Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research si...Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research significance for network security.Due to the strong generalization of invalid features during training process,it is more difficult for single autoencoder intrusion detection model to obtain effective results.A network intrusion detection model based on the Ensemble of Denoising Adversarial Autoencoder(EDAAE)was proposed,which had higher accuracy and reliability compared to the traditional anomaly detection model.Using the adversarial learning idea of Adversarial Autoencoder(AAE),the discriminator module was added to the original model,and the encoder part was used as the generator.The distribution of the hidden space of the data generated by the encoder matched with the distribution of the original data.The generalization of the model to the invalid features was also reduced to improve the detection accuracy.At the same time,the denoising autoencoder and integrated operation was introduced to prevent overfitting in the adversarial learning process.Experiments on the CICIDS2018 traffic dataset showed that the proposed intrusion detection model achieves an Accuracy of 95.23%,which out performs traditional self-encoders and other existing intrusion detection models methods in terms of overall performance.展开更多
A novel method for detecting anomalous program behavior is presented, which is applicable to hostbased intrusion detection systems that monitor system call activities. The method constructs a homogeneous Markov chain ...A novel method for detecting anomalous program behavior is presented, which is applicable to hostbased intrusion detection systems that monitor system call activities. The method constructs a homogeneous Markov chain model to characterize the normal behavior of a privileged program, and associates the states of the Markov chain with the unique system calls in the training data. At the detection stage, the probabilities that the Markov chain model supports the system call sequences generated by the program are computed. A low probability indicates an anomalous sequence that may result from intrusive activities. Then a decision rule based on the number of anomalous sequences in a locality frame is adopted to classify the program＇s behavior. The method gives attention to both computational efficiency and detection accuracy, and is especially suitable for on-line detection. It has been applied to practical host-based intrusion detection systems.展开更多
Mobile ad hoc networking （MANET） has become an exciting and important technology in recent years, because of the rapid proliferation of wireless devices. Mobile ad hoc networks is highly vulnerable to attacks due to...Mobile ad hoc networking （MANET） has become an exciting and important technology in recent years, because of the rapid proliferation of wireless devices. Mobile ad hoc networks is highly vulnerable to attacks due to the open medium, dynamically changing network topology, cooperative algorithms, and lack of centralized monitoring and management point. The traditional way of protecting networks with firewalls and encryption software is no longer sufficient and effective for those features. A distributed intrusion detection approach based on timed automata is given. A cluster-based detection scheme is presented, where periodically a node is elected as the monitor node for a cluster. These monitor nodes can not only make local intrusion detection decisions, but also cooperatively take part in global intrusion detection. And then the timed automata is constructed by the way of manually abstracting the correct behaviours of the node according to the routing protocol of dynamic source routing （DSR）. The monitor nodes can verify the behaviour of every nodes by timed automata, and validly detect real-time attacks without signatures of intrusion or trained data. Compared with the architecture where each node is its own IDS agent, the approach is much more efficient while maintaining the same level of effectiveness. Finally, the intrusion detection method is evaluated through simulation experiments.展开更多
Infrared target intrusion detection has significant applications in the fields of military defence and intelligent warning.In view of the characteristics of intrusion targets as well as inspection difficulties,an infr...Infrared target intrusion detection has significant applications in the fields of military defence and intelligent warning.In view of the characteristics of intrusion targets as well as inspection difficulties,an infrared target intrusion detection algorithm based on feature fusion and enhancement was proposed.This algorithm combines static target mode analysis and dynamic multi-frame correlation detection to extract infrared target features at different levels.Among them,LBP texture analysis can be used to effectively identify the posterior feature patterns which have been contained in the target library,while motion frame difference method can detect the moving regions of the image,improve the integrity of target regions such as camouflage,sheltering and deformation.In order to integrate the advantages of the two methods,the enhanced convolutional neural network was designed and the feature images obtained by the two methods were fused and enhanced.The enhancement module of the network strengthened and screened the targets,and realized the background suppression of infrared images.Based on the experiments,the effect of the proposed method and the comparison method on the background suppression and detection performance was evaluated,and the results showed that the SCRG and BSF values of the method in this paper had a better performance in multiple data sets,and it’s detection performance was far better than the comparison algorithm.The experiment results indicated that,compared with traditional infrared target detection methods,the proposed method could detect the infrared invasion target more accurately,and suppress the background noise more effectively.展开更多
