41 results about "Grey box" patented technology

The invention belongs to the technical field of motors, and discloses a static state offline parameter identification method and device for a permanent magnet synchronous motor, and the method comprises the steps: obtaining nameplate information of a to-be-identified motor; inputting nameplate information of the to-be-identified motor into a grey box model used for predicting off-line parameters of the motor to obtain preset off-line parameters of the to-be-identified motor, and taking the preset off-line parameters as a static state off-line parameter identification result of the to-be-identified motor; wherein the grey box model used for predicting the off-line parameters of the motor is a large language model into which physical principle constraints of the permanent magnet synchronous motor are introduced; according to the method, the nameplate information of the to-be-identified motor is acquired and input into the grey box model used for predicting the off-line parameters of the motor, so that the preset off-line parameters of the motor can be quickly acquired, the off-line parameter identification of the motor in the static state is realized, the parameter identification process is greatly simplified, and the identification efficiency and precision of the off-line parameters are improved.

The invention relates to a protocol fuzz testing method and system based on a long-life-cycle enumeration variable state. The method comprises the following steps: S1, a static enumeration declaration statement instrumentation analysis stage; s2, a short life cycle enumeration variable dynamic analysis stage; s3, a static assignment statement instrumentation analysis stage; and S4, a fuzzy test stage. The system comprises a static analysis module, a dynamic analysis module, a network IO module and a variation feedback module. According to the method, the source code of the protocol entity program to be tested is statically analyzed to capture distribution and use of enumeration type variables in the source code. State transition is represented by monitoring changes of enumeration variable value domains, and a corresponding state tree structure is constructed, so that stateful grey box fuzz testing is carried out on any protocol entity program to be tested under the condition that extra manual analysis is not needed.

The invention provides a temperature alarm method and device for a bus duct, a terminal and a storage medium. The method comprises the following steps: constructing a white box model for representing a corresponding relation between bus current and bus temperature rise according to an equipment mechanism modeling mode and a thermodynamic principle; constructing an initial grey box model between the bus current and the bus temperature rise based on the white box model, and training the initial grey box model based on a machine learning algorithm to obtain a trained target grey box model; predicting a dynamic temperature rise threshold value and a predicted temperature value corresponding to the current current value by using the target grey box model; the current current value is a current value corresponding to a currently monitored actual temperature value of the bus duct, comparing a difference value between the actual temperature value and the predicted temperature value with a dynamic temperature rise threshold to obtain a comparison result, and determining whether to trigger an alarm based on the comparison result. The dynamic threshold value is generated through model prediction, and the problem that the abnormal temperature rise of the bus cannot be accurately fed back due to manual configuration of the static threshold value is solved.

The invention provides a grid-connected inverter grey box impedance identification method and device suitable for multiple working points. The method comprises the following steps: determining a sequence impedance analytical expression of a grid-connected inverter considering frequency coupling characteristics based on a control scheme containing double-loop control; extracting a first nonlinear function relation and a second nonlinear function relation; respectively replacing the first nonlinear function relationship and the second nonlinear function relationship with a feedforward neural network, and constructing a first neural network and a second neural network; injecting voltage disturbance to the grid-connected point to obtain a first data set and a second data set; and training the first neural network and the second neural network based on the first data set and the second data set to obtain a grid-connected inverter grey box impedance model adapted to multiple working points, and calculating a positive sequence impedance amplitude and a phase angle of the grid-connected inverter considering frequency coupling characteristics. The accuracy of neural network model training can be improved under the condition of limited data volume, and accurate identification of the impedance of the grid-connected inverter under multiple working points is realized.

The invention discloses a new energy equipment sequence impedance multi-level frequency domain identification method, and relates to the field of power electronic equipment parameter and model frequency domain identification. Complex vector fitting is carried out on frequency domain response data of impedance measurement, an analytical model or an equivalent zero-pole model of power electronic equipment is obtained, and a basis is provided for grid-connected resonance stability analysis and impedance remodeling; comprising the following steps: S1, carrying out sequence impedance frequency scanning of single harmonic disturbance injection to obtain impedance frequency domain response data; s2, performing complex vector fitting to obtain a pole-residue type port frequency domain model; s3, evaluating'grey / black box 'characteristics according to the transparency of the control architecture information; s4, deducing a theoretical sequence impedance model of the grey box equipment, and identifying parameters based on a pole-residue expression; deducing an equivalent zero pole model for the black box equipment or the new energy cluster; compared with traditional qualitative analysis, accurate quantitative analysis and impedance remodeling are achieved, and analysis accuracy and engineering applicability are improved.

