A new approach for intrusion detection system based on training multilayer perceptron by using enhanced Bat algorithm

The most pressing issue in network security is the establishment of an approach that is capable of detecting violations in computer systems and networks. There have been several efforts for improving it from various points of view. One example is the improvement of the classification of packets on the network, which is imperative in detecting abnormal traffic and hence any potential intrusion. Thus, this study proposes a new approach for intrusion detection that is implemented using an enhanced Bat algorithm (EBat) for training an artificial neural network. The goal of the current study is to increase the accuracy of the classification for malicious and un-malicious network traffic. The proposed study herein includes a comparison with nine other metaheuristic algorithms (conventional and new algorithms) that are used to evaluate the new approach alongside the related works. Firstly, the EBat algorithm was developed and used to select suitable weights and biases. Next, the neural network was employed using the found optimal weights and biases to realize the intrusion detection approach. Four types of intrusion detection evaluation datasets were used to compare the proposed approach against the other algorithms. The findings revealed that the proposed method outperformed the other nine classification algorithms and it is unparalleled for the network intrusion detection.

[1]  Zulaiha Ali Othman,et al.  Bees algorithm for feature selection in network anomaly detection , 2012 .

[2]  Seyed Mohammad Mirjalili.  How effective is the Grey Wolf optimizer in training multi-layer perceptrons , 2014, Applied Intelligence.

[3]  Aman Jantan,et al.  An enhanced Bat algorithm with mutation operator for numerical optimization problems , 2017, Neural Computing and Applications.

[4]  Christian Blum,et al.  An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training , 2007, Neural Computing and Applications.

[5]  Nimmy Cleetus,et al.  Multi-objective functions in particle swarm optimization for intrusion detection , 2014, 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[6]  Taeshik Shon,et al.  A hybrid machine learning approach to network anomaly detection , 2007, Inf. Sci..

[7]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[8]  Maninder Singh,et al.  Data mining-based integrated network traffic visualization framework for threat detection , 2014, Neural Computing and Applications.

[9]  Shalini Batra,et al.  Fuzzified Cuckoo based Clustering Technique for Network Anomaly Detection , 2017, Comput. Electr. Eng..

[10]  Mahmod S. Mahmod,et al.  A COMPARISON STUDY FOR INTRUSION DATABASE (KDD99, NSL-KDD) BASED ON SELF ORGANIZATION MAP (SOM) ARTIFICIAL NEURAL NETWORK , 2013 .

[11]  Gisbert Schneider,et al.  Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training , 2006, BMC Bioinformatics.

[12]  D.R. Hush,et al.  Progress in supervised neural networks , 1993, IEEE Signal Processing Magazine.

[13]  Nour Moustafa,et al.  UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set) , 2015, 2015 Military Communications and Information Systems Conference (MilCIS).

[14]  Andrew Lewis,et al.  Let a biogeography-based optimizer train your Multi-Layer Perceptron , 2014, Inf. Sci..

[15]  Gulshan Kumar,et al.  A Multi-objective Genetic Algorithm Based Approach for Effective Intrusion Detection Using Neural Networks , 2015, Intelligent Methods for Cyber Warfare.

[16]  Sung-Bae Cho,et al.  Evolutionary neural networks for anomaly detection based on the behavior of a program , 2005, IEEE Trans. Syst. Man Cybern. Part B.

[17]  Ali A. Ghorbani,et al.  Toward developing a systematic approach to generate benchmark datasets for intrusion detection , 2012, Comput. Secur..

[18]  Aman Jantan,et al.  Swarm intelligence and neural network for data classification , 2014, 2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014).

[19]  Mansour Sheikhan,et al.  Flow-based anomaly detection in high-speed links using modified GSA-optimized neural network , 2012, Neural Computing and Applications.

[20]  Anazida Zainal,et al.  Feature Selection Using Rough-DPSO in Anomaly Intrusion Detection , 2007, ICCSA.

[21]  Takashi Sato,et al.  Structure Design of Neural Networks Using Genetic Algorithms , 2001, Complex Syst..

[22]  Mehdi MORADI,et al.  A Neural Network Based System for Intrusion Detection and Classification of Attacks , 2004 .

[23]  Shreyash Tambe,et al.  EFFECTIVE DATA MINING USING NEURAL NETWORKS , 2016 .

[24]  Seyed Mojtaba Hosseini Bamakan,et al.  Ramp loss K-Support Vector Classification-Regression; a robust and sparse multi-class approach to the intrusion detection problem , 2017, Knowl. Based Syst..

[25]  Ruzhi Xu,et al.  Research intrusion detection based PSO-RBF classifier , 2011, 2011 IEEE 2nd International Conference on Software Engineering and Service Science.

[26]  Michael R. Lyu,et al.  A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training , 2007, Appl. Math. Comput..