To solve the problem that current intrusion detection model needs large-scale data in formulating the model in real-time use, an intrusion detection system model based on grey theory （GTIDS） is presented. Grey theor...To solve the problem that current intrusion detection model needs large-scale data in formulating the model in real-time use, an intrusion detection system model based on grey theory （GTIDS） is presented. Grey theory has merits of fewer requirements on original data scale, less limitation of the distribution pattern and simpler algorithm in modeling. With these merits GTIDS constructs model according to partial time sequence for rapid detect on intrusive act in secure system. In this detection model rate of false drop and false retrieval are effectively reduced through twice modeling and repeated detect on target data. Furthermore, GTIDS framework and specific process of modeling algorithm are presented. The affectivity of GTIDS is proved through emulated experiments comparing snort and next-generation intrusion detection expert system （NIDES） in SRI international.展开更多
A new approach for abnormal behavior detection was proposed using causality analysis and sparse reconstruction. To effectively represent multiple-object behavior, low level visual features and causality features were ...A new approach for abnormal behavior detection was proposed using causality analysis and sparse reconstruction. To effectively represent multiple-object behavior, low level visual features and causality features were adopted. The low level visual features, which included trajectory shape descriptor, speeded up robust features and histograms of optical flow, were used to describe properties of individual behavior, and causality features obtained by causality analysis were introduced to depict the interaction information among a set of objects. In order to cope with feature noisy and uncertainty, a method for multiple-object anomaly detection was presented via a sparse reconstruction. The abnormality of the testing sample was decided by the sparse reconstruction cost from an atomically learned dictionary. Experiment results show the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases for abnormal behavior detection.展开更多
An important problem in wireless communication networks (WCNs) is that they have a minimum number of resources, which leads to high-security threats. An approach to find and detect the attacks is the intrusion detecti...An important problem in wireless communication networks (WCNs) is that they have a minimum number of resources, which leads to high-security threats. An approach to find and detect the attacks is the intrusion detection system (IDS). In this paper, the fuzzy lion Bayes system (FLBS) is proposed for intrusion detection mechanism. Initially, the data set is grouped into a number of clusters by the fuzzy clustering algorithm. Here, the Naive Bayes classifier is integrated with the lion optimization algorithm and the new lion naive Bayes (LNB) is created for optimally generating the probability measures. Then, the LNB model is applied to each data group, and the aggregated data is generated. After generating the aggregated data, the LNB model is applied to the aggregated data, and the abnormal nodes are identified based on the posterior probability function. The performance of the proposed FLBS system is evaluated using the KDD Cup 99 data and the comparative analysis is performed by the existing methods for the evaluation metrics accuracy and false acceptance rate (FAR). From the experimental results, it can be shown that the proposed system has the maximum performance, which shows the effectiveness of the proposed system in the intrusion detection.展开更多
Static secure techniques, such as firewall, hierarchy filtering, distributed disposing,layer management, autonomy agent, secure communication, were introduced in distributed intrusion detection. The self-protection ag...Static secure techniques, such as firewall, hierarchy filtering, distributed disposing,layer management, autonomy agent, secure communication, were introduced in distributed intrusion detection. The self-protection agents were designed, which have the distributed architecture,cooperate with the agents in intrusion detection in a loose-coupled manner, protect the security of intrusion detection system, and respond to the intrusion actively. A prototype self-protection agent was implemented by using the packet filter in operation system kernel. The results show that all the hosts with the part of network-based intrusion detection system and the whole intrusion detection system are invisible from the outside and network scanning, and cannot apperceive the existence of network-based intrusion detection system. The communication between every part is secure. In the low layer, the packet streams are controlled to avoid the buffer leaks exist ing in some system service process and back-door programs, so as to prevent users from misusing and vicious attack like Trojan Horse effectively.展开更多
