A comprehensive survey and taxonomy of the SVM-based intrusion detection systems
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Amir Masoud Rahmani | Tarik A. Rashid | Mokhtar Mohammadi | Moazam Bidaki | Quan Thanh Tho | Mehdi Hosseinzadeh | Sarkhel H. Taher Karim | Adil Hussain Mohammed Aldalwie | M. Hosseinzadeh | A. Rahmani | Q. T. Tho | M. Mohammadi | Moazam Bidaki | S. H. Karim | Tarik A. Rashid | Adil Hussein Mohammed Aldalwie
[1] Wu Liu,et al. Intrusion Detection Using SVM , 2011, 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing.
[2] S. Mercy Shalinie,et al. Detection of DDoS attacks using Enhanced Support Vector Machines with real time generated dataset , 2011, 2011 Third International Conference on Advanced Computing.
[3] Konrad Rieck,et al. A close look on n-grams in intrusion detection: anomaly detection vs. classification , 2013, AISec.
[4] Jingbo Yuan,et al. Intrusion Detection Model Based on Improved Support Vector Machine , 2010, 2010 Third International Symposium on Intelligent Information Technology and Security Informatics.
[5] Huang Chuanhe,et al. Anomaly detection using Support Vector Machine classification with k-Medoids clustering , 2012, 2012 Third Asian Himalayas International Conference on Internet.
[6] Namita Mittal,et al. Hybrid Approach for Detection of Anomaly Network Traffic using Data Mining Techniques , 2012 .
[7] Claudia Szabo,et al. Adaptive Performance Anomaly Detection in Distributed Systems Using Online SVMs , 2020, IEEE Transactions on Dependable and Secure Computing.
[8] Xiangliang Zhang,et al. Abstracting massive data for lightweight intrusion detection in computer networks , 2016, Inf. Sci..
[10] Mansour Sheikhan,et al. Hybrid of binary gravitational search algorithm and mutual information for feature selection in intrusion detection systems , 2015, Soft Computing.
[11] Zhong Jin,et al. A novel SVM by combining kernel principal component analysis and improved chaotic particle swarm optimization for intrusion detection , 2014, Soft Computing.
[12] Xingyu Gong,et al. Feature selection method for network intrusion based on GQPSO attribute reduction , 2011, 2011 International Conference on Multimedia Technology.
[13] LeeSeungmin,et al. A novel hybrid intrusion detection method integrating anomaly detection with misuse detection , 2014 .
[14] Bin Gu,et al. New Incremental Learning Algorithm for Semi-Supervised Support Vector Machine , 2018, KDD.
[15] Jun Gao,et al. Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection , 2014, IEEE Transactions on Cybernetics.
[16] Dan Pei,et al. Opprentice: Towards Practical and Automatic Anomaly Detection Through Machine Learning , 2015, Internet Measurement Conference.
[17] P. R. Devale,et al. Decision tree based Support Vector Machine for Intrusion Detection , 2010, 2010 International Conference on Networking and Information Technology.
[18] Raja Azlina Raja Mahmood,et al. Feature Selection Based on Genetic Algorithm and SupportVector Machine for Intrusion Detection System , 2013 .
[19] H. Gharaee,et al. A novel hybrid anomaly based intrusion detection method , 2012, 6th International Symposium on Telecommunications (IST).
[20] Jie Gu,et al. An effective intrusion detection framework based on SVM with feature augmentation , 2017, Knowl. Based Syst..
[21] Alexander J. Smola,et al. Online learning with kernels , 2001, IEEE Transactions on Signal Processing.
[22] Tianlong Gu,et al. The Intrusion Detection Model based on Parallel Multi - Artificial Bee Colony and Support Vector Machine , 2019, 2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI).
[23] Na Wang,et al. A Hybrid Cloud Intrusion Detection Method Based on SDAE and SVM , 2019, 2019 12th International Conference on Intelligent Computation Technology and Automation (ICICTA).
[24] Suleiman Idris,et al. Intrusion Detection System Based on Support Vector Machine Optimised with Cat Swarm Optimization Algorithm , 2019, 2019 2nd International Conference of the IEEE Nigeria Computer Chapter (NigeriaComputConf).
[25] Victor Valeriu Patriciu,et al. Intrusions detection based on Support Vector Machine optimized with swarm intelligence , 2014, 2014 IEEE 9th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI).
[26] Valentin Sgarciu,et al. A feature selection approach implemented with the Binary Bat Algorithm applied for intrusion detection , 2015, 2015 38th International Conference on Telecommunications and Signal Processing (TSP).
[27] I. Sumaiya Thaseen,et al. Intrusion detection model using fusion of PCA and optimized SVM , 2014, 2014 International Conference on Contemporary Computing and Informatics (IC3I).
