An effect of chaos grasshopper optimization algorithm for protection of network infrastructure

Abstract Due to the proliferation of sophisticated cyber extortion with exponentially critical effects, intrusion detection system is being evolved systematically their revealing, understanding, attribution and mitigation capabilities. Unfortunately, most of the modern Intrusion Detection System (IDS) technique does not provide sufficient defense services in the wireless environment while maintaining operational continuity and the stability of the defense objective in the presence of intruders and modern attacks. To resolve this problem, we propose a new feature selection technique by combining Ensemble of Feature Selection (EFS) and Chaotic Adaptive Grasshopper Optimization Algorithm (CAGOA) method, called ECAGOA. The proposed method has the capability of preventing stagnation issue and is particularly credited to the following three aspects. Firstly, EFS method is applied for selecting the high ranked subset of attributes. Then, we have employed chaos concept in Grasshopper Optimization Algorithm (GOA) which generates a uniformly distributed population to enhance the quality of the initial populations and has the capability to manage two different issues such as the ability to search for new space termed as exploration and the ability to use existing space termed as exploitation in the optimization process. In order to avoid local optima and premature convergence, lastly, an adaptive grasshopper optimization algorithm is developed by using organized parameter adaptation method. Furthermore, the adaptive behavior of GOA is applied to decide whether a record signifies an anomaly or not, differing from some approaches acquainted in the literature. Support vector machine (SVM) is used as a fitness function in the proposed method to choose the relevant features that can help classify the attacks accurately. In addition, it is also applied to optimize the penalty factor (C), kernel parameter (σ), and tube size (ϵ) of SVM method. The proposed algorithm is evaluated using three popular datasets: ISCX 2012, NSL-KDD and CIC-IDS2017. The evaluation results show that the proposed method outperformed several feature selection techniques from state-of-the-art methods in terms of detection rate, accuracy, and false alarm rate.

[1]  Mário A. T. Figueiredo,et al.  Efficient feature selection filters for high-dimensional data , 2012, Pattern Recognit. Lett..

[2]  Wei-Ning Yang,et al.  Constructing gene regulatory networks from microarray data using GA/PSO with DTW , 2012, Appl. Soft Comput..

[3]  Wenyong Wang,et al.  A new feature selection method based on a validity index of feature subset , 2017, Pattern Recognit. Lett..

[4]  Ying Chen,et al.  Hybrid Intrusion Detection with Weighted Signature Generation over Anomalous Internet Episodes , 2007, IEEE Transactions on Dependable and Secure Computing.

[5]  S. Ayyasamy,et al.  Efficient pattern matching algorithm for security and Binary Search Tree (BST) based memory system in Wireless Intrusion Detection System (WIDS) , 2020, Comput. Commun..

[6]  Hossam Faris,et al.  Binary dragonfly optimization for feature selection using time-varying transfer functions , 2018, Knowl. Based Syst..

[7]  Manu Vardhan,et al.  Implementation of adaptive scheme in evolutionary technique for anomaly-based intrusion detection , 2019, Evolutionary Intelligence.

[8]  Manu Vardhan,et al.  Incorporating evolutionary computation for securing wireless network against cyberthreats , 2020, The Journal of Supercomputing.

[9]  Li-Pei Wong,et al.  An ensemble of intelligent water drop algorithm for feature selection optimization problem , 2018, Appl. Soft Comput..

[10]  Xiangjian He,et al.  Detection of Denial-of-Service Attacks Based on Computer Vision Techniques , 2015, IEEE Transactions on Computers.

[11]  Manel Guerrero Zapata,et al.  A hybrid multiobjective RBF-PSO method for mitigating DoS attacks in Named Data Networking , 2015, Neurocomputing.

[12]  Amir Asif,et al.  Distributed-Graph-Based Statistical Approach for Intrusion Detection in Cyber-Physical Systems , 2018, IEEE Transactions on Signal and Information Processing over Networks.

[13]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[14]  Mohamed M. Abd-Eldayem.  A proposed HTTP service based IDS , 2014 .

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

[16]  Ahmed A. Ewees,et al.  Improved grasshopper optimization algorithm using opposition-based learning , 2018, Expert Syst. Appl..

[17]  Sachin Tripathi,et al.  An efficient feature selection based Bayesian and Rough set approach for intrusion detection , 2020, Appl. Soft Comput..

[18]  Jugal Kalita,et al.  Active learning to detect DDoS attack using ranked features , 2019, Comput. Commun..

[19]  Rossitza Setchi,et al.  Feature selection using Joint Mutual Information Maximisation , 2015, Expert Syst. Appl..

[20]  Nima Jafari Navimipour,et al.  Intrusion detection systems in the Internet of things: A comprehensive investigation , 2019, Comput. Networks.

[21]  Jugal K. Kalita,et al.  Packet and Flow Based Network Intrusion Dataset , 2012, IC3.

[22]  Alok Kumar Shukla,et al.  A hybrid framework for optimal feature subset selection , 2019, J. Intell. Fuzzy Syst..

[23]  Akhan Akbulut,et al.  Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic , 2020, Comput. Networks.

[24]  Shahram Babaie,et al.  A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection , 2018, Comput. Networks.

[25]  Ian F. Akyildiz,et al.  A survey on wireless multimedia sensor networks , 2007, Comput. Networks.

[26]  Ahmed Fathy,et al.  Recent meta-heuristic grasshopper optimization algorithm for optimal reconfiguration of partially shaded PV array , 2018, Solar Energy.

[27]  Hao Yu,et al.  Distributed Machine Learning on Smart-Gateway Network Towards Real-Time Indoor Data Analytics , 2017 .