[27]  Tomasz Imielinski,et al.  Database Mining: A Performance Perspective , 1993, IEEE Trans. Knowl. Data Eng..

[28]  Dervis Karaboga,et al.  Hybrid Artificial Bee Colony algorithm for neural network training , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[29]  Jill Slay,et al.  The Significant Features of the UNSW-NB15 and the KDD99 Data Sets for Network Intrusion Detection Systems , 2015, 2015 4th International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security (BADGERS).

[30]  Jill Slay,et al.  The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set , 2016, Inf. Secur. J. A Glob. Perspect..

[31]  Chaouki Khammassi,et al.  A GA-LR wrapper approach for feature selection in network intrusion detection , 2017, Comput. Secur..

[32]  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..

[33]  Zulaiha Ali Othman,et al.  Great Deluge Algorithm Feature Selection for Network Intrusion Detection , 2013 .

[34]  Siti Zaiton Mohd Hashim,et al.  Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm , 2012, Appl. Math. Comput..

[35]  WenJie Tian,et al.  Network intrusion detection analysis with neural network and particle swarm optimization algorithm , 2010, 2010 Chinese Control and Decision Conference.

[36]  Mohamed S. Kamel,et al.  RBF-based real-time hierarchical intrusion detection systems , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[37]  Ali A. Ghorbani,et al.  A Feature Classification Scheme For Network Intrusion Detection , 2007, Int. J. Netw. Secur..

[38]  Gaige Wang,et al.  A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization , 2013, J. Appl. Math..

[39]  AbrahamAjith,et al.  Metaheuristic design of feedforward neural networks , 2017 .

[40]  Bahari Belaton,et al.  Improving accuracy of applications fingerprinting on local networks using NMAP-AMAP-ETTERCAP as a hybrid framework , 2013, 2013 IEEE International Conference on Control System, Computing and Engineering.

[41]  Weijie Zhang,et al.  Prediction for network traffic of radial basis function neural network model based on improved particle swarm optimization algorithm , 2018, Neural Computing and Applications.

[42]  Mansour Sheikhan,et al.  Gravitational search algorithm–optimized neural misuse detector with selected features by fuzzy grids–based association rules mining , 2012, Neural Computing and Applications.

[43]  Arputharaj Kannan,et al.  Decision tree based light weight intrusion detection using a wrapper approach , 2012, Expert Syst. Appl..

[44]  Jasmin Kevric,et al.  An effective combining classifier approach using tree algorithms for network intrusion detection , 2017, Neural Computing and Applications.

[45]  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.

[46]  A. Jantan,et al.  NEW APPROACH TO IMPROVE ANOMALY DETECTION USING A NEURAL NETWORK OPTIMIZED BY HYBRID ABC AND PSO ALGORITHMS , 2018 .

[47]  Marjan Mernik,et al.  A parameter control method of evolutionary algorithms using exploration and exploitation measures with a practical application for fitting Sovova's mass transfer model , 2013, Appl. Soft Comput..

[48]  Václav Snásel,et al.  Metaheuristic design of feedforward neural networks: A review of two decades of research , 2017, Eng. Appl. Artif. Intell..

[49]  Aman Jantan,et al.  Hybridizing artificial bee colony with monarch butterfly optimization for numerical optimization problems , 2018, Neural Computing and Applications.

[50]  Carlos García Garino,et al.  An autonomous labeling approach to support vector machines algorithms for network traffic anomaly detection , 2012, Expert Syst. Appl..

[51]  Jung-Min Park,et al.  An overview of anomaly detection techniques: Existing solutions and latest technological trends , 2007, Comput. Networks.

[52]  Dorothy E. Denning,et al.  An Intrusion-Detection Model , 1986, 1986 IEEE Symposium on Security and Privacy.

[53]  Atilla Özgür,et al.  A review of KDD99 dataset usage in intrusion detection and machine learning between 2010 and 2015 , 2016, PeerJ Prepr..

[54]  Marjan Mernik,et al.  Exploration and exploitation in evolutionary algorithms: A survey , 2013, CSUR.

[55]  Salvatore J. Stolfo,et al.  A framework for constructing features and models for intrusion detection systems , 2000, TSEC.

[56]  Sokratis K. Katsikas,et al.  Intrusion Detection Using Evolutionary Neural Networks , 2008, 2008 Panhellenic Conference on Informatics.

[57]  Svein J. Knapskog,et al.  Attribute Normalization in Network Intrusion Detection , 2009, 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks.

[58]  Mohiuddin Ahmed,et al.  A survey of network anomaly detection techniques , 2016, J. Netw. Comput. Appl..

[59]  A. Jantan,et al.  USING HYBRID ARTIFICIAL BEE COLONY ALGORITHM AND PARTICLE SWARM OPTIMIZATION FOR TRAINING FEED-FORWARD NEURAL NETWORKS , 2014 .