[28] Xie Yong,et al. An intelligent anomaly analysis for intrusion detection based on SVM , 2012, 2012 International Conference on Computer Science and Information Processing (CSIP).
[29] Jie Gu,et al. A novel approach to intrusion detection using SVM ensemble with feature augmentation , 2019, Comput. Secur..
[30] Xin Zhang,et al. The Application of Machine Learning Methods to Intrusion Detection , 2012, 2012 Spring Congress on Engineering and Technology.
[31] Yinhui Li,et al. An efficient intrusion detection system based on support vector machines and gradually feature removal method , 2012, Expert Syst. Appl..
[32] A. Kannan,et al. Intrusion detection using optimal genetic feature selection and SVM based classifier , 2015, 2015 3rd International Conference on Signal Processing, Communication and Networking (ICSCN).
[33] Valentin Sgarciu,et al. Intelligent feature selection method rooted in Binary Bat Algorithm for intrusion detection , 2015, 2015 IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics.
[34] Hong Gu,et al. Anomaly detection combining one-class SVMs and particle swarm optimization algorithms , 2010 .
[35] Zheng Wang,et al. Network signal processing and intrusion detection by a hybrid model of LSSVM and PSO , 2013, 2013 15th IEEE International Conference on Communication Technology.
[36] Jiankun Hu,et al. A novel statistical technique for intrusion detection systems , 2018, Future Gener. Comput. Syst..
[37] Lei Li,et al. Fuzzy Multi-class Support Vector Machine Based on Binary Tree in Network Intrusion Detection , 2010, 2010 International Conference on Electrical and Control Engineering.
[38] AbrahamAjith,et al. Modeling intrusion detection system using hybrid intelligent systems , 2007 .
[39] Seyed Mojtaba Hosseini Bamakan,et al. An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization , 2016, Neurocomputing.
[40] Dong Liang,et al. A Clustering-SVM Ensemble Method for Intrusion Detection System , 2019, 2019 8th International Symposium on Next Generation Electronics (ISNE).
[41] Xiangji Huang,et al. Mining network data for intrusion detection through combining SVMs with ant colony networks , 2014, Future Gener. Comput. Syst..
[42] Yuan-Hai Shao,et al. Laplacian smooth twin support vector machine for semi-supervised classification , 2013, International Journal of Machine Learning and Cybernetics.
[43] Muhammad Hussain,et al. Optimized intrusion detection mechanism using soft computing techniques , 2013, Telecommun. Syst..
[44] Chou-Yuan Lee,et al. An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection , 2012, Appl. Soft Comput..
[45] Wei Xu,et al. Incremental SVM based on reserved set for network intrusion detection , 2011, Expert Syst. Appl..
[46] Manish Tiwari,et al. Novel Approach of Intrusion Detection Classification Deeplearning Using SVM , 2020 .
[47] S. Mercy Shalinie,et al. Real time detection and classification of DDoS attacks using enhanced SVM with string kernels , 2011, 2011 International Conference on Recent Trends in Information Technology (ICRTIT).
[48] Sheng-Hsun Hsu,et al. Application of SVM and ANN for intrusion detection , 2005, Comput. Oper. Res..
[49] Wathiq Laftah Al-Yaseen,et al. Real-time multi-agent system for an adaptive intrusion detection system , 2017, Pattern Recognit. Lett..
[50] Yu-Lin He,et al. Fuzziness based semi-supervised learning approach for intrusion detection system , 2017, Inf. Sci..
[51] Hui Wang,et al. A novel intrusion detection method based on improved SVM by combining PCA and PSO , 2011, Wuhan University Journal of Natural Sciences.
[52] Slobodan Petrovic,et al. A Comparison of Feature-Selection Methods for Intrusion Detection , 2010, MMM-ACNS.
[53] Brahim Belhaouari Samir,et al. An approach towards intrusion detection using PCA feature subsets and SVM , 2012, 2012 International Conference on Computer & Information Science (ICCIS).
[54] Neeraj Kumar,et al. Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid , 2016, IEEE Transactions on Industrial Informatics.
[55] Mamun Bin Ibne Reaz,et al. A novel SVM-kNN-PSO ensemble method for intrusion detection system , 2016, Appl. Soft Comput..
[56] Sami Bourouis,et al. A Real Time Adaptive Intrusion Detection Alert Classifier for High Speed Networks , 2013, 2013 IEEE 12th International Symposium on Network Computing and Applications.
[57] Peisheng Pan,et al. A Hybrid Intrusion Detection Method Based on Improved Fuzzy C-Means and Support Vector Machine , 2019, 2019 International Conference on Communications, Information System and Computer Engineering (CISCE).
[58] Fei Wang,et al. A Model Based on Hybrid Support Vector Machine and Self-Organizing Map for Anomaly Detection , 2010, 2010 International Conference on Communications and Mobile Computing.
