A comprehensive survey and taxonomy of the SVM-based intrusion detection systems

[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..