177 results about "Defence system" patented technology

The invention belongs to the technical field of defense of new energy centralized control station networks, and particularly relates to a dynamic defense system and method of a new energy centralized control station network based on dynamic IP. By monitoring and statistics of the abnormal traffic, the present invention can determine the occurrence frequency and the occurrence interval of the abnormal traffic, further analyze and determine the defense state of a new energy centralized control station based on this, and execute the corresponding dynamic optimization solution according to different defense states, so that the IP address and the firewall of the new energy centralized control station can be dynamically adjusted to ensure the security performance of the new energy centralized control station, reduce the risk of external intrusion, effectively resist malicious network reconnaissance such as scanning attacks and ensure the stable operation of a new energy power generation system.

This disclosure describes utilizing an attack defense system to improve the defense robustness of a targeted large generative model (LGM) by generating a set of variant prompt injection attacks that are successful against the targeted LGM, where the set of variants is based on a prompt injection attack (e.g., jailbreak) against the targeted LGM or another LGM. For example, the attack defense system utilizes a two-phase framework to generate variant prompt injection attacks and evaluate their attack effectiveness against a targeted LGM. The attack defense system achieves improved variant prompt injection attacks by repeating the two-phase framework and gaining insights from the effectiveness scores of previously generated variants. In addition to generating enhanced variants, the attack defense system generates diverse variants to safeguard the targeted LGM against a broader range of prompt injection attacks that employ more creative and complex styles.

The invention relates to the technical field of AI agent defense, and particularly discloses an AI agent autonomous defense system and method for network attack path prediction. An attack chain reasoning module; a risk assessment module; a path prediction module; a resource scheduling module; a defense execution module; through cooperation of a dynamic environment sensing module, an attack chain reasoning module, a risk assessment module, a path prediction module, a resource scheduling module and a defense execution module, automation and intelligentization of the whole process from environment sensing, attack analysis to defense execution are realized, manual intervention is reduced, human errors are reduced, and the defense efficiency is improved. And the attack reasoning module is used for reasoning a multi-stage attack path, so that the limitation of single-stage attack analysis is broken through, an attack logic chain of an attacker from reconnaissance and penetration to transverse movement is completely presented, the attack life cycle is comprehensively covered, and the timeliness, comprehensiveness and security of network security defense are improved.

Provided are an active defense system and method for an unknown threat. The system includes an intelligent threat early-warning module (10), an unknown threat detection module (20) and a self-adaption defense processing module (30). The intelligent threat early-warning module (10) is configured to perform threat prediction on a power grid situation data set collected from a power information network in real time to obtain threat early-warning information and send the information to the unknown threat detection module (20). The unknown threat detection module (20) is configured to perform threat detection and analysis on collected unknown threat network data when receiving the threat early-warning information to generate a threat analysis report and send the report to the self-adaption defense processing module (30). The self-adaption defense processing module (30) is configured to trigger a defense processing operation corresponding to a preset threat defense strategy according to the threat analysis report.

The invention discloses a network security threat detection and defense system based on artificial intelligence, and relates to the technical field of network security, and the system comprises a first detection module which is used for collecting a plurality of network data sources in real time and generating a standardized real-time feature vector, the second detection module periodically collects various historical data sources and generates baseline feature vectors capable of representing normal network behaviors. According to the detection system, similarity comparison is carried out on the real-time feature vector and the baseline feature vector, so that whether obvious deviation exists or not is quickly judged, and network threat detection of a known type is realized; after high-dimensional features are extracted from the real-time feature vector and the baseline feature vector respectively, similarity comparison is carried out on output high-dimensional vectors again, and detection of unknown types of network threats is achieved; a defense system correspondingly executes limiting or blocking operation according to a detected judgment result of a known type of threat and an unknown type of threat.