[59] Naiqi Wu,et al. SVM-DT-based adaptive and collaborative intrusion detection , 2018, IEEE/CAA Journal of Automatica Sinica.
[60] Lei Li,et al. A New Intrusion Detection System Based on Rough Set Theory and Fuzzy Support Vector Machine , 2011, 2011 3rd International Workshop on Intelligent Systems and Applications.
[61] Zhenyu Liu,et al. A method of SVM with Normalization in Intrusion Detection , 2011 .
[62] Valentin Sgarciu,et al. An Improved Bat Algorithm Driven by Support Vector Machines for Intrusion Detection , 2015, CISIS-ICEUTE.
[63] Abdorasoul Ghasemi,et al. Learning a new distance metric to improve an SVM-clustering based intrusion detection system , 2015, 2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP).
[64] Vinay Kumar,et al. SVM Hyper-Parameters Optimization using Multi-PSO for Intrusion Detection , 2020, Social Networking and Computational Intelligence.
[65] Muhammad Hussain,et al. Enhancing SVM performance in intrusion detection using optimal feature subset selection based on genetic principal components , 2014, Neural Computing and Applications.
[66] Yongjun Zhang,et al. A novel approach to intrusion detection base on fast incremental SVM , 2012, Proceedings of 2012 2nd International Conference on Computer Science and Network Technology.
[67] Jun Wang,et al. A real time IDSs based on artificial Bee Colony-support vector machine algorithm , 2010, Third International Workshop on Advanced Computational Intelligence.
[68] A Jaya Lakshmi,et al. Optimized feature selection with k-means clustered triangle SVM for Intrusion Detection , 2011, 2011 Third International Conference on Advanced Computing.
[69] Valentin Sgarciu,et al. Enhanced intrusion detection system based on bat algorithm-support vector machine , 2014, 2014 11th International Conference on Security and Cryptography (SECRYPT).
[70] Mamun Bin Ibne Reaz,et al. Ensemble of binary SVM classifiers based on PCA and LDA feature extraction for intrusion detection , 2016, 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC).
[71] Cherukuri Aswani Kumar,et al. Intrusion detection model using fusion of chi-square feature selection and multi class SVM , 2017, J. King Saud Univ. Comput. Inf. Sci..
[72] Huazhong Wang,et al. Application of velocity adaptive shuffled frog leaping bat algorithm in ICS intrusion detection , 2017, 2017 29th Chinese Control And Decision Conference (CCDC).
[73] Ashraf Darwish,et al. Principle components analysis and Support Vector Machine based Intrusion Detection System , 2010, 2010 10th International Conference on Intelligent Systems Design and Applications.
[74] Shifei Ding,et al. An overview on semi-supervised support vector machine , 2017, Neural Computing and Applications.
[75] Han Yu,et al. Research on Network Intrusion Detection Based on Support Vector Machine Optimized with Grasshopper Optimization Algorithm , 2019, 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS).
[76] Tina Manghnani,et al. Computational CBGSA - SVM Model for Network Based Intrusion Detection System , 2019, ATIS.
[77] Lasith Yasakethu,et al. Anomaly Detection via One Class SVM for Protection of SCADA Systems , 2013, 2013 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery.
[78] Mangui Liang,et al. A new intrusion detection method based on SVM with minimum within-class scatter , 2013, Secur. Commun. Networks.
[79] Roshan Ramakrishna Naik,et al. Principle component analysis based intrusion detection system using support vector machine , 2016, 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT).
[80] Mingwei Zhao,et al. Feature Selection and Design of Intrusion Detection System Based on k-Means and Triangle Area Support Vector Machine , 2010, 2010 Second International Conference on Future Networks.
[81] Hongbing Wang,et al. Research of intrusion detection algorithm based on parallel SVM on spark , 2017, 2017 7th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC).
[82] Abhishek Tiwari,et al. Design and Analysis of Intrusion Detection System via Neural Network, SVM, and Neuro-Fuzzy , 2018, Advances in Intelligent Systems and Computing.
[83] Guan Xiaoqing,et al. Network intrusion detection method based on Agent and SVM , 2010, 2010 2nd IEEE International Conference on Information Management and Engineering.
[84] Shi-Jinn Horng,et al. A novel intrusion detection system based on hierarchical clustering and support vector machines , 2011, Expert Syst. Appl..
[85] Zhang Ya Ming,et al. Network intrusion detection method by least squares support vector machine classifier , 2010, 2010 3rd International Conference on Computer Science and Information Technology.
[86] M. Udayapal Reddy. Network Intrusion Detection Using Multiclass Support Vector Machine , 2010 .
[87] Jiankang Guo,et al. An Intrusion Detection Method Based on Multiple Kernel Support Vector Machine , 2011, 2011 International Conference on Network Computing and Information Security.
