Intelligent Intrusion Detection System Through Combined and Optimized Machine Learning

In this paper, an existing rule-based intrusion detection system (IDS) is made more intelligent through the application of machine learning. Snort was chosen as it is an open source software and th...

[1]  E. Chang,et al.  State of the art review of the existing bayesian-network based approaches to trust and reputation computation , 2007, Second International Conference on Internet Monitoring and Protection (ICIMP 2007).

[2]  Saturnino Maldonado-Bascón,et al.  Model Selection for Support Vector Machines Using Ant Colony Optimization in an Electronic Nose Application , 2006, ANTS Workshop.

[3]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[4]  Milan Tuba,et al.  Support vector machine parameter tuning using firefly algorithm , 2016, 2016 26th International Conference Radioelektronika (RADIOELEKTRONIKA).

[5]  Joao Paulo Papa,et al.  Intrusion Detection System Based On Flows Using Machine Learning Algorithms , 2017, IEEE Latin America Transactions.

[6]  Georgios Kambourakis,et al.  Intrusion Detection in 802.11 Networks: Empirical Evaluation of Threats and a Public Dataset , 2016, IEEE Communications Surveys & Tutorials.

[7]  Niko Heikura.  Analyzing Offensive and Defensive Networking Tools in a Laboratory Environme , 2015 .

[8]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[9]  Anita Sahoo,et al.  Optimization of SVM classifier using Firefly algorithm , 2013, 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013).

[10]  Zhao Xin,et al.  Project financing risk assessment based on ACO and SVM , 2009, 2009 ISECS International Colloquium on Computing, Communication, Control, and Management.

[11]  Kwangjo Kim,et al.  Data Randomization and Cluster-Based Partitioning for Botnet Intrusion Detection , 2016, IEEE Transactions on Cybernetics.

[12]  Madan Gopal,et al.  A hybrid SVM based decision tree , 2010, Pattern Recognit..

[13]  Yuefei Zhu,et al.  A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks , 2017, IEEE Access.

[14]  Wei Hu,et al.  AdaBoost-Based Algorithm for Network Intrusion Detection , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[15]  Hyun-Chul Kim,et al.  Constructing support vector machine ensemble , 2003, Pattern Recognit..

[16]  Jean-Marie Flaus,et al.  A Deep Learning Approach for Intrusion Detection System in Industry Network , 2018, BDCSIntell.

[17]  Chulhyun Kim,et al.  Forecasting time series with genetic fuzzy predictor ensemble , 1997, IEEE Trans. Fuzzy Syst..

[18]  Quanyan Zhu,et al.  Distributed Privacy-Preserving Collaborative Intrusion Detection Systems for VANETs , 2018, IEEE Transactions on Signal and Information Processing over Networks.

[19]  Harry Zhang,et al.  Naive Bayes for optimal ranking , 2008, J. Exp. Theor. Artif. Intell..

[20]  Zulaiha Ali Othman,et al.  Public domain datasets for optimizing network intrusion and machine learning approaches , 2011, 2011 3rd Conference on Data Mining and Optimization (DMO).

[21]  Wolfgang Konen,et al.  SVM Ensembles Are Better When Different Kernel Types Are Combined , 2013, ECDA.

[22]  Kangfeng Zheng,et al.  Intrusion detection algorithm based on density, cluster centers, and nearest neighbors , 2016, China Communications.