The invention provides a defense system for complex cooperative attacks in a power distribution network, and relates to the technical field of power system network security. The defense system comprises a data behavior analysis unit, an attack identification early warning unit and a system response and report unit; the data behavior analysis unit comprises a data acquisition preprocessing module, a feature extraction module, a behavior baseline establishment module, an anomaly detection module and an attack source positioning module; the data acquisition preprocessing module is in one-way connection with the feature extraction module; the feature extraction module is in one-way connection with the behavior baseline establishment module; the behavior baseline establishment module is in one-way connection with the anomaly detection module; the anomaly detection module is in one-way connection with the attack source positioning module; according to the invention, real-time data of key nodes are collected through the data acquisition and preprocessing module, and deep analysis is carried out by using the feature extraction module, the behavior baseline establishment module and the anomaly detection module, so that multiple sections of abnormal behaviors and potential threats in the power distribution system can be accurately identified.

The invention discloses a DNS attack detection and defense system based on an adversarial network, and belongs to the field of DNS. The system is composed of a data acquisition and feature engineering module, a confrontation sample generation module, a multi-modal detection engine module and a dynamic defense and feedback system. Firstly, a data acquisition and feature engineering module captures and analyzes DNS traffic in real time, extracts multi-dimensional behavior features, generates structured feature data, divides the structured feature data into normal samples and attack samples, inputs the normal samples and the attack samples into an adversarial sample generation module, and constructs an adversarial attack sample with strong camouflage by introducing a Wasserstein distance optimized generative adversarial network; and then, inputting the normal sample and the adversarial attack sample into a multi-modal detection engine module in a mixed manner, performing fusion analysis, and outputting an attack probability and a classification result. Finally, a classification result is transmitted to a dynamic defense and feedback system, various response strategies are automatically executed, meanwhile, the classification result is fed back to a storage updating system, and a closed-loop self-adaptive defense mechanism is formed. According to the invention, the accuracy and efficiency of DNS attack detection are improved.

The invention discloses a power transmission line insulator dual defense system and method, and belongs to the technical field of power transmission line maintenance, in the system, a power supply subsystem generates, stores and conveys high-pressure gas, and a primary defense subsystem and a secondary defense subsystem are respectively communicated with the power supply subsystem through high-pressure conveying hoses to obtain a high-pressure gas source; the first defense subsystem is arranged around an insulator of the power transmission line and forms an annular air curtain to prevent rain and snow from being attached to the surface of the insulator; the secondary defense subsystem detects and removes rain and snow breaking through the annular air curtain; the photovoltaic power supply subsystem is used for providing power for the whole defense system; all the subsystems are electrically connected with the intelligent control subsystem, and the intelligent control subsystem is used for coordinating starting, operation and parameter adjustment of all the subsystems. The problems that an existing anti-icing technology is high in energy consumption and slow in response, an icing micromechanism is difficult to capture, and protection is insufficient due to the fact that extreme working conditions are difficult to reproduce are solved, and efficient blocking and removing of rain and snow are achieved.

The invention discloses a dynamic attack surface transformation active defense system for a production system, and relates to the field of network security, the active defense system comprises a system support module, an interface interpreter module, a moving target construction module, a dynamic arrangement module, a management module, an information processing module and a threat monitoring module; the system support module provides network service, file system and user account camouflage functions and converts the functions into standard interfaces through the interface interpreter module to be called by the upper layer module, and the moving target construction module interacts with the system support module through an interface interpreter and supports dynamic arrangement of camouflage resources. The dynamic arrangement module adjusts attack surface features of system resources according to real-time threat information provided by the threat monitoring module, when the threat monitoring module finds potential attacks, threat information is transmitted to the management module, the dynamic arrangement module is triggered to carry out adjustment, and the information processing module records logs and displays system states and threat alarms. And administrators are assisted to make decisions.