[88] Xiangjian He,et al. Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm , 2016, IEEE Transactions on Computers.
[89] Mohammad Javad Golkar,et al. A hybrid method consisting of GA and SVM for intrusion detection system , 2016, Neural Computing and Applications.
[90] Ke Liu,et al. A Novel Approach of KPCA and SVM for Intrusion Detection , 2012 .
[91] Huaping Liu,et al. A New Intelligent Intrusion Detection Method Based on Attribute Reduction and Parameters Optimization of SVM , 2010, 2010 Second International Workshop on Education Technology and Computer Science.
[92] Q. Henry Wu,et al. Online training of support vector classifier , 2003, Pattern Recognit..
[93] Valentin Sgarciu,et al. Anomaly Intrusions Detection Based on Support Vector Machines with an Improved Bat Algorithm , 2015, 2015 20th International Conference on Control Systems and Computer Science.
[94] Jun Su,et al. Application of Support Vector Machine Model Based on an Improved Elephant Herding Optimization Algorithm in Network Intrusion Detection , 2019 .
[95] K. V. N. Sunitha,et al. Effective discriminant function for intrusion detection using SVM , 2016, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).
[96] Ting Zhu,et al. An Optimization Method for Parameters of SVM in Network Intrusion Detection System , 2016, 2016 International Conference on Distributed Computing in Sensor Systems (DCOSS).
[97] Asif Ekbal,et al. Genetic algorithm combined with support vector machine for building an intrusion detection system , 2012, ICACCI '12.
[98] Lin Li,et al. Industrial communication intrusion detection algorithm based on improved one-class SVM , 2015, 2015 World Congress on Industrial Control Systems Security (WCICSS).
[99] Siyang Zhang,et al. A novel hybrid KPCA and SVM with GA model for intrusion detection , 2014, Appl. Soft Comput..
[100] Arputharaj Kannan,et al. Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM , 2012, Comput. Intell. Neurosci..
[101] Chuanhe Huang,et al. Selection of Candidate Support Vectors in incremental SVM for network intrusion detection , 2014, Comput. Secur..
[102] K. L. Shunmuganathan,et al. Multi-Agent-Based Anomaly Intrusion Detection , 2011, Inf. Secur. J. A Glob. Perspect..
[103] Dewan Md Farid,et al. Feature selection and intrusion classification in NSL-KDD cup 99 dataset employing SVMs , 2014, The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014).
[104] Mamun Bin Ibne Reaz,et al. A novel weighted support vector machines multiclass classifier based on differential evolution for intrusion detection systems , 2017, Inf. Sci..
[105] Mohammad Pourmahmood Aghababa,et al. Improving intrusion detection using a novel normalization method along with the use of harmony search algorithm for feature selection , 2015, 2015 7th Conference on Information and Knowledge Technology (IKT).
[106] P. Anitha,et al. RETRACTED ARTICLE: Oppositional based Laplacian grey wolf optimization algorithm with SVM for data mining in intrusion detection system , 2019, Journal of Ambient Intelligence and Humanized Computing.
[107] R. Remya,et al. A hybrid method based on genetic algorithm, self-organised feature map, and support vector machine for better network anomaly detection , 2013, 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT).
[108] Shailendra Singh,et al. An IWD-based feature selection method for intrusion detection system , 2017, Soft Computing.
[109] Xiaohong Guan,et al. An SVM-based machine learning method for accurate internet traffic classification , 2010, Inf. Syst. Frontiers.
[110] Chunying Cheng,et al. Network Intrusion Detection with Bat Algorithm for Synchronization of Feature Selection and Support Vector Machines , 2016, ISNN.
[111] Ayush Sharma,et al. Genetic Algorithm Based Feature Selection Algorithm for Effective Intrusion Detection in Cloud Networks , 2012, 2012 IEEE 12th International Conference on Data Mining Workshops.
[112] Beizhan Wang,et al. Feature selection based on Rough set and modified genetic algorithm for intrusion detection , 2010, 2010 5th International Conference on Computer Science & Education.
[113] Ming Zhang,et al. An Anomaly Detection Model Based on One-Class SVM to Detect Network Intrusions , 2015, 2015 11th International Conference on Mobile Ad-hoc and Sensor Networks (MSN).
[114] K. Chandrasekaran,et al. Distributed-Intrusion Detection System Using Combination of Ant Colony Optimization (ACO) and Support Vector Machine (SVM) , 2016, 2016 International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE).
[115] Hong Shen,et al. Application of online-training SVMs for real-time intrusion detection with different considerations , 2005, Comput. Commun..
[116] Christopher Leckie,et al. High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning , 2016, Pattern Recognit..
[117] V. S. Shankar Sriram,et al. An efficient intrusion detection system based on hypergraph - Genetic algorithm for parameter optimization and feature selection in support vector machine , 2017, Knowl. Based Syst..