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SVM-DT-Based Adaptive and Collaborative intrusion detection

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 108-118页

作者： Shaohua Teng Naiqi Wu Haibin Zhu Luyao Teng Wei Zhang School of Computer Science and Technology Guangdong University of Technology Guangzhou 510006 China Institute of Systems Engineering Macao University of Science and Technology Macao 999078 China school of Electro-Mechanical Engineering Guangdong University of Technology Guangzhou 510006 China Collaborative Systems Laboratory Nipissing University North Bay Ontario PICILZ Canada College of Engineering and Science Victoria University Ballarat Rd Footscray VIC 3011 Australia IEEE

As a primary defense technique, intrusion detection becomes more and more significant since the security of the networks is one of the most critical issues in the world. We present an adaptive collaboration intrusion detection method to improve the safety of a network. A self-adaptive and collaborative intrusion detection model is built by applying the Environmentsclasses, agents, roles, groups, and objects(E-CARGO) model. The objects, roles, agents, and groups are designed by using decision trees(DTs) and support vector machines(SVMs), and adaptive scheduling mechanisms are set up. The KDD CUP 1999 data set is used to verify the effectiveness of the method. The experimental results demonstrate the feasibility and efficiency of the proposed collaborative and adaptive intrusion detection method. Also, the proposed method is shown to be more predominant than the methods that use a set of single type support vector machine(SVM) in terms of detection precision rate and recall rate.

关键词： Adaptive and collaborative intrusion detection decision tree(DT) support vector machines(SVM)

Distributed intrusion detection for mobile ad hoc networks

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Journal of Systems Engineering and Electronics 2008年第4期19卷 851-859页

作者： Yi Ping Jiang Xinghao Wu Yue Liu Ning School of Information Security Engineering Shanghai Jiaotong Univ Shanghai 200030 P. R. China State Key Lab of Information Security Graduate School of Chinese Academy of Sciences Beijing 100039 P. R. China

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.

关键词： mobile ad hoc networks routing protocol security intrusion detection timed automata.

An intrusion detection Method for Advanced Metering Infrastructure System Based on Federated Learning

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Journal of Modern Power Systems and Clean Energy 2023年第3期11卷 927-937页

作者： Haolan Liang Dongqi Liu Xiangjun Zeng Chunxiao Ye School of Electrical and Information Engineering Changsha University of Science and TechnologyChangsha 410114China Hunan Institute of Engineering Xiangtan 411104China

An advanced metering infrastructure(AMI)system plays a key role in the smart grid(SG),but it is vulnerable to *** detection methods for AMI cyberattacks mainly focus on the data center or a distributed independent *** one hand,it is difficult to train an excellent detection intrusion model on a self-learning independent *** the other hand,large amounts of data are shared over the network and uploaded to a central node for *** processes may compromise data privacy,cause communication delay,and incur high communication *** these limitations,we propose an intrusion detection method for AMI system based on federated learning(FL).The intrusion detection system is deployed in the data concentrators for training,and only its model parameters are communicated to the data ***,the data center distributes the learning to each data concentrator through aggregation and weight assignments for collaborative *** optimized deep neural network(DNN)is exploited for this proposed method,and extensive experiments based on the NSL-KDD dataset are carried *** the results,this proposed method improves detection performance and reduces computation costs,communication delays,and communication overheads while guaranteeing data privacy.

关键词： Federated learning(FL) advanced metering infrastructure(AMI)system intrusion detection data concentrator

Internet of things intrusion detection model and algorithm based on cloud computing and multi-feature extraction extreme learning machine

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Digital Communications and Networks 2023年第1期9卷 111-124页

作者： Haifeng Lin Qilin Xue Jiayin Feng Di Bai Nanjing Forestry University China Nanjing Agricultural University China Hebei Normal University of Science and Technology China

With the rapid development of the Internet of Things(IoT),there are several challenges pertaining to security in IoT *** with the characteristics of the traditional Internet,the IoT has many problems,such as large assets,complex and diverse structures,and lack of computing *** network intrusion detection systems cannot meet the security needs of IoT *** view of this situation,this study applies cloud computing and machine learning to the intrusion detection system of IoT to improve detection ***,traditional intrusion detection algorithms require considerable time for training,and these intrusion detection algorithms are not suitable for cloud computing due to the limited computing power and storage capacity of cloud nodes;therefore,it is necessary to study intrusion detection algorithms with low weights,short training time,and high detection accuracy for deployment and application on cloud *** appropriate classification algorithm is a primary factor for deploying cloud computing intrusion prevention systems and a prerequisite for the system to respond to intrusion and reduce intrusion *** paper discusses the problems related to IoT intrusion prevention in cloud computing *** on the analysis of cloud computing security threats,this study extensively explores IoT intrusion detection,cloud node monitoring,and intrusion response in cloud computing environments by using cloud computing,an improved extreme learning machine,and other *** use the Multi-Feature Extraction Extreme Learning Machine(MFE-ELM)algorithm for cloud computing,which adds a multi-feature extraction process to cloud servers,and use the deployed MFE-ELM algorithm on cloud nodes to detect and discover network intrusions to cloud *** our simulation experiments,a classical dataset for intrusion detection is selected as a test,and test steps such as data preprocessing,feature engineering,model training,an

关键词： Internet of Things Cloud Computing intrusion Prevention intrusion detection Extreme Learning Machine

Fooling intrusion detection systems using adversarially autoencoder

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Digital Communications and Networks 2021年第3期7卷 453-460页