The invention provides a domain self-adaption-based generative attack and defense confrontation method. Directional interference on a black-box intelligent system is realized by constructing a generative confrontation network framework. The method takes a domain self-adaption theory in transfer learning as a core, builds a dynamic mapping relation between a white box model group and a black box target system, designs a dual-channel generative adversarial network, jointly supervises sample authenticity and cross-model transfer consistency through cross entropy loss and KL divergence constraint, builds a dynamic boundary feedback system, and achieves the dynamic boundary feedback. And a disturbance generation strategy of the generator is adjusted according to the real-time attack effect, and a self-adaptive attack and defense game closed loop is formed. Compared with a traditional black box attack method, the method has the advantages that migration robustness of an adversarial sample in a model heterogeneous scene is remarkably improved, stable attack efficiency can still be maintained when a defense system has protection mechanisms such as adversarial training and input reconstruction, and the method can be applied to the fields such as penetration testing of an intelligent security system and the like. And a technical support is provided for vulnerability evaluation of the intelligent system in a complex environment.

The invention relates to the field of microorganisms, and discloses a complex microbial inoculant for preventing and treating clubroot of rape. Comprising the following raw materials in parts by weight: 10 to 12 parts of acid-resistant bacillus, 12 to 15 parts of pseudomonas fluorescens PF-5, 8 to 10 parts of bacillus mucilaginosus, 5 to 7 parts of halophilic bacillus, 8 to 10 parts of bacillus amyloliquefaciens, 12 to 15 parts of trichoderma atroviride ReTv2, 6 to 8 parts of azotobacter chroococcum, 10 to 12 parts of chitosan-gamma-polyglutamic acid microspheres, 12 to 15 parts of diatomite and 8 to 12 parts of humic acid. And 2-4 parts of algal polysaccharides. The living space of plasmodiophora brassicae is preempted by the acid-resistant bacillus; the pseudomonas fluorescens PF-5 directly strikes pathogenic bacteria and stimulates the defense system of the oilseed rape; the bacillus amyloliquefaciens releases a plurality of antibacterial substances; the trichoderma atroviride ReTv2 rapidly destroys the structure of the plasmodiophora brassicae in a manner of hyperparasitism and exoenzyme secretion.

A model-based artificial intelligence (Al) system and method that applies trained decision- making on extracted features for target classification, including at least one sensor for detecting a point target, an interactive multiple model (IMM) filter for extracting features related to motion kinematics of the point target, and a classifier for applying trained decision-making on the extracted features to determine classification of the point target

The invention relates to the technical field of network security, and particularly discloses a heterogeneous network security defense system and method based on artificial intelligence, and the system comprises a full-dimensional perception layer which is used for obtaining original data of heterogeneous network security through a hardware-level data collection and protocol self-adaption technology; the cognitive enhancement layer is used for undertaking the output of the full-dimensional perception layer and optimizing the threat identification precision through four-dimensional spatial-temporal feature fusion and antagonism feature purification; the intelligent decision-making layer is used for accurately depicting a complex attack mode through hypergraph modeling and causal reasoning based on output of the cognitive enhancement layer; according to the method, through integration of four-dimensional spatial-temporal feature fusion and antagonistic feature purification technologies, the identification precision of complex and variable threats in a heterogeneous network is remarkably improved, especially for unknown or variable attack means; by means of hypergraph modeling and causal reasoning technologies, a complex attack mode is accurately described, an optimal defense action sequence is automatically generated, and conversion from passive defense to active defense is achieved.

The invention discloses a bacteriophage defense system as well as a construction method and application of the bacteriophage defense system in anti-bacteriophage engineering bacteria. The bacteriophage defense system is derived from Escherichia coli Nissle 1917, and comprises proteins as shown in SEQ ID NO.1, SEQ ID NO.2 and SEQ ID NO.3, and derived proteins of the proteins as shown in SEQ ID NO.1, SEQ ID NO.2 and SEQ ID NO.3. According to the invention, a Type II restriction-modification system with strong bacteriophage defense capability is identified from Escherichia coli Nissle 1917, and the Type II restriction-modification system is transplanted into strains such as Escherichia coli W3110, MG1655 (DE3), BL21 (DE3) and the like, so that the strains are endowed with superstrong bacteriophage resistance. The system is successfully applied to industrial engineering bacteria for producing ectoine, fine glufosinate-ammonium, ergothioneine and other products, and the bacteriophage defense capability of the industrial engineering bacteria is effectively enhanced on the basis that the production capacity of the industrial engineering bacteria is not seriously influenced.

