A Literature Survey on the Importance of Intrusion Detection System for Wireless Networks


  • D. Selvamani Assistant Professor, Department of Computer Science, SIVET College, Gowrivakkam, Chennai, Tamil Nadu, India
  • V. Selvi Assistant Professor, Department of Computer Science, Mother Teresa Women’s University, Kodaikanal, Tamil Nadu, India




Network Security, Cloud Computing, Sensor Networks, Ad Hoc Networks, Internet of Things


Network security has become more important to personal computer users, organizations, and the military. With the advent of the internet, security became a major concern and the history of security allows a better understanding of the emergence of security technology. The entire field of network security is vast and in an evolutionary stage. The range of study encompasses a brief history dating back to internet’s beginnings and the current development in network security. In order to understand the research being performed today, background knowledge of the importance of security, types of attacks in the networks. This paper elaborates theliterature study on network security in various domains in the year 2013 to 2018. Finally, it summarizes the research directions by literature survey.


M. Elbasiony, Reda, et al, “A hybrid network intrusion detection framework based on random forests and weighted k-means,” Ain Shams Engineering Journal, Vol. 4, No. 4, pp.753-762, 2013.

S. A. Joshi and Varsha S. Pimprale, “Network Intrusion Detection System (NIDS) based on data mining,” International Journal of Engineering Science and Innovative Technology (IJESIT), Vol. 2, No. 1, pp. 95-98, 2013.

Sannasi Ganapathy, et al., “Intelligent feature selection and classification techniques for intrusion detection in networks: a survey,” EURASIP Journal on Wireless Communications and Networking, Vol.1, pp.271, 2013.

Louvieris, Panos, Natalie Clewley and Xiaohui Liu, “Effects-based feature identification for network intrusion detection,” Neurocomputing, Vol. 121, pp. 265-273, 2013.

Jaehak Yu, et al., “An in-depth analysis on traffic flooding attacks detection and system using data mining techniques,” Journal of Systems Architecture, Vol.59, No.10, pp.1005-1012, 2013.

Monowar H. Bhuyan, et al., “Detecting distributed denial of service attacks: methods, tools and future directions,” The Computer Journal, Vol.57, No.4, pp.537-556, 2013.

Iftikhar Ahmad, et al., “Enhancing SVM performance in intrusion detection using optimal feature subset selection based on genetic principal components,” Neural computing and applications, Vol.24, No.7-8, pp.1671-1682, 2014.

Wenying Feng, et al., “Mining network data for intrusion detection through combining SVMs with ant colony networks,” Future Generation Computer Systems, Vol.37, pp.127-140, 2014.

Li, Wenchao, et al., “A new intrusion detection system based on KNN classification algorithm in wireless sensor network,” Journal of Electrical and Computer Engineering, 2014.[10] Kuang, Fangjun, Weihong Xu and Siyang Zhang, “A novel hybrid KPCA and SVM with GA model for intrusion detection,” Applied Soft Computing, Vol.18, pp.178-184, 2014.

Roshan Chitrakar and Chuanhe Huang, “Selection of candidate support vectors in incremental SVM for network intrusion detection,” computers & security, Vol.45, pp.231-241, 2014.

G. V. Nadiammai and M. Hemalatha, “Effective approach toward Intrusion Detection System using data mining techniques,” Egyptian Informatics Journal, Vol.15, No.1, pp.37-50, 2014.

Gisung Kim, Seungmin Lee and Sehun Kim, “A novel hybrid intrusion detection method integrating anomaly detection with misuse detection,” Expert Systems with Applications, Vol.41, No. 4, pp. 1690-1700, 2014.

Shamshirband and Shahaboddin, et al., “Cooperative game theoretic approach using fuzzy Q-learning for detecting and preventing intrusions in wireless sensor networks,” Engineering Applications of Artificial Intelligence, Vol.32, pp.228-241, 2014.

Jeong, EunHee and ByungKwan Lee, “An IP Traceback Protocol using a Compressed Hash Table, a Sinkhole router and data mining based on network forensics against network attacks,” Future Generation Computer Systems, Vol.33, pp.42-52, 2014.

Shengyi Pan, Thomas Morris and Uttam Adhikari, “Developing a hybrid intrusion detection system using data mining for power systems,” IEEE Transactions on Smart Grid, Vol.6, No.6, pp.3104-3113, 2015.

Mustafa Amir Faisal, et al., “Data-Stream-Based Intrusion Detection System for Advanced Metering Infrastructure in Smart Grid: A Feasibility Study,” IEEE Systems journal, Vol.9, No.1, pp.31-44, 2015.

Elhag, Salma, et al, “On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on intrusion detection systems,” Expert Systems with Applications, Vol.42, No.1, pp.193-202, 2015.

Eesa, Adel Sabry, Zeynep Orman and Adnan Mohsin Abdulazeez Brifcani, “A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems,” Expert Systems with Applications ,Vol.42, No.5, pp.2670-2679.2015.

