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intrusion detection Methods Based on Incomplete RFID Traces

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Chinese Journal of Electronics 2017年第4期26卷 675-680页

作者： LI Peng WANG Zhen XU He ZHU Feng WANG Ruchuan School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks

According to the Radio frequency identification(RFID) tag cloning attack problems of RFID system and the existing problems of intrusion detection research based on incomplete RFID traces, this paper proposes a method of intrusion detection by combining the Markov chain technology and probability statistics technology. We use this method to calculate the threshold that is used to determine whether the tag event is a cloning tag intrusion one. Current research focuses on RFID intrusion detection under the condition of complete RFID traces, such as encryption algorithm improvement, tag authentication protocol improvement, and track and trace technology in RFID supply chain. RFID traces are occasionally to be incomplete, which causes the above methods to be inefficient. Our method proposed in this paper is the solution to incomplete RFID traces which makes up for the study of RFID intrusion detection research.

关键词： intrusion detection Radio frequency identification Information security Tag cloning attack Incomplete RFID traces

intrusion detection using rough set classification

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Journal of Zhejiang University Science 2004年第9期5卷 1076-1086页

作者：张连华张冠华郁郎张洁白英彩 DepartmentofComputerScienceandEngineering ShanghaiJlaotongUmversityShanghai200030China *** DepartmentofComputing HongKongPolytechnicUniversityHongKongChina DepartmentofComputerScienceandEngineering ShanghaiJiaotongUniversityShanghai200030China

Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learning algorithm, is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using Support Vector Machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of'IF-THEN' rules, which have the advantage of explication. Tests and comparison of RSC with SVM on DARPA benchmark data showed that for Probe and DoS attacks both RSC and SVM yielded highly accurate results (greater than 99% accuracy on testing set).

关键词： intrusion detection Rough set classification Support vector machine Genetic algorithm

intrusion detection System for Big Data Analytics in IoT Environment

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Computer Systems Science & Engineering 2022年第10期43卷 381-396页

作者： M.Anuradha G.Mani T.Shanthi N.R.Nagarajan P.Suresh C.Bharatiraja Department of Computer Science and Engineering St.Joseph’s College of EngineeringChennai600119India Department of Computer Science and Engineering University College of Engineering ArniThatchur632326India Department of Electronics&Communication Engineering Kings College of EngineeringPudukkottai613303India Department of Electronics&Communication Engineering K.Ramakrishnan College of EngineeringTiruchirapalli621112India Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India

In the digital area,Internet of Things(IoT)and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal *** IoT networks are popular and widely employed in real world applications,security in IoT networks remains a challenging *** intrusion detection systems(IDS)cannot be employed in IoT networks owing to the limitations in resources and ***,this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning(IMFSDL)based classification model,called IMFSDL-IDS for IoT *** proposed IMFSDL-IDS model involves data collection as the primary process utilizing the IoT devices and is preprocessed in two stages:data transformation and data *** manage big data,Hadoop ecosystem is ***,the IMFSDL-IDS model includes a hill climbing with moth flame optimization(HCMFO)for feature subset selection to reduce the complexity and increase the overall detection ***,the beetle antenna search(BAS)with variational autoencoder(VAE),called BAS-VAE technique is applied for the detection of intrusions in the feature reduced *** BAS algorithm is integrated into the VAE to properly tune the parameters involved in it and thereby raises the classification *** validate the intrusion detection performance of the IMFSDL-IDS system,a set of experimentations were carried out on the standard IDS dataset and the results are investigated under distinct *** resultant experimental values pointed out the betterment of the IMFSDL-IDS model over the compared models with the maximum accuracy 95.25%and 97.39%on the applied NSL-KDD and UNSW-NB15 dataset correspondingly.

关键词： Big data cybersecurity IoT networks intrusion detection deep learning metaheuristics intelligent systems

intrusion detection System Using FKNN and Improved PSO

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Computers, Materials & Continua 2021年第5期67卷 1429-1445页

作者： Raniyah Wazirali College of Computing and Informatics Saudi Electronic UniversitySaudi ArabiaRiyadh

intrusion detection system(IDS)techniques are used in cybersecurity to protect and safeguard sensitive *** increasing network security risks can be mitigated by implementing effective IDS methods as a defense *** proposed research presents an IDS model based on the methodology of the adaptive fuzzy k-nearest neighbor(FKNN)*** this method,two parameters,i.e.,the neighborhood size(k)and fuzzy strength parameter(m)were characterized by implementing the particle swarm optimization(PSO).In addition to being used for FKNN parametric optimization,PSO is also used for selecting the conditional feature subsets for *** proficiently regulate the indigenous and comprehensive search skill of the PSO approach,two control parameters containing the time-varying inertia weight(TVIW)and time-varying acceleration coefficients(TVAC)were applied to the *** addition,continuous and binary PSO algorithms were both executed on a multi-core *** proposed IDS model was compared with other state-of-the-art *** results of the proposed methodology are superior to the rest of the techniques in terms of the classification accuracy,precision,recall,and *** results showed that the proposed methods gave the highest performance scores compared to the other conventional algorithms in detecting all the attack types in two ***,the proposed method was able to obtain a large number of true positives and negatives,with minimal number of false positives and negatives.