The invention relates to the technical field of network security, and provides a defense system security assessment method and system based on a time sequence attention mechanism, and the method comprises the steps: obtaining multi-dimensional time sequence data of network operation for a to-be-assessed network; for the multi-dimensional time sequence data, extracting short-term mode features of the network system in the defense process to obtain extracted features; carrying out weighting processing on the extracted features by adopting a time sequence self-attention mechanism; acquiring attack situation information, and performing embedding processing to obtain context information; and fusing the time sequence features processed by the time sequence self-attention mechanism with the context information to generate uniform feature representation, and further generating a defense effect score as an evaluation result. Based on a time sequence self-attention mechanism, in combination with deep learning technologies such as context embedding and convolution feature extraction, the real-time effect of a defense system can be accurately evaluated on the basis of different attack situations and time sequence features, and quantifiable scores are generated.

The invention relates to the technical field of firewalls, in particular to an IPS escape attack defense capability test method and system, and the method comprises the steps: collecting a service scene for a to-be-tested defense system, generating corresponding background traffic according to the service scene, and carrying out the screening to obtain an attack data set; obtaining a test demand, adjusting the attack data set according to the test demand to obtain test traffic, and mixing the background traffic and the test traffic to obtain mixed traffic; and testing the defense system to be tested based on the mixed flow. In order to solve the problem of poor IPS escape attack defense capability evaluation effect in the prior art, a process of collecting a service scene of a defense system to be tested is introduced before a test is started, and background traffic obtained by simulating a plurality of different data sources is formed by combining an actual service scene; and vulnerabilities easily occurring in the defense system to be tested are selected and an attack data set is constructed, and on this basis, construction and testing of the mixed traffic are performed, so that a test result is more in line with an actual situation.

The invention relates to an unmanned cluster networking equipment resource allocation method and device, and a storage medium. The method comprises the following steps: based on a many-to-many confrontation task scene between an unmanned cluster networking and an enemy defense system, carrying out the resource allocation of the unmanned cluster networking equipment; constructing an unmanned cluster task revenue function, a cost loss function, a confrontation cost function and an enemy defense system damage function; comprehensively considering performance differences and cost benefits of various weapon systems, and constructing an unmanned cluster networking equipment resource allocation model by adjusting weight coefficients and introducing additional items of specific task requirements; and solving the unmanned cluster networking equipment resource allocation model by adopting a differential genetic algorithm to obtain an optimal weapon resource allocation strategy. Compared with the prior art, various complex factors are comprehensively considered, the optimal or nearly optimal equipment resource allocation scheme can be quickly generated, and the overall combat effectiveness and task success rate of the unmanned cluster are remarkably improved.

The invention provides an attack technology and tactical identification method oriented to honey array linkage. The method comprises the following steps: coding according to malicious sample data and associated TTP labels, and constructing a training data set; constructing an interpretable discrimination model for binary classification modeling to obtain a behavior triggering weight vector and an offset item of the malicious sample; extracting dominant trigger rules to construct a mapping inference rule base; obtaining alarm data, extracting a behavior clue based on a preset standard behavior dimension set, converting the behavior clue to obtain a trigger identifier, and mapping the alarm data into an alarm behavior vector; constructing a prediction TTP label set of the alarm behavior vector and recording a trigger path; and encapsulating the behavior semantic data, constructing an execution strategy response rule set, performing mapping in the execution strategy response rule set according to the behavior semantic data to obtain an execution strategy, and adjusting honey array deployment according to the execution strategy. By applying the method, accurate identification and transparent reasoning of the potential TTP in the attack activity can be constructed and realized, and linkage scheduling of a defense system is supported.