[60]  Hamid H. Jebur,et al.  Identifying Generic Features of KDD Cup 1999 for Intrusion Detection , 2015 .

[61]  Abbas Sabbar Dakhil.  Biosynthesis of silver nanoparticle (AgNPs) using Lactobacillus and their effects on oxidative stress biomarkers in rats , 2017 .

[62]  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..

[63]  Ali A. Ghorbani,et al.  A detailed analysis of the KDD CUP 99 data set , 2009, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications.

[64]  Ge Yu,et al.  Method of evolutionary neural network-based intrusion detection , 2001, 2001 International Conferences on Info-Tech and Info-Net. Proceedings (Cat. No.01EX479).

[65]  Georgios Kambourakis,et al.  Swarm intelligence in intrusion detection: A survey , 2011, Comput. Secur..

[66]  Xiangliang Zhang,et al.  Profiling program behavior for anomaly intrusion detection based on the transition and frequency property of computer audit data , 2006, Comput. Secur..

[67]  Haitao Li,et al.  Research on prediction of traffic flow based on dynamic fuzzy neural networks , 2015, Neural Computing and Applications.

[68]  Nur Izura Udzir,et al.  Anomaly-based intrusion detection through K-means clustering and naives bayes classification , 2013 .

[69]  Kishan G. Mehrotra,et al.  Elements of artificial neural networks , 1996 .

[70]  Lan Shi,et al.  PCA-PSO-BP Neural Network Application in IDS , 2015 .

[71]  Adel Sabry Eesa,et al.  A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems , 2015, Expert Syst. Appl..

[72]  Mohammad Javad Golkar,et al.  A hybrid method consisting of GA and SVM for intrusion detection system , 2016, Neural Computing and Applications.

[73]  Hong Shen,et al.  Online training of SVMs for real-time intrusion detection , 2004, 18th International Conference on Advanced Information Networking and Applications, 2004. AINA 2004..

[74]  Li Zhang,et al.  A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks , 2016, Neural Computing and Applications.

[75]  F.H.F. Leung,et al.  Tuning of the structure and parameters of neural network using an improved genetic algorithm , 2001, IECON'01. 27th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.37243).

[76]  Teresa Bernarda Ludermir,et al.  Hybrid Training of Feed-Forward Neural Networks with Particle Swarm Optimization , 2006, ICONIP.

[77]  Dongkyoo Shin,et al.  A Study on Comparison of KDD CUP 99 and NSL-KDD Using Artificial Neural Network , 2017, CSA/CUTE.

[78]  Hossam Faris,et al.  Training feedforward neural networks using multi-verse optimizer for binary classification problems , 2016, Applied Intelligence.

[79]  ShiYong,et al.  An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization , 2016 .

[80]  Anup K. Ghosh,et al.  A Study in Using Neural Networks for Anomaly and Misuse Detection , 1999, USENIX Security Symposium.

[81]  Mohammad Khubeb Siddiqui,et al.  Analysis of KDD CUP 99 Dataset using Clustering based Data Mining , 2013 .

[82]  Chunlin Zhang,et al.  Comparison of BPL and RBF Network in Intrusion Detection System , 2003, RSFDGrC.

[83]  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..

[84]  Lifeng Xi,et al.  An Improved Particle Swarm Optimization for Evolving Feedforward Artificial Neural Networks , 2007, Neural Processing Letters.

[85]  Georgios Kambourakis,et al.  Dendron : Genetic trees driven rule induction for network intrusion detection systems , 2018, Future Gener. Comput. Syst..

[86]  Stefan C. Kremer,et al.  Network intrusion detection system based on recursive feature addition and bigram technique , 2018, Comput. Secur..

[87]  Tian Wang,et al.  Improved BP Neural Network for Intrusion Detection Based on AFSA , 2015 .

[88]  Bart Baesens,et al.  Editorial survey: swarm intelligence for data mining , 2010, Machine Learning.

[89]  Zulaiha Ali Othman,et al.  Improving Bee Algorithm Based Feature Selection in Intrusion Detection System Using Membrane Computing , 2014, J. Networks.

[90]  Weihua Gui,et al.  A Hybrid Multiobjective Differential Evolution Algorithm and Its Application to the Optimization of Grinding and Classification , 2013, J. Appl. Math..

[91]  Guifa Teng,et al.  Unsupervised SVM Based on p-kernels for Anomaly Detection , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

[92]  Dervis Karaboga,et al.  Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks , 2007, MDAI.

[93]  Mohammed Azmi Al-Betar,et al.  β\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}-Hill climbing: an exploratory local search , 2016, Neural Computing and Applications.

[94]  Aman Jantan,et al.  A New Approach Based on Honeybee to Improve Intrusion Detection System Using Neural Network and Bees Algorithm , 2011, ICSECS.

[95]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.