[28]  Ali Bou Nassif,et al.  Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection , 2019, Comput. Networks.

[29]  Miad Faezipour,et al.  Enhancing Wireless Intrusion Detection Using Machine Learning Classification with Reduced Attribute Sets , 2018, 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC).

[30]  Claudio A. Perez,et al.  Gender Classification Based on Fusion of Different Spatial Scale Features Selected by Mutual Information From Histogram of LBP, Intensity, and Shape , 2013, IEEE Transactions on Information Forensics and Security.

[31]  Taufik Abrão,et al.  Network Anomaly Detection System using Genetic Algorithm and Fuzzy Logic , 2018, Expert Syst. Appl..

[32]  Nur Izura Udzir,et al.  Packet Header Anomaly Detection Using Statistical Analysis , 2014, SOCO-CISIS-ICEUTE.

[33]  Hadis Karimipour,et al.  Cyber intrusion detection by combined feature selection algorithm , 2019, J. Inf. Secur. Appl..

[34]  Dorothy E. Denning,et al.  An Intrusion-Detection Model , 1987, IEEE Transactions on Software Engineering.

[35]  Ali A. Ghorbani,et al.  Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization , 2018, ICISSP.

[36]  Ali Fanian,et al.  A real-time alert correlation method based on code-books for intrusion detection systems , 2020, Comput. Secur..

[37]  Lu Lv,et al.  A novel intrusion detection system based on an optimal hybrid kernel extreme learning machine , 2020, Knowl. Based Syst..

[38]  Alok Kumar Shukla,et al.  Building an Effective Approach toward Intrusion Detection Using Ensemble Feature Selection , 2019, Int. J. Inf. Secur. Priv..

[39]  Maninder Singh,et al.  Hybrid intrusion detection and signature generation using Deep Recurrent Neural Networks , 2019, Neural Computing and Applications.

[40]  Jiankun Hu,et al.  Evaluating host-based anomaly detection systems: A preliminary analysis of ADFA-LD , 2013, 2013 6th International Congress on Image and Signal Processing (CISP).

[41]  Wei-Yang Lin,et al.  Intrusion detection by machine learning: A review , 2009, Expert Syst. Appl..

[42]  Muhammad Tariq,et al.  Accurate detection of sitting posture activities in a secure IoT based assisted living environment , 2018, Future Gener. Comput. Syst..

[43]  Can Berk Kalayci,et al.  A survey of swarm intelligence for portfolio optimization: Algorithms and applications , 2018, Swarm Evol. Comput..

[44]  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.

[45]  Lothar Thiele,et al.  A Comparison of Selection Schemes Used in Evolutionary Algorithms , 1996, Evolutionary Computation.

[46]  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).

[47]  Mohammad Kazem Ebrahimpour,et al.  Ensemble of feature selection methods: A hesitant fuzzy sets approach , 2017, Appl. Soft Comput..

[48]  L. Javier García-Villalba,et al.  Adaptive artificial immune networks for mitigating DoS flooding attacks , 2018, Swarm Evol. Comput..

[49]  Mamun Bin Ibne Reaz,et al.  A survey of intrusion detection systems based on ensemble and hybrid classifiers , 2017, Comput. Secur..

[50]  Emin Anarim,et al.  An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks , 2005, Expert Syst. Appl..

[51]  Manu Vardhan,et al.  Defense against distributed DoS attack detection by using intelligent evolutionary algorithm , 2020, International Journal of Computers and Applications.

[52]  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..

[53]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[54]  Pietro Sabatino,et al.  Ensemble based collaborative and distributed intrusion detection systems: A survey , 2016, J. Netw. Comput. Appl..

[55]  Iraj Mahdavi,et al.  Anomaly network-based intrusion detection system using a reliable hybrid artificial bee colony and AdaBoost algorithms , 2019, J. King Saud Univ. Comput. Inf. Sci..

[56]  Ruby Sharma,et al.  An Enhanced Approach to Fuzzy C-means Clustering for Anomaly Detection , 2018 .

[57]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[58]  Manu Vardhan,et al.  An adaptive inertia weight teaching-learning-based optimization algorithm and its applications , 2020 .

[59]  Rahul Dubey,et al.  Robust heart rate estimation from multimodal physiological signals using beat signal quality index based majority voting fusion method , 2017, Biomed. Signal Process. Control..

[60]  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..

[61]  M. Hemalatha,et al.  Effective approach toward Intrusion Detection System using data mining techniques , 2014 .

[62]  Arezoo Zakeri,et al.  Efficient feature selection method using real-valued grasshopper optimization algorithm , 2019, Expert Syst. Appl..

[63]  R. Vijayanand,et al.  Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection , 2018, Comput. Secur..

[64]  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.

[65]  Kangfeng Zheng,et al.  Feature selection method with joint maximal information entropy between features and class , 2018, Pattern Recognit..

[66]  Xuehua Zhao,et al.  An improved grasshopper optimization algorithm with application to financial stress prediction , 2018, Applied Mathematical Modelling.

[67]  Sankalap Arora,et al.  Chaotic grasshopper optimization algorithm for global optimization , 2019, Neural Computing and Applications.

[68]  Andrew Lewis,et al.  Grasshopper Optimisation Algorithm: Theory and application , 2017, Adv. Eng. Softw..

[69]  Kwangjo Kim,et al.  Deep Abstraction and Weighted Feature Selection for Wi-Fi Impersonation Detection , 2018, IEEE Transactions on Information Forensics and Security.

[70]  Sanjeev Kumar Pippal,et al.  An empirical evaluation of teaching–learning-based optimization, genetic algorithm and particle swarm optimization , 2019, International Journal of Computers and Applications.