decisions are not balanced in an existing attitude awareness method. The defense method comprises the following steps: constructing a multi-layer attack tree model of the water conservancy network; obtaining heterogeneous security data collected by monitoring nodes in a network, quantifying the heterogeneous security data into normalized threat values in one-to-one correspondence with leaf nodes in the model based on an asset vulnerability incidence matrix and a situation element fuzzy membership function, and taking the normalized threat values as conditional occurrence probabilities of the leaf nodes; and traversing all attack paths in the multi-layer attack tree model, and executing an operation on any path. According to the defense method, interference to normal operation of the water conservancy core business is reduced, and balance between safety and business is achieved.

A non-transitory computer-readable recording medium stores therein a risk calculation program that causes a computer to execute a process including, storing a weight for every attack condition, calculated with reference to a usage rate of each of the attack conditions, and a presence rate of each of a plurality of specification elements contained in a specification of the AI system, regarding the conditions for establishing the attack, the presence rate being defined in specifications of a plurality of existing AI systems, identifying an establishment status of the attack condition, with reference to information regarding the specification element extracted from information regarding the specification of an AI system subject to the risk determination, and calculating a risk score for every attack tree of the AI system subject to the risk determination, with reference to the weight for every attack condition, and the identified establishment status of the attack condition.

This invention discloses a large-scale model-based TARA risk quantification modeling method, relating to the field of vehicle-mounted information security technology. The invention includes the following steps: Step a: Using a system modeling agent, the vehicle system architecture document is parsed to extract components, communication channels, and data flows, establishing a structured asset model; Step b: Using an asset identification agent, the seven-dimensional security attributes of each asset are identified, completing standardized annotation of asset security attributes; Step c: Using a damage scenario agent, standardized damage scenarios are generated based on a damage knowledge graph, and a four-dimensional impact assessment is performed; Step d: Using a threat analysis agent, threat scenarios are generated based on damage scenarios and a threat knowledge graph, an attack tree is constructed, and compliance mapping is performed. This invention achieves fully automated risk quantification of the TARA process through multi-agent phased collaboration and scenario reasoning enhanced by dual knowledge graphs, eliminating human subjective bias and improving the consistency and completeness of assessment results.

The application discloses a kind of multi-round jailbreak attack evaluation method and device for Text-to-SQL system, it is related to network security technical field, the method includes: based on multi-round Text-to-SQL dataset Construction contains multi-class attack scene's jailbreak attack dataset, constructs and trains the attacker model based on pre-training language model, to generate multi-round natural language question with attack intention, establish multi-round jailbreak attack tree, to structured representation by attack target decomposition Multiple-step attack path, based on attack tree generation with semantic coherence multi-round attack question sequence, the sequence is input target Text-to-SQL system, execute the SQL query of its output, and according to evaluation index quantification attack effectiveness, the present application can systematize gradually progressive jailbreak attack in multi-round dialogue, realize the automatic testing and evaluation of Text-to-SQL model security, provide basis for system reinforcement and defense strategy formulation.

The embodiment of the invention provides a risk assessment method, system and device for in-vehicle wireless communication and a medium, and belongs to the technical field of vehicle safety. The method comprises the following steps: acquiring functional module information of a vehicle; according to the function module information, each function module configured by the vehicle is divided into a first type of modules and a second type of modules, damage scenes of the first type of modules and the second type of modules are determined according to the first preset dimension, the first type of modules apply the star flash communication technology, and the second type of modules do not apply the star flash communication technology; constructing a forward attack tree and a reverse attack tree based on the damage scene, and aggregating the forward attack tree and the reverse attack tree according to the first type of modules and the second type of modules to obtain a target attack tree; and calculating a feasibility score for the target attack tree according to a second preset dimension, and determining a risk assessment result of the vehicle according to the feasibility score and the damage scene. The embodiment of the invention aims to improve the wireless communication security of the vehicle.

The invention relates to a risk calculation program, a risk calculation method, and a risk calculation device. In the present invention, a computer executes: a process for storing a weight for each attack condition calculated on the basis of a usage rate for each of the attack conditions, which is an element for establishing a prescribed attack on an AI system, and a presence rate; the presence rate is the presence rate under a plurality of existing AI system specifications for each of a plurality of specification elements which are elements included in the AI system specifications and which are related to conditions for the establishment of the attack; accepting specification information of the AI system of the risk determination object; for each pre-created attack tree, on the basis of the specification element information extracted from the specification information of the AI system of the risk determination object, determining the establishment state of the attack condition; a risk value for each of the attack trees of the AI system to be subjected to risk determination is calculated on the basis of the weight for each of the attack conditions and the established condition of the attack condition.