In this paper,we introduce an adaptive clustering algorithm for intrusion detection based on wavecluster which was introduced by Gholamhosein in 1999 and used with success in image processing.Because of the non-statio...In this paper,we introduce an adaptive clustering algorithm for intrusion detection based on wavecluster which was introduced by Gholamhosein in 1999 and used with success in image processing.Because of the non-stationary characteristic of network traffic,we extend and develop an adaptive wavecluster algorithm for intrusion detection.Using the multiresolution property of wavelet transforms,we can effectively identify arbitrarily shaped clusters at different scales and degrees of detail,moreover,applying wavelet transform removes the noise from the original feature space and make more accurate cluster found.Experimental results on KDD-99 intrusion detection dataset show the efficiency and accuracy of this algorithm.A detection rate above 96% and a false alarm rate below 3% are achieved.展开更多
There are inherent vulnerabilities that are not easily preventable in the mobile Ad-Hoc networks.To build a highly secure wireless Ad-Hoc network,intrusion detection and response techniques need to be deployed;The int...There are inherent vulnerabilities that are not easily preventable in the mobile Ad-Hoc networks.To build a highly secure wireless Ad-Hoc network,intrusion detection and response techniques need to be deployed;The intrusion detection and cluster-based Ad-Hoc networks has been introduced,then,an architecture for better intrusion detection based on cluster using Data Mining in wireless Ad -Hoc networks has been shown.A statistical anomaly detection approach has been used.The anomaly detection and trace analysis have been done locally in each node and possibly through cooperation with clusterhead detection in the network.展开更多
Anomaly detection has been an active research topic in the field of network intrusion detection for many years. A novel method is presented for anomaly detection based on system calls into the kernels of Unix or Linux...Anomaly detection has been an active research topic in the field of network intrusion detection for many years. A novel method is presented for anomaly detection based on system calls into the kernels of Unix or Linux systems. The method uses the data mining technique to model the normal behavior of a privileged program and uses a variable-length pattern matching algorithm to perform the comparison of the current behavior and historic normal behavior, which is more suitable for this problem than the fixed-length pattern matching algorithm proposed by Forrest et al. At the detection stage, the particularity of the audit data is taken into account, and two alternative schemes could be used to distinguish between normalities and intrusions. The method gives attention to both computational efficiency and detection accuracy and is especially applicable for on-line detection. The performance of the method is evaluated using the typical testing data set, and the results show that it is significantly better than the anomaly detection method based on hidden Markov models proposed by Yan et al. and the method based on fixed-length patterns proposed by Forrest and Hofmeyr. The novel method has been applied to practical hosted-based intrusion detection systems and achieved high detection performance.展开更多
The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced met...The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.展开更多
The nature of adhoc networks makes them vulnerable to security attacks. Many security technologies such as intrusion prevention and intrusion detection are passive in response to intrusions in that their countermea- s...The nature of adhoc networks makes them vulnerable to security attacks. Many security technologies such as intrusion prevention and intrusion detection are passive in response to intrusions in that their countermea- sures are only to protect the networks, and there is no automated network-wide counteraction against detected intrusions, the architecture of cooperation intrusion response based multi-agent is propose. The architecture is composed of mobile agents. Monitor agent resides on every node and monitors its neighbor nodes. Decision agent collects information from monitor nodes and detects an intrusion by security policies. When an intruder is found in the architecture, the block agents will get to the neighbor nodes of the intruder and form the mobile firewall to isolate the intruder. In the end, we evaluate it by simulation.展开更多