The invention discloses a network attack traceability and defense system based on rule reasoning and multi-agent collaboration, which comprises a multi-source data acquisition module in modular design, a quick decision-making module based on rule reasoning, a multi-agent collaboration decision-making module and an attack chain visualization module, and key tasks such as data acquisition, threat identification, path analysis and visual display are cooperatively completed. Two innovation modules of rule reasoning and multi-agent cooperation are introduced, so that the overall intelligent level of the network attack tracing and defending system is improved, the response speed and judgment accuracy of the network attack tracing and defending system in a variable threat environment and the dynamic adaptation capability of a security policy are enhanced, and the network attack tracing and defending system has important application and popularization values.

Systems, frameworks, and methods for reinforcement learning (RL)-based real-time path planning for unmanned aerial vehicles (UAVs) are provided. The learning capabilities of federated learning (FL) can be integrated with an improved deep RL framework, including using a significant reply memory buffer (SRMB) to accelerate the intelligent behavior. The framework can train a UAV to intelligently dodge static and dynamic defense systems and achieve assigned goals (e.g., in a hostile area). The FL can enable collaborative learning through a swarm of UAV agents.

The invention relates to the field of intelligent monitoring, and discloses a machine room wild animal intelligent defense system based on a millimeter wave radar, which comprises a signal capture module, a signal processing module, a feature extraction module, a classification tracking module and an alarm linkage module, the signal capturing module collects target reflection signals through a millimeter wave radar, the signal processing module carries out multi-target separation and interference suppression on the signals, the feature extraction module extracts time-frequency characteristics and motion track characteristics of targets, and the classification tracking module classifies the targets and tracks the motion states of the targets in real time. And the alarm linkage module evaluates the threat level according to the classification result and triggers corresponding defense measures. The system can accurately identify and track targets, timely respond and execute defense actions, has the advantages of high precision, multi-target monitoring, real-time dynamic tracking, intelligent defense and the like, and can effectively guarantee safe operation of machine room equipment.

The invention discloses an anti-bacteriophage escherichia coli chassis cell based on combination of bacteriophage adaptive evolution and a bacterial anti-bacteriophage defense system. Receptor genes of bacteriophages are excavated through co-evolution of bacteriophages T1, T4, T7, vBEcoMP251 and vBEcoMP255 and escherichia coli strains, the resistance of escherichia coli to the bacteriophages is enhanced by knocking out screened bacteriophage receptors on the genome level, and meanwhile, defense systems shedu and septu with the ability to resist the bacteriophages are obtained through screening. The resistance of escherichia coli to the bacteriophage is further enhanced by integrating the gene to the ylbe and yeeP gene loci, construction of multi-resistant broad-spectrum chassis cells of the bacteriophage is completed, and certain application prospects are achieved.

A cell phone case to hold a concealed defense system, comprising, a concealed defense system, a cell phone case, and an attachable means to removably attach the concealed defense system to the back of the cell phone case to form a flat foot that stabilizes and directs the defense system toward the danger or a target. The concealed defense system can be a flat mini pepper spray canister or a flat defense projectile device.

The present invention discloses a backdoor attack defense method and defense system based on security training, which belongs to the field of neural network security technology, and can significantly reduce the success rate of backdoor attacks and maintain the accuracy of the model on normal samples and the robust accuracy on poisoned samples. The method comprises: step 1, obtaining a backdoor poisoned data set; step 2, filtering out poisoned samples in the backdoor poisoned data set using a filtering index to obtain a training sample set; step 3, training a neural network model using the training sample set; step 4, updating the filtering index according to the trained neural network model; step 5, repeating steps 2 to 4 for a preset number of times. The present invention is used for backdoor attack defense.