Alheeti, Khattab M. Ali, Anna Gruebler and Klaus D. McDonald-Maier, “An intrusion detection system against malicious attacks on the communication network of driverless cars,” Consumer Communications and Networking Conference (CCNC), 2015 12th Annual IEEE. IEEE, 2015.

Kelton AP Costa, et al., “A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks,” Information Sciences, Vol. 294, pp. 95-108, 2015.

Opeyemi Osanaiye, et al., “Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing,” EURASIP Journal on Wireless Communications and Networking, 2016. Vol.1, pp. 130.2016.

Vasan, K. Keerthi and B. Surendiran, “Dimensionality reduction using Principal Component Analysis for network intrusion detection,” Perspectives in Science, Vol.8, pp.510-512.2016.

Nathan Keegan, et al., “A survey of cloud-based network intrusion detection analysis,” Human-centric Computing and Information Sciences, Vol.6, No.1, pp.19, 2016.

Soo-Yeon Ji, et al., “A multi-level intrusion detection method for abnormal network behaviors,” Journal of Network and Computer Applications, Vol. 62, pp. 9-17, 2016.

Bamakan and Seyed Mojtaba Hosseini, et al., “An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization,” Neurocomputing, Vol. 199, pp.90-102, 2016.

Aburomman, Abdulla Amin and Mamun Bin Ibne Reaz, “A novel SVM-kNN-PSO ensemble method for intrusion detection system,” AppliedSoftComputing, Vol. 38, pp.360-372, 2016.

Ashfaq and Rana Aamir Raza, et al., “Fuzziness based semi-supervised learning approach for intrusion detection system,” InformationSciences, Vol. 378, pp. 484-497, 2017.

Kevric, Jasmin, Samed Jukic and Abdulhamit Subasi, “An effective combining classifier approach using tree algorithms for network intrusion detection,” Neural Computing and Applications, Vol. 28, No.1, pp.1051-1058, 2017.

Al-Yaseen, Wathiq Laftah, Zulaiha Ali Othman and Mohd Zakree Ahmad Nazri, “Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system,” Expert Systems with Applications Vol. 67, pp.296-303.2017.

Elike Hodo, et al., “Shallow and deep networks intrusion detection system: A taxonomy and survey,” arXivpreprintar, Vol. Xiv: 1701. 02145, 2017.

Thaseen, Ikram Sumaiya and Cherukuri Aswani Kumar, “Intrusion detection model using fusion of chi-square feature selection and multi class SVM,” Journal of King Saud University-Computer and Information Sciences, Vol. 29, No.4, pp.462-472, 2017.

MR Gauthama Raman, et al., “A hypergraph and arithmetic residue-based probabilistic neural network for classification in intrusion detection systems,” NeuralNetworks, Vol. 92, pp.89-97, 2017.

Abien Fred M. Agarap, “A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data,” Proceedings of the 2018 10th International Conference on Machine Learning and Computing, ACM, 2018.

Syed Ali Raza Shah and Biju Issac, “Performance comparison of intrusion detection systems and application of machine learning to Snort system,” Future Generation Computer Systems, Vol. 80, pp.157-170, 2018.

Weizhi Meng, et al., “Enhancing Trust Management for Wireless Intrusion Detection via Traffic Sampling in the Era of Big Data,” IeeeAccess, Vol. 6, pp.7234-7243, 2018.

Cabaj, Krzysztof, Marcin Gregorczyk and Wojciech Mazurczyk, “Software-defined networking-based crypto ransomware detection using HTTP traffic characteristics,” Computers & Electrical Engineering, Vol. 66, pp.353-368, 2018.

Yanfang Ye, et al., “DeepAM: a heterogeneous deep learning framework for intelligent malware detection,” KnowledgeandInformationSystems, Vol. 54, No.2, pp.265-285, 2018.

Sandeep Kumar Singh, et al., “Joint-Transformation-Based Detection of False Data Injection Attacks in Smart Grid,” IEEE Transactions on Industrial Informatics, Vol. 14, No.1, pp. 89-97, 2018.

Li, Longjie, et al., “Towards Effective Network Intrusion Detection: A Hybrid Model Integrating Gini Index and GBDT with PSO,” Journal of Sensors, 2018.

Demertzis, Konstantinos and Lazaros Iliadis, “A hybrid network anomaly and intrusion detection approach based on evolving spiking neural network classification,” International Conference on e-Democracy, Springer, Cham, 2013.

Igor Santos, et al., “Opcode sequences as representation of executables for data-mining-based unknown malware detection,” Information Sciences, Vol. 231, pp.64-82, 2013.

Cristian I. Pinzon, et al., “idMAS-SQL: intrusion detection based on MAS to detect and block SQL injection through data mining,” Information Sciences, Vol. 231, pp.15-31, 2013.

David Zhao, et al., “Botnet detection based on traffic behavior analysis and flow intervals,” Computers& Security Vol. 39, pp.2-16, 2013.