An abstraction and an investigation to the worth of dendritic cells (DCs) ability to collect, process and present antigens are presented. Computationally, this ability is shown to provide a feature reduction mechanism...An abstraction and an investigation to the worth of dendritic cells (DCs) ability to collect, process and present antigens are presented. Computationally, this ability is shown to provide a feature reduction mechanism that could be used to reduce the complexity of a search space, a mechanism for development of highly specialized detector sets as well as a selective mechanism used in directing subsets of detectors to be activated when certain danger signals are present. It is shown that DCs, primed by different danger signals, provide a basis for different anomaly detection pathways. Different antigen-peptides are developed based on different danger signals present, and these peptides are presented to different adaptive layer detectors that correspond to the given danger signal. Experiments are then undertaken that compare current approaches, where a full antigen structure and the whole repertoire of detectors are used, with the proposed approach. Experiment results indicate that such an approach is feasible and can help reduce the complexity of the problem by significant levels. It also improves the efficiency of the system, given that only a subset of detectors are involved during the detection process. Having several different sets of detectors increases the robustness of the resulting system. Detectors developed based on peptides are also highly discriminative, which reduces the false positives rates, making the approach feasible for a real time environment.展开更多
文摘Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research significance for network security.Due to the strong generalization of invalid features during training process,it is more difficult for single autoencoder intrusion detection model to obtain effective results.A network intrusion detection model based on the Ensemble of Denoising Adversarial Autoencoder(EDAAE)was proposed,which had higher accuracy and reliability compared to the traditional anomaly detection model.Using the adversarial learning idea of Adversarial Autoencoder(AAE),the discriminator module was added to the original model,and the encoder part was used as the generator.The distribution of the hidden space of the data generated by the encoder matched with the distribution of the original data.The generalization of the model to the invalid features was also reduced to improve the detection accuracy.At the same time,the denoising autoencoder and integrated operation was introduced to prevent overfitting in the adversarial learning process.Experiments on the CICIDS2018 traffic dataset showed that the proposed intrusion detection model achieves an Accuracy of 95.23%,which out performs traditional self-encoders and other existing intrusion detection models methods in terms of overall performance.
基金the National Grand Fundamental Research "973" Program of China (2004CB318109)the High-Technology Research and Development Plan of China (863-307-7-5)the National Information Security 242 Program ofChina (2005C39).
文摘A novel method for detecting anomalous program behavior is presented, which is applicable to hostbased intrusion detection systems that monitor system call activities. The method constructs a homogeneous Markov chain model to characterize the normal behavior of a privileged program, and associates the states of the Markov chain with the unique system calls in the training data. At the detection stage, the probabilities that the Markov chain model supports the system call sequences generated by the program are computed. A low probability indicates an anomalous sequence that may result from intrusive activities. Then a decision rule based on the number of anomalous sequences in a locality frame is adopted to classify the program＇s behavior. The method gives attention to both computational efficiency and detection accuracy, and is especially suitable for on-line detection. It has been applied to practical host-based intrusion detection systems.
基金the National High Technology Development "863" Program of China (2006AA01Z436, 2007AA01Z452)the National Natural Science Foundation of China(60702042).
文摘Mobile ad hoc networking （MANET） has become an exciting and important technology in recent years, because of the rapid proliferation of wireless devices. Mobile ad hoc networks is highly vulnerable to attacks due to the open medium, dynamically changing network topology, cooperative algorithms, and lack of centralized monitoring and management point. The traditional way of protecting networks with firewalls and encryption software is no longer sufficient and effective for those features. A distributed intrusion detection approach based on timed automata is given. A cluster-based detection scheme is presented, where periodically a node is elected as the monitor node for a cluster. These monitor nodes can not only make local intrusion detection decisions, but also cooperatively take part in global intrusion detection. And then the timed automata is constructed by the way of manually abstracting the correct behaviours of the node according to the routing protocol of dynamic source routing （DSR）. The monitor nodes can verify the behaviour of every nodes by timed automata, and validly detect real-time attacks without signatures of intrusion or trained data. Compared with the architecture where each node is its own IDS agent, the approach is much more efficient while maintaining the same level of effectiveness. Finally, the intrusion detection method is evaluated through simulation experiments.
基金This work was supported by the National Natural Science Foundation of China(grant number:61671470)the National Key Research and Development Program of China(grant number:2016YFC0802904)the Postdoctoral Science Foundation Funded Project of China(grant number:2017M623423).