作者： Junjun Chen Di Wu Ying Zhao Nabin Sharma Michael Blumenstein Shui Yu College of Information Science and Technology Beijing University of Chemical TechnologyBeijing100029China School of Computer Science University of Technology SydneyUltimo2007Australia Centre for Artificial Intelligence University of Technology SydneyUltimo2007Australia

Due to the increasing cyber-attacks,various intrusion detection Systems(IDSs)have been proposed to identify network *** existing machine learning-based IDSs learn patterns from the features extracted from network traffic flows,and the deep learning-based approaches can learn data distribution features from the raw data to differentiate normal and anomalous network *** having been used in the real world widely,the above methods are vulnerable to some types of *** this paper,we propose a novel attack framework,Anti-intrusion detection AutoEncoder(AIDAE),to generate features to disable the *** the proposed framework,an encoder transforms features into a latent space,and multiple decoders reconstruct the continuous and discrete features,***,a generative adversarial network is used to learn the flexible prior distribution of the latent *** correlation between continuous and discrete features can be kept by using the proposed training *** conducted on NSL-KDD,UNSW-NB15,and CICIDS2017 datasets show that the generated features indeed degrade the detection performance of existing IDSs dramatically.

关键词： intrusion detection Cyber attacks Autoencoder Generative adversarial networks

intrusion detection Method for Program Vulnerability via Library Calls

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Wuhan University Journal of Natural Sciences 2007年第1期12卷 126-130页

作者： DUAN Xuetao ZHONG Anming LI Ying JIA Chunfu College of Information Technology and Science Nankai University Tianjin 300071 China College of Information Science and Technology University of Science and Technology of China Hefei 230026Anhui China

Library function call sequence is the direct reflection of a program＇s behavior. The relationship between program vulnerability and library calls is analyzed, and an intrusion detection method via library calls is proposed, in which the short sequences of library call are used as signature profile. In this intrusion detection method, library interposition is used to hook library calls, and with the discussion of the features of the library call sequence in detail, an algorithm based on information-theory is applied to determine the appropriate length of the library call sequence. Experiments show good performance of our method against intrusions caused by the popular program vulnerabilities.

关键词： intrusion detection program vulnerability library call information entropy

A Novel Eccentric intrusion detection Model Based on Recurrent Neural Networks with Leveraging LSTM

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Computers, Materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha Krishnan Muthunambu Senthil Prabakaran Balasubramanian Prabhu Kavin Kishore Senthil Siruvangur Kavitha Chinnadurai Jehad Ali Department of Computer Applications Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India Department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the ***,this development has expanded the potential targets that hackers might *** adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or *** identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious *** research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)*** proposed model can identify various types of cyberattacks,including conventional and distinctive *** networks,a specific kind of feedforward neural networks,possess an intrinsic memory *** Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended *** such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual *** are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric intrusion detection *** model utilises Recurrent Neural Networks,specifically exploiting LSTM *** proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： Cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTM) CICIDS2019 dataset innovative cyberattacks

Online intrusion detection Mechanism Based on Model Migration in Intelligent Pumped Storage Power Stations

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China Communications 2023年第4期20卷 368-381页

作者： Yue Zong Yuanlin Luo Yuechao Wu Jiande Huang Bowen Yang Xiaoyu Kang Shumei Liu Yao Yu Power China Huadong Engineering Corporation Limited Hangzhou 311122China East China Tongbai Pumped Storage Power Generation Co. LtdTaizhou 317200China School of Computer Science and Engineering Northeastern UniversityShenyang 110169China

With the continuous integration of new energy into the power grid,various new attacks continue to emerge and the feature distributions are constantly changing during the deployment of intelligent pumped storage power *** intrusion detection model trained on the old data is hard to effectively identify new attacks,and it is difficult to update the intrusion detection model in time when lacking *** solve this issue,by using model-based transfer learning methods,in this paper we propose a convolutional neural network(CNN)based transfer online sequential extreme learning machine(TOS-ELM)scheme to enable the online intrusion detection,which is called CNN-TOSELM in this *** our proposed scheme,we use pre-trained CNN to extract the characteristics of the target domain data as input,and then build online learning classifier TOS-ELM to transfer the parameter of the ELM classifier of the source *** results show the proposed CNNTOSELM scheme can achieve better detection performance and extremely short model update time for intelligent pumped storage power stations.

关键词： transfer learning intrusion detection online classification

A new data normalization method for unsupervised anomaly intrusion detection

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Journal of Zhejiang University-Science C(Computers and Electronics) 2010年第10期11卷 778-784页

作者： Long-zheng CAI Jian CHEN Yun KE Yao CHEN Zhi-gang LI Engineering and Commerce College South-Central University for Nationalities Wuhan 430065 China Guangdong Institute of Science and Technology Zhuhai 519090 China

Unsupervised anomaly detection can detect attacks without the need for clean or labeled training *** paper studies the application of clustering to unsupervised anomaly detection(ACUAD).Data records are mapped to a feature *** are detected by determining which points lie in the sparse regions of the feature space.A critical element for this method to be effective is the definition of the distance function between data *** propose a unified normalization distance framework for records with numeric and nominal features mixed data.A heuristic method that computes the distance for nominal features is proposed,taking advantage of an important characteristic of nominal features-their probability ***,robust methods are proposed for mapping numeric features and computing their distance,these being able to tolerate the impact of the value difference in scale and diversification among features,and outliers introduced by *** experiments with the KDD 1999 dataset showed that ACUAD can detect intrusions with relatively low false alarm rates compared with other approaches.

关键词： Unsupervised anomaly detection Data mining intrusion detection Network security