关键词： FKNN PSO approach machine learning-based cybersecurity intrusion detection

intrusion detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

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Computers, Materials & Continua 2023年第1期74卷 801-815页

作者： Yongjie Yang Shanshan Tu Raja Hashim Ali Hisham Alasmary Muhammad Waqas Muhammad Nouman Amjad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Information Security and Cybersecurity Unit King Khalid UniversityAbhaSaudi Arabia Computer Engineering Department College of Information TechnologyUniversity of Bahrain32038Bahrain School of Engineering Edith Cowan UniversityJoondalup PerthWA6027Australia School of Engineering University of Management and TechnologyLahorePakistan

With the recent developments in the Internet of Things(IoT),the amount of data collected has expanded tremendously,resulting in a higher demand for data storage,computational capacity,and real-time processing *** computing has traditionally played an important role in establishing ***,fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility,location awareness,heterogeneity,scalability,low latency,and geographic ***,IoT networks are vulnerable to unwanted assaults because of their open and shared *** a result,various fog computing-based security models that protect IoT networks have been developed.A distributed architecture based on an intrusion detection system(IDS)ensures that a dynamic,scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is *** this study,we examined the time-related aspects of network traffic *** presented an intrusion detection model based on a twolayered bidirectional long short-term memory(Bi-LSTM)with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark *** showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy,precision,recall and F1 score.

关键词： Fog computing intrusion detection bi-LSTM attention mechanism

intrusion detection for Wireless Mesh Networks using Finite State Machine

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China Communications 2010年第5期7卷 40-48页

作者： Yi Ping Wu Yue Liu Ning Wang Zhiyang School of Information Security Engineering Shanghai Jiao Tong University Shanghai 200240 P. R. China Key Laboratory of Child Development and Learning Science of Ministry of Education Southeast University Nanjing 210096 China

Wireless Mesh Networks is vulnerable to attacks due to the open medium, dynamically changing network topology, cooperative algorithms, Lack of centralized monitoring and management point. The traditional way of protecting networks with firewalls and encryption software is no longer suffi- cient and effective for those features. In this paper, we propose a distributed intrusion detection ap- proach based on timed automata. 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 we con- struct the Finite State Machine （FSM） by the way of manually abstracting the correct behaviors of the node according to the routing protocol of Dynamic Source Routing （DSR）. The monitor nodes can verify every node＇s behavior by the Finite State Ma- chine （FSM）, and validly detect real-time attacks without signatures of intrusion or trained *** with the architecture where each node is its own IDS agent, our approach is much more efficient while maintaining the same level of effectiveness. Finally, we evaluate the intrusion detection method through simulation experiments.

关键词： wireless mesh networks security intrusion detection finite state machine

intrusion detection in the Internet of Things Using Fusion of GRU-LSTM Deep Learning Model

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Intelligent Automation & Soft Computing 2023年第8期37卷 2279-2290页

作者： Mohammad S.Al-kahtani Zahid Mehmood Tariq Sadad Islam Zada Gauhar Ali Mohammed ElAffendi Department of Computer Engineering Prince Sattam Bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer Engineering University of Engineering and TechnologyTaxila47050Pakistan The FAMLIR Group The University of LahoreLahore54000Pakistan Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan Faculty of Computing International Islamic UniversityIslamabad44000Pakistan EIAS Data Science and Blockchain Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

Cybersecurity threats are increasing rapidly as hackers use advanced *** a result,cybersecurity has now a significant factor in protecting organizational *** detection systems(IDSs)are used in networks to flag serious issues during network management,including identifying malicious traffic,which is a *** remains an open contest over how to learn features in IDS since current approaches use deep learning *** learning,which combines swarm intelligence and evolution,is gaining attention for further improvement against cyber *** this study,we employed a PSO-GA(fusion of particle swarm optimization(PSO)and genetic algorithm(GA))for feature selection on the CICIDS-2017 *** achieve better accuracy,we proposed a hybrid model called LSTM-GRU of deep learning that fused the GRU(gated recurrent unit)and LSTM(long short-term memory).The results show considerable improvement,detecting several network attacks with 98.86%accuracy.A comparative study with other current methods confirms the efficacy of our proposed IDS scheme.

关键词： Cyber security deep learning intrusion detection PSO-GA CICIDS-2017 intelligent system security and privacy IoT

intrusion detection Approach Using Connectionist Expert System

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Journal of Beijing Institute of Technology 2005年第4期14卷 467-470页

作者：马锐刘玉树杜彦辉 School of Software Beijing Institute of Technology Beijing100081 China Department of Information Security Science Chinese People's Public Security University Beijing100038 China

In order to improve the detection efficiency of rule-based expert systems, an intrusion detection approach using connectionist expert system is proposed. The approach converts the AND/OR nodes into the corresponding neurons, adopts the three layered feed forward network with full interconnection between layers, translates the feature values into the continuous values belong to the interval [0, 1], shows the confidence degree about intrusion detection rules using the weight values of the neural networks and makes uncertain inference with sigmoid function. Compared with the rule based expert system, the neural network expert system improves the inference efficiency.

关键词： intrusion detection neural networks expert system

intrusion detection Method Based on Improved Growing Hierarchical Self-Organizing Map

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Transactions of Tianjin University 2016年第4期22卷 334-338页

作者：张亚平布文秀苏畅王璐瑶许涵 School of Software Tianjin University School of Mathematical Science Nankai University

Considering that growing hierarchical self-organizing map(GHSOM) ignores the influence of individual component in sample vector analysis, and its accurate rate in detecting unknown network attacks is relatively lower, an improved GHSOM method combined with mutual information is proposed. After theoretical analysis, experiments are conducted to illustrate the effectiveness of the proposed method by accurately clustering the input data. Based on different clusters, the complex relationship within the data can be revealed effectively.

关键词： growing hierarchical self-organizing map(GHSOM) hierarchical structure mutual information intrusion detection network security