The invention relates to a dynamic defense system and method for durability degradation of a reinforced concrete structure. The system comprises a composite fabric network pre-buried in the reinforced concrete structure, a sensing unit for monitoring the health state of the structure and a response control unit, the composite fabric network comprises conductive row fiber bundles and conductive column fiber bundles which are intercrossed and woven into a two-dimensional matrix skeleton and can be addressed independently; arranging a plurality of repairing microcapsules which adopt a specific sensitive polymer wall material to package a chemical repairing agent at cross nodes, and additionally arranging function execution assemblies at corresponding nodes according to requirements; and the response control unit applies an excitation signal exceeding a preset threshold value to the target node, and generates or triggers the function execution component to form a corresponding external excitation energy field to act on the sensitive polymer wall material of the repairing microcapsule, so that the sensitive polymer wall material is broken or subjected to phase change to release the chemical repairing agent. According to the method, the threat position, the threat type and the threat level can be accurately positioned, and the repair range and strength are dynamically adjusted.

The invention provides an application of schizophyllan as a human beta-defensin-2 accelerant. According to the application, the effect of inducing human beta-defensin-2 (hBD-2) expression of the schizophyllan is found for the first time, and the schizophyllan can effectively stimulate an antimicrobial defense system on the surface of a human body to restore the stability of a natural internal environment, so that the schizophyllan is used for improving natural defense on the surface of the human body or improving microbiome balance on the surface of the human body; the active effects on prevention and / or reduction of skin infection, skin sensitivity, skin barrier damage and skin microecological disorder, prevention and improvement of pox muscles and the like are achieved, and the application field of the substance is widened.

The invention discloses an attack detection method and system for gateway equipment. According to the heterogeneous cooperative defense system based on protocol context awareness provided by the invention, a'kernel mode instruction recombination-user mode adversarial learning 'double-engine architecture is innovatively constructed, and low-delay extraction of attack features and active evolution of a protection strategy are realized. Specifically, the invention provides a migration type adversarial evaluation model, which breaks through the dependence of a traditional detection scheme on protocol grammar, dynamically constructs a cross-protocol attack sample through a generative adversarial network, captures traffic time sequence anomaly in combination with a bidirectional LSTM, and constructs a dynamic graph containing threat propagation probability and resource occupation trend. Therefore, feature migration and generalization modeling can be carried out on unknown or variant attack behaviors, the recognition capability on zero-day attacks and cross-protocol attacks is enhanced, and finally the detection accuracy is improved.

The invention relates to the technical field of network security, in particular to an IPv6 network DDoS attack detection and defense system based on deep learning, which comprises a flow monitoring and data acquisition module, a preprocessing and feature extraction module, a deep learning attack detection module, an attack response and decision module and a defense execution and feedback module, the method comprises the following specific steps: S1, monitoring traffic in an IPv6 network in real time through a traffic monitoring and data acquisition module, and capturing traffic characteristics; s2, cleaning and preprocessing the collected original traffic data by a preprocessing and feature extraction module, and extracting key features related to the DDoS attack; according to the IPv6 network DDoS attack detection and defense system based on deep learning, abnormal traffic can be rapidly identified through the deep learning model in the system, timely response can be ensured when a DDoS attack occurs, real-time performance is ensured, a defense strategy can be automatically adjusted through the defense execution and feedback module, manual intervention is not needed, and the intelligent degree of defense is improved.

The invention discloses a transformer active defense system testing device and method based on high-speed partial discharge data playback, and the device comprises a high-speed partial discharge data storage unit which stores the original data of partial discharge; the high-speed network interface unit transmits the partial discharge data in the selected network message format; the FPGA data processing and playback unit analyzes the network message, extracts an original partial discharge data point, and converts the original partial discharge data point into a high-speed digital signal according to precise time sequence control; the high-speed digital-to-analog converter converts the high-speed digital signal into an analog signal; the signal conditioning and interface unit is used for conditioning the analog signal into an analog partial discharge signal meeting the input requirement of a partial discharge acquisition unit of the active defense system to be tested; and the control and configuration unit monitors and records the playback process state and the protection action response of the active defense system to be tested. According to the invention, various performance indexes of the active defense system can be verified safely, efficiently and truly.

Genetically modified phages are provided. Accordingly, there is provided a genetically modified phage comprising a polynucleotide encoding an anti-defense system polypeptide. Also provided are methods of producing and using same.