文摘Infrared target intrusion detection has significant applications in the fields of military defence and intelligent warning.In view of the characteristics of intrusion targets as well as inspection difficulties,an infrared target intrusion detection algorithm based on feature fusion and enhancement was proposed.This algorithm combines static target mode analysis and dynamic multi-frame correlation detection to extract infrared target features at different levels.Among them,LBP texture analysis can be used to effectively identify the posterior feature patterns which have been contained in the target library,while motion frame difference method can detect the moving regions of the image,improve the integrity of target regions such as camouflage,sheltering and deformation.In order to integrate the advantages of the two methods,the enhanced convolutional neural network was designed and the feature images obtained by the two methods were fused and enhanced.The enhancement module of the network strengthened and screened the targets,and realized the background suppression of infrared images.Based on the experiments,the effect of the proposed method and the comparison method on the background suppression and detection performance was evaluated,and the results showed that the SCRG and BSF values of the method in this paper had a better performance in multiple data sets,and it’s detection performance was far better than the comparison algorithm.The experiment results indicated that,compared with traditional infrared target detection methods,the proposed method could detect the infrared invasion target more accurately,and suppress the background noise more effectively.
文摘To solve the problem that current intrusion detection model needs large-scale data in formulating the model in real-time use, an intrusion detection system model based on grey theory （GTIDS） is presented. Grey theory has merits of fewer requirements on original data scale, less limitation of the distribution pattern and simpler algorithm in modeling. With these merits GTIDS constructs model according to partial time sequence for rapid detect on intrusive act in secure system. In this detection model rate of false drop and false retrieval are effectively reduced through twice modeling and repeated detect on target data. Furthermore, GTIDS framework and specific process of modeling algorithm are presented. The affectivity of GTIDS is proved through emulated experiments comparing snort and next-generation intrusion detection expert system （NIDES） in SRI international.
基金Project(50808025) supported by the National Natural Science Foundation of ChinaProject(20090162110057) supported by the Doctoral Fund of Ministry of Education,China
文摘A new approach for abnormal behavior detection was proposed using causality analysis and sparse reconstruction. To effectively represent multiple-object behavior, low level visual features and causality features were adopted. The low level visual features, which included trajectory shape descriptor, speeded up robust features and histograms of optical flow, were used to describe properties of individual behavior, and causality features obtained by causality analysis were introduced to depict the interaction information among a set of objects. In order to cope with feature noisy and uncertainty, a method for multiple-object anomaly detection was presented via a sparse reconstruction. The abnormality of the testing sample was decided by the sparse reconstruction cost from an atomically learned dictionary. Experiment results show the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases for abnormal behavior detection.
文摘An important problem in wireless communication networks (WCNs) is that they have a minimum number of resources, which leads to high-security threats. An approach to find and detect the attacks is the intrusion detection system (IDS). In this paper, the fuzzy lion Bayes system (FLBS) is proposed for intrusion detection mechanism. Initially, the data set is grouped into a number of clusters by the fuzzy clustering algorithm. Here, the Naive Bayes classifier is integrated with the lion optimization algorithm and the new lion naive Bayes (LNB) is created for optimally generating the probability measures. Then, the LNB model is applied to each data group, and the aggregated data is generated. After generating the aggregated data, the LNB model is applied to the aggregated data, and the abnormal nodes are identified based on the posterior probability function. The performance of the proposed FLBS system is evaluated using the KDD Cup 99 data and the comparative analysis is performed by the existing methods for the evaluation metrics accuracy and false acceptance rate (FAR). From the experimental results, it can be shown that the proposed system has the maximum performance, which shows the effectiveness of the proposed system in the intrusion detection.
文摘Static secure techniques, such as firewall, hierarchy filtering, distributed disposing,layer management, autonomy agent, secure communication, were introduced in distributed intrusion detection. The self-protection agents were designed, which have the distributed architecture,cooperate with the agents in intrusion detection in a loose-coupled manner, protect the security of intrusion detection system, and respond to the intrusion actively. A prototype self-protection agent was implemented by using the packet filter in operation system kernel. The results show that all the hosts with the part of network-based intrusion detection system and the whole intrusion detection system are invisible from the outside and network scanning, and cannot apperceive the existence of network-based intrusion detection system. The communication between every part is secure. In the low layer, the packet streams are controlled to avoid the buffer leaks exist ing in some system service process and back-door programs, so as to prevent users from misusing and vicious attack like Trojan Horse effectively.
文摘In this paper,we introduce an adaptive clustering algorithm for intrusion detection based on wavecluster which was introduced by Gholamhosein in 1999 and used with success in image processing.Because of the non-stationary characteristic of network traffic,we extend and develop an adaptive wavecluster algorithm for intrusion detection.Using the multiresolution property of wavelet transforms,we can effectively identify arbitrarily shaped clusters at different scales and degrees of detail,moreover,applying wavelet transform removes the noise from the original feature space and make more accurate cluster found.Experimental results on KDD-99 intrusion detection dataset show the efficiency and accuracy of this algorithm.A detection rate above 96% and a false alarm rate below 3% are achieved.
文摘There are inherent vulnerabilities that are not easily preventable in the mobile Ad-Hoc networks.To build a highly secure wireless Ad-Hoc network,intrusion detection and response techniques need to be deployed;The intrusion detection and cluster-based Ad-Hoc networks has been introduced,then,an architecture for better intrusion detection based on cluster using Data Mining in wireless Ad -Hoc networks has been shown.A statistical anomaly detection approach has been used.The anomaly detection and trace analysis have been done locally in each node and possibly through cooperation with clusterhead detection in the network.
基金supported by the National Grand Fundamental Research "973" Program of China (2004CB318109)the National High-Technology Research and Development Plan of China (2006AA01Z452)the National Information Security "242"Program of China (2005C39).
文摘Anomaly detection has been an active research topic in the field of network intrusion detection for many years. A novel method is presented for anomaly detection based on system calls into the kernels of Unix or Linux systems. The method uses the data mining technique to model the normal behavior of a privileged program and uses a variable-length pattern matching algorithm to perform the comparison of the current behavior and historic normal behavior, which is more suitable for this problem than the fixed-length pattern matching algorithm proposed by Forrest et al. At the detection stage, the particularity of the audit data is taken into account, and two alternative schemes could be used to distinguish between normalities and intrusions. The method gives attention to both computational efficiency and detection accuracy and is especially applicable for on-line detection. The performance of the method is evaluated using the typical testing data set, and the results show that it is significantly better than the anomaly detection method based on hidden Markov models proposed by Yan et al. and the method based on fixed-length patterns proposed by Forrest and Hofmeyr. The novel method has been applied to practical hosted-based intrusion detection systems and achieved high detection performance.
文摘The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.
基金This project was supported by the National Natural Science Foundation of China (60672068)the National High Technology Development 863 Program of China (2006AA01Z436, 2007AA01Z452.)
文摘The nature of adhoc networks makes them vulnerable to security attacks. Many security technologies such as intrusion prevention and intrusion detection are passive in response to intrusions in that their countermea- sures are only to protect the networks, and there is no automated network-wide counteraction against detected intrusions, the architecture of cooperation intrusion response based multi-agent is propose. The architecture is composed of mobile agents. Monitor agent resides on every node and monitors its neighbor nodes. Decision agent collects information from monitor nodes and detects an intrusion by security policies. When an intruder is found in the architecture, the block agents will get to the neighbor nodes of the intruder and form the mobile firewall to isolate the intruder. In the end, we evaluate it by simulation.
基金Project(50275150) supported by the National Natural Science Foundation of ChinaProjects(20040533035, 20070533131) supported by the National Research Foundation for the Doctoral Program of Higher Education of China
文摘An abstraction and an investigation to the worth of dendritic cells (DCs) ability to collect, process and present antigens are presented. Computationally, this ability is shown to provide a feature reduction mechanism that could be used to reduce the complexity of a search space, a mechanism for development of highly specialized detector sets as well as a selective mechanism used in directing subsets of detectors to be activated when certain danger signals are present. It is shown that DCs, primed by different danger signals, provide a basis for different anomaly detection pathways. Different antigen-peptides are developed based on different danger signals present, and these peptides are presented to different adaptive layer detectors that correspond to the given danger signal. Experiments are then undertaken that compare current approaches, where a full antigen structure and the whole repertoire of detectors are used, with the proposed approach. Experiment results indicate that such an approach is feasible and can help reduce the complexity of the problem by significant levels. It also improves the efficiency of the system, given that only a subset of detectors are involved during the detection process. Having several different sets of detectors increases the robustness of the resulting system. Detectors developed based on peptides are also highly discriminative, which reduces the false positives rates, making the approach feasible for a real time environment.