The invention relates to the technical field of software testing, and particularly discloses an automatic inspection platform based on a grey box test, comprising a detection console module used for managing and controlling a test process; the grey box security test overall module is used for enabling the data flow generated by the test to penetrate through the network; the grey box security test network module is used for taking a rear-end server as a computing resource and a storage resource, and packaging the rear-end server into a service by a certain management server for external provision; the grey box security test function module is used for detecting newly generated vulnerabilities in the software development process in real time; and the grey box security test tool module is used for detecting security problems in the software. By using the instrumentation technology and the flow agent, real-time vulnerability detection is carried out on the running application and API in the research and development test stage, full life cycle management and multi-dimensional application security management and control of vulnerabilities detected by an application system are supported, and the method has the advantages of high coverage, low false alarm, real-time detection and the like, and can be seamlessly fused with a DevSecOps process.

The invention relates to the technical field of program testing, in particular to a seed scheduling method and system for multi-objective optimization in grey box fuzzy testing, and the method comprises the steps: dividing related indexes of seeds into state related indexes and state independent indexes; modeling a priority ranking problem of the seeds into a multi-objective optimization problem according to state related indexes, calculating Pareto leading edges of the seeds based on a non-dominated ranking algorithm, and screening out the seeds with high priorities; selecting seeds according to a seed selection basic principle; determining the weight of each index by using an analytic hierarchy process according to the state-independent indexes, evaluating the quality of the seeds through an approximate ideal solution algorithm, and distributing reasonable energy for the seeds; and based on the priority and the energy distribution result, selecting the seeds for mutation to generate a new test case. According to the method, seed priority division and energy distribution are optimized, so that the seed scheduling efficiency in grey-box fuzzy testing is remarkably improved, and accurate testing is realized at a higher code coverage rate and a faster vulnerability discovery speed.

The invention discloses a some / ip protocol grey box fuzzy test method and system, and the method comprises the steps: calculating a generation probability for each element of each seed sequence in an obtained protocol data packet, calculating a copy probability according to the seed sequence and a corresponding sensitive element sequence, and taking the condition probability of maximizing the sensitive element sequence as a target function, completing the extraction of sensitive elements; taking the ID as an index, and constructing a corpus by the sensitive elements, the relative offset, the weights and the response state codes; and selecting a corresponding corpus from the corpus for a protocol data packet to be mutated according to a protocol state, performing replacement variation by using the sensitive element corresponding to the maximum weight and the relative offset, and completing the fuzzy test by monitoring the execution condition of the test case. The variation strategy is optimized by identifying the sensitive structure of the protocol data packet, the seed generation quality is improved through the structure-sensitive variation strategy, and the fuzzy test efficiency and the vulnerability detection capability are improved.

The invention discloses an intelligent automobile fault identification method based on a minimum grey box, and the method comprises the steps: obtaining intelligent automobile fault injection data with fault classification labels, constructing a Bayesian network through employing a structure learning method, forming a directed acyclic graph which reflects a variable causal dependence relation, mapping the directed acyclic graph into a fault tree model, and carrying out the recognition of the fault tree model. The method comprises the following steps: establishing a corresponding structure of a fault event and a logic relationship, determining a minimum cut subset through a fault tree model, screening in combination with a Markov blanket, extracting a minimum and sufficient key variable set, forming a minimum grey box, performing parameter learning by taking a directed acyclic graph as structural input and in combination with fault injection data, and constructing a dynamic Bayesian network. And inputting the minimum grey box variable set into the network to realize the detection of the fault state of the intelligent automobile and the judgment of the fault type. According to the method, through minimum grey box construction and dynamic Bayesian network reasoning, the problems of large data redundancy, strong black box performance and insufficient causal interpretation in the prior art are solved.