Yuxin Ding, et al., “A fast malware detection algorithm based on objective-oriented association mining,” computers &security, Vol.39, pp. 315-324, 2013.

Hubballi, Neminath and Vinoth Suryanarayanan, “False alarm minimization techniques in signature-based intrusion detection systems: A survey,” Computer Communications, Vol. 49, pp.1-17, 2014.

Gideon Creech, and Jiankun Hu, “A semantic approach to host-based intrusion detection systems using contiguousand discontiguous system call patterns,” IEEE Transactions on Computers, Vol. 63, No.4, pp. 807-819.

Wei Wang, et al., “Autonomic intrusion detection: Adaptively detecting anomalies over unlabeled audit data streams in computer networks,” Knowledge-Based Systems, Vol. 70, pp.103-117.2014.

Ammar Alazab, et al., “Using response action with intelligent intrusion detection and prevention system against web application malware,” Information Management & Computer Security, Vol. 22, No.5, pp.431-449, 2014.

Ismaila Idris and Ali Selamat, “Improved email spam detection model with negative selection algorithm and particle swarm optimization,” Applied Soft Computing, Vol. 22, pp.11-27.2014.[51] Youngjoon Ki, Eunjin Kim and Huy Kang Kim, “A novel approach to detect malware based on API call sequence analysis,” International Journal of Distributed Sensor Networks, Vol. 11, No. 6, pp. 659101, 2015.

Bhavin Shah and Bhushan H. Trivedi, “Improving performance of mobile agent based intrusion detection system,” Advanced Computing & Communication Technologies (ACCT), 2015 Fifth International Conference on IEEE, 2015.

Zhang, Ming, Boyi Xu and Jie Gong, “An anomaly detection model based on one-class svm to detect network intrusions,” Mobile Ad-hoc and Sensor Networks (MSN), 2015 11th International Conference on. IEEE, 2015.

Khamphakdee, Nattawat, Nunnapus Benjamas and Saiyan Saiyod, “Improving intrusion detection system based on snort rules for network probe attacks detection with association rules technique of data mining,” Journal of ICT Research and Applications, Vol. 8, No.3, pp. 234-250, 2015.

S. Sangeetha, et al., “Signature based semantic intrusion detection system on cloud,” Information Systems Design and Intelligent Applications. Springer, New Delhi, pp. 657-666, 2015.

Yujie Fan, Ye Yanfang and Lifei Chen, “Malicious sequential pattern mining for automatic malware detection,” Expert Systems with Applications, Vol. 52, pp. 16-25, 2016.

Galal, Hisham Shehata, Yousef Bassyouni Mahdy, and Mohammed Ali Atiea, “Behavior-based features model for malware detection,” Journal of Computer Virology and Hacking Techniques, Vol. 12, No.2, pp.59-67, 2016.

Akash Garg and Prachi Maheshwari, “Performance Analysis of Snort-based Intrusion Detection System,” Advanced Computing and Communication Systems (ICACCS), 3rd International Conference on IEEE, Vol. 1, 2016.

Kristof Böhmer and Stefanie Rinderle-Ma, “Automatic signature generation for anomaly detection in business process instance data,” Enterprise, Business-Process and Information Systems Modeling. Springer, Cham, pp.196-211, 2016.

Andrea Saracino, et al., “Madam: Effective and efficient behavior-based android malware detection and prevention,” IEEE Transactions on Dependable and Secure Computing, 2016.

Eduardo Viegas, et al., “Towards an energy-efficient anomaly-based intrusion detection engine for embedded systems,” IEEE Transactions on Computers, Vol. 66, No.1, pp.163-177, 2017.

Mirza M. Baig, Mian M. Awais and El-Sayed M. El-Alfy, “A multiclass cascade of artificial neural network for network intrusion

detection,” Journal of Intelligent & Fuzzy Systems, Vol. 32, No.4, pp.2875-2883, 2017.

Feng, Cheng, Tingting Li and Deeph Chana, “Multi-level anomaly detection in industrial control systems via package signatures and lstm networks,” Dependable Systems and Networks (DSN), 2017 47th Annual IEEE/IFIP International Conference on IEEE, 2017.

Weizhi Meng, et al., “A bayesian inference-based detection mechanism to defend medical smartphone networks against insider attacks,” Journal of Network and Computer Applications, Vol. 78, pp. 162-169, 2017.

Zhengbing Hu, et al., “Anomaly detection system in secure cloud computing environment,” International Journal of Computer Network and Information Security, Vol. 9, No.4, pp.10, 2017.

Eduardo K. Viegas, Altair O. Santin and Luiz S. Oliveira, “Toward a reliable anomaly-based intrusion detection in real-world environments,” ComputerNetworks, Vol. 127, pp. 200-216, 2017.

Aljawarneh, Shadi, Monther Aldwairi and Muneer Bani Yassein, “Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model,” Journal of Computational Science, Vol. 25, pp.152-160. 