The invention discloses an array antenna pattern simulation method based on a U-Net network and a grey box model, and the method comprises the implementation steps: constructing a network based on a U-Net architecture, taking an ideal array pattern without considering the coupling influence between antenna units as the input, taking a real array pattern considering the coupling influence between the antenna units as the output, and carrying out the simulation of the array antenna pattern based on the U-Net network and the grey box model. And mapping from an ideal array directional diagram to a real array directional diagram is realized. Through fusion of a numerical method and deep learning, the defects that a traditional numerical method is low in simulation efficiency and a black box model based on a deep learning method is insufficient in simulation precision are overcome. According to the method, high precision can be kept in simulation of array antenna directional diagrams of different structural sizes, the simulation speed of the array directional diagrams is remarkably increased, the simulation memory requirement is reduced, the design period of the array antennas is greatly shortened, and the method can better meet the requirement for efficient iterative optimization in engineering application.

The present invention relates to the field of machine learning, and specifically to a method and device for copyright protection of large language models based on fingerprint member probability offset signals. A fingerprint data set is constructed, and the fingerprint data set is symmetrically divided into a training subset and a reference subset; the original model is fine-tuned and trained using the reference subset to obtain a reference model; according to the training subset, a semantically equivalent perturbation sample set is generated; according to the semantically equivalent perturbation sample set, the probability offset indexes of the model to be verified and the reference model are calculated respectively; according to the probability offset indexes of the model to be verified and the reference model respectively, the average signal strength index of the fingerprint data set is determined; according to the average signal strength index, it is judged whether the model to be verified constitutes infringement. The present invention can realize covert, efficient, accurate and highly robust model copyright verification in gray-box or black-box scenarios after the leakage of large language models.

In exemplary embodiments, methods and systems are provided that include one or more vehicle sensors and a vehicle processor. The one or more sensors are configured to acquire sensor data about the vehicle's operation. The processor is coupled to the one or more sensors and is configured to enable at least the input of the sensor data into a grey-box model, including a neural network model for the forces acting on the vehicle and a physics-based model for the vehicle's body movements; the determination of a vehicle trajectory using the grey-box model; the determination of a control action for the vehicle based on the grey-box model using the sensor data; and the execution of the control action for the vehicle in accordance with the instructions provided by the processor.

The invention discloses a dynamic grey box modal analysis method, device and equipment for a following grid type inverter, a medium and a product, and relates to the field of dynamic stability of a power system. The method comprises the following steps: based on an admittance splitting theory, splitting the dynamics of the following grid type inverter, then determining the overall admittance when the following grid type inverter is aligned to a global coordinate system, further determining a disturbance quantity, and determining an overall participation factor based on the disturbance quantity; performing normalization conversion on the overall participation factors based on participation comparison, and then positioning a leading controller; calculating an explicit parameter participation factor based on the dominant controller and the overall admittance, and determining the parameter sensitivity; parameter sensitivity is used to improve the stability of the power system. The method aims to reasonably analyze the influence degree of different controllers in the following grid type inverter on the stability so as to position the key control loop and the key control parameters influencing the power system, so that the control parameters of the following grid type inverter are effectively set and optimized, and the system stability is improved.

The invention discloses a vulnerability feature-guided directional grey box fuzzy testing method and system. The method comprises the following steps of: extracting dangerous code blocks from input source codes of a tested program based on given vulnerability features by adopting a code similarity detection tool; extracting a function call graph, a control flow graph and data flow dependence from the input source code of the tested program by adopting a graph extractor by taking a dangerous code block as a target, and generating a combined graph from the function call graph, the control flow graph and the data flow dependence; carrying out distance calculation on the dangerous code blocks through a specified distance algorithm so as to find out related code blocks in the merged image; and performing selective instrumentation and fuzzy testing on the input source code of the tested program based on the obtained dangerous code block and the related code block. According to the method, the target site guiding advantage of the directional grey box fuzzy test is exerted, and meanwhile, the overhead of path exploration is remarkably reduced, so that more accurate and more efficient vulnerability detection is realized.

The invention discloses an intelligent system grey box fuzzy testing method based on state awareness. The method comprises the steps that S1, a state-memory data set is acquired; s2, constructing and training a feature representation network; s3, calculating a feature center and a judgment threshold value of each known state category; s4, collecting the memory when the intelligent system runs in real time, obtaining a to-be-tested memory snapshot, and extracting a feature vector of the to-be-tested memory snapshot by using the feature representation network; s5, performing similarity comparison on the feature vector of the memory snapshot to be detected and all feature centers in the state prototype library, and if the distance between the feature vector of the memory snapshot to be detected and any feature center does not exceed a corresponding judgment threshold, judging that the intelligent system is in the known state category; otherwise, deducing the state as a potential new state; and S6, verifying the potential new state, and if the potential new state is confirmed to be a new state, adding the feature vector of the potential new state into the state-memory data set, and dynamically updating the state prototype library. According to the invention, more accurate description and efficient testing of complex state logic of the intelligent system are realized.

Computer-implemented method for simulating module operation of a module of a modular industrial plant include providing an initial model for the module based on a simulation, e.g., a white-box simulation or a black-box simulation or a grey-box simulation; performing a knowledge-based enhancement step or a data-driven enhancement step comprising obtaining an enhanced model; and simulating module operation of the module by means of the enhanced model.

The invention discloses an intelligent contract vulnerability automatic detection method and system based on a directional test, combines static analysis and a directional grey box fuzzy test detection means, provides a novel grey box fuzzy test guidance strategy, and utilizes vulnerability information of preliminary investigation of an intelligent contract in a static analysis stage to realize automatic detection of the vulnerability of the intelligent contract. Seed scheduling in a grey box fuzzy test stage is guided, and the priority of seeds is judged through the path distance between a seed operation path and a target basic block and the data distance, so that the seeds closer to trigger vulnerabilities have more opportunities to generate filial generations, and a seed population closer to the target basic block is derived more pertinently. According to the method, directional testing is adopted, and static analysis and fuzzy testing are combined, so that quick and accurate detection of the vulnerability of the smart contract is realized.

The application provides a kind of grey box fuzzing test method and system based on Intel-PT and Syzkaller, method includes: first fuzzing test engine starts test cycle, constantly test and synchronize test progress to manager;Manager sends the new use case found by second fuzzing test engine to first fuzzing test engine in response to the poll request of first fuzzing test engine, the effective queue of first fuzzing test engine is updated, start to generate new use case and test;First fuzzing test engine sends new use case to executor, executor prepares to execute test;Executor identifies kernel address range before executing test, collects static storage area of new use case, applies to start PT tracking function by super call;Intercept control register setting request, obtain the value of control register, and assign the value of control register to the parameter of super call, the value of control register is the page table base address of tracking system;Process super call to set the kernel address range of tracking, set page table base address, start PT tracking;Executor starts to parse and execute input use case, real-time call registered parsing function to complete parsing, fill in static storage area;After the execution of multiple rounds of system call of executor, updated coverage information is synchronized to first fuzzing test engine.The grey box fuzzing test method and system based on Intel-PT and Syzkaller provided by the application can be used for closed source version linux system binary grey box fuzzing test, to form effective feedback information closed loop.

The invention relates to a stateful protocol grey box fuzzy test method based on heuristic variation scheduling, which comprises the following steps: S1, initializing a test environment, and establishing a high-speed communication channel based on a shared memory, S2, dividing an initial seed set into a plurality of source seed systems according to sources; s3, selecting a target test state based on an inferred protocol state machine, and selecting a message sequence capable of reaching the state from a corpus, S4, selecting an enabled mutation operator to perform havoc mutation on candidate subsequences according to a current generation offspring mutation strategy of a current source seed system; s5, the test case is sent to the server through the shared memory channel, and the execution state is monitored; s6, generating a new generation of filial generation strategy after the first generation of filial generation strategy of the current source seed system is evaluated; and S7, repeating the steps S3 to S6 until a test termination condition is met. According to the method, global exploration and local development can be effectively balanced, the diversity and pertinence of test cases are improved, and the coverage capability of protocol states and code paths and the discovery probability of deep vulnerabilities are remarkably enhanced.

The invention relates to a complex control flow-oriented directional grey box fuzzy test method, which comprises the following steps of: S1, constructing a target call stack and performing program instrumentation, and collecting the target call stack and path feedback information during operation at an instrumentation point; s2, coarse-grained calling sequence similarity screening is carried out, and coarse-grained calling sequence similarity is obtained; s3, fine-grained semantic similarity calculation: performing weighted aggregation on semantic vectors of all function nodes in the target call stack to form stack-level semantic vectors, and calculating fine-grained semantic similarity by using cosine similarity; s4, carrying out comprehensive distance fusion to obtain a comprehensive distance for seed priority evaluation; according to the method, the path identification and approximation capability of the directional grey box fuzzy test in a complex control flow scene is optimized; the efficient and extensible directional fuzzy testing method is provided for modern software security testing, the analysis ability in a complex control flow scene is expanded, the software security is improved, and the vulnerability verification process is optimized.

This invention belongs to the field of software security testing technology, specifically a method for automatically generating vulnerability proof-of-concept (PoC) based on option-aware directed gray-box fuzzing. The invention includes: performing static analysis on the target program, extracting each configuration option and its directly affecting variables, analyzing the complete value space and control conditions of the variables, and inferring the constraint relationships between options; based on option knowledge, employing option validity inference technology based on taint analysis during the fuzzing phase to dynamically identify option combinations effective in reaching the target location; introducing an alternating guided fuzzing strategy, alternately executing option input fuzzing and file input fuzzing, systematically driving driver execution to approximate the target vulnerability location, and ultimately efficiently generating the target vulnerability PoC. This invention effectively solves the problems of traditional directed gray-box fuzzing tools ignoring the influence of program configuration options, the independence of options and file mutations, and the lack of coordination, significantly improving the efficiency and success rate of vulnerability PoC generation.

In exemplary embodiments, methods and systems are provided that include one or more sensors of a vehicle and a processor of the vehicle. The one or more sensors are configured to obtain sensor data as to operation of the vehicle. The processor is coupled to the one or more sensors, and is configured to at least facilitate inputting the sensor data into a grey-box model including a neural network model for the forces acting on the vehicle and a physics-based model for body motion of the vehicle; determining a trajectory of the vehicle, using the grey-box model; determining a control action for the vehicle, based on the grey-box model using the sensor data; and executing the control action for the vehicle, in accordance with instructions provided by the processor.

The invention discloses a guitar amplifier circuit modeling method based on a grey box, and relates to the technical field of guitar amplifier circuit modeling. Comprising the steps that an input signal is firstly filtered by a prefilter module, so that the distortion amount of each frequency band signal is determined; scaling the input signal and the output signal respectively based on the front gain and the rear gain of the nonlinear module; the signal is changed into a distorted signal after passing through the nonlinear module, and the distorted signal is filtered by a rear linear filtering module H2 (z), so that an overtone component and a frequency component in the distorted signal are determined; according to the digital nonlinear modeling method for the guitar amplifier device based on the grey box model, digital nonlinear modeling of the guitar amplifier device is only related to signals of input / output reference equipment, and the modeling cost is greatly reduced; meanwhile, by observing the structure of the digital model, the calculation amount of the method is relatively small, and the requirement of embedded equipment for real-time performance is met.

The invention relates to the technical field of shop assistant service specification recognition, in particular to a convenient store assistant service specification recognition system and method driven by a grey box model, and the system comprises a non-standard service action, the output end of the non-standard service action is electrically connected with a video inspection strategy, and the output end of the video inspection strategy is electrically connected with a human body element detector. The output end of the human body element detection is electrically connected with a human action habit algorithm, and the input end of the human action habit algorithm is electrically connected with physical priori knowledge; on one hand, the method is assisted in copying and popularizing video monitoring industries such as supermarkets and retail industries, and on the other hand, through the scheme which is low in economic cost and high in accuracy, manpower can be replaced to a certain extent to complete convenience store assistant service standard video inspection, gas station convenience store service supervision is assisted, and enterprise management cost is reduced.

The invention discloses a fully mechanized coal mining face dynamic cutting simulation method based on a black box-grey box-white box coal seam model. Constructing a virtual geological simulation module, and generating a virtual geological model data file; on the basis of virtual geologic model data, a coal rock interface is constructed through simulated drilling sampling and a Kriging interpolation algorithm, an exploration error coefficient is introduced, and black box, grey box and white box coal seam error models are constructed; importing a three-dimensional model and an error model of the fully-mechanized coal mining equipment into the simulation platform, and constructing an equipment-coal seam coupling model; calling real-time pose change data of a coal mining machine roller, and adaptively generating a cutting top plate and a cutting bottom plate; and utilizing a pre-trained intelligent model to predict the cutting force in real time, dynamically updating the state of the virtual exploration coal seam error model based on a prediction result and a real-time cutting track, and autonomously regulating and controlling the operation parameters of the coal mining machine. According to the method, a three-level error model is constructed, a dynamic model system of the geological cognition process can be truly reflected, and deep fusion of exploration errors and cutting simulation is achieved.

The invention relates to the field of machine learning, in particular to a large language model copyright protection method and device based on a fingerprint member probability offset signal, and the method comprises the steps: constructing a fingerprint data set, and symmetrically dividing the fingerprint data set into a training subset and a reference subset; performing fine tuning training on the original model by using the reference subset to obtain a reference model; generating a semantic equivalence disturbance sample set according to the training subset; according to the semantic equivalence disturbance sample set, calculating respective probability offset indexes of the to-be-verified model and the reference model; according to respective probability offset indexes of the to-be-verified model and the reference model, determining an average signal strength index of the fingerprint data set; and judging whether the to-be-verified model forms infringement or not according to the average signal strength index. According to the method, hidden, efficient, accurate and high-robustness model copyright verification under a gray box or black box scene after the large language model is leaked can be realized.

The invention relates to a grey box model-driven gas station employee dressing standard identification system. The system is composed of five modules including a dressing nonstandard behavior module, a video inspection strategy module, a target detection model module, a physical priori knowledge module and a physical information-driven dressing nonstandard identification algorithm module. The non-standard dressing behaviors comprise non-standard behaviors of not wearing black insulating shoes and not wearing work cards. According to the method, intelligent identification can be carried out on the dressing normativity of employees in the video monitoring service industry under the conditions of poor network environment, poor hardware facilities, poor monitoring environment and complex scenes, and on one hand, the method is assisted to be copied and popularized in the video monitoring industries of supermarkets and retail industries; meanwhile, by means of the scheme with low economic cost and high accuracy, standard video inspection of gas station staff can be completed instead of manpower to a certain extent, gas station service supervision is assisted, and enterprise management cost is reduced.

The invention provides a fuzz test debugging method and device based on a recording and replaying technology, and relates to the technical field of computers.The method comprises the steps that fuzz testing is conducted on a target object, and first information in the testing process is recorded; the first information comprises interaction information between test strategies used in the fuzzy test process and metadata related to seeds in the test process; after the fuzz test is finished, obtaining first information, and replaying the recorded fuzz test behavior based on the first information to obtain second information; the second information comprises an execution path of the seed and information related to the test behavior. According to the fuzz test debugging method and device based on the recording and replaying technology, the execution condition of the key strategy in the fuzz test process is accurately recorded and replayed, so that the analysis and improvement process of the fuzz tester is simplified, and the fuzz test debugging method and device are particularly suitable for optimizing the grey box fuzz test guided by the coverage rate.

The application discloses a microcontroller unit (MCU) firmware grey box fuzzing test method. The method first establishes a high-fidelity peripheral simulator in a simulation execution environment, the peripheral simulator including peripheral registers, internal states and state machines, and determines the interrupt line enable state by jointly monitoring the global interrupt controller register and the peripheral interrupt control register to realize the controlled triggering of the peripheral interrupt. Secondly, the time when the firmware or DMA reads the peripheral data register is determined as an input consumption point during firmware execution, an incremental snapshot containing the processor register state, the peripheral state and the memory dirty page information is saved at the input consumption point, and only the input data that has not been read is mutated after the snapshot is restored. Finally, the test input is divided into multiple input streams corresponding to different peripherals or data registers, a probability distribution is constructed according to the access frequency of each input stream, and the input stream to be mutated is selected, so that the path exploration efficiency and the vulnerability discovery capability of the fuzzing test are improved.

The invention relates to an automatic SSTI grey box penetration test method based on reinforcement learning. Comprising the following steps: taking a Web application program source code as input, and outputting a risk point list through white-box risk prediction by adopting CodeQL and reinforcement learning; in a full black box mode, crawling Web application dynamic content to identify potential injection points, and forming an injection point set together with the risk point list; and constructing an escape agent and a killing-free agent, sharing a state space, testing the injection point set, and generating a corresponding attack load. Vulnerability information returned by CodeQL is associated with a URL, and is converted into an injection point which can be identified by a black box attack load generator, and the injection point is supplemented to a black box analysis process to reduce the missing report rate; a black box attack load generator is used for replacing manual feedback, a white box risk prediction result is automatically verified, the authenticity of vulnerabilities is verified through actual attack testing, and automatic optimization of the false alarm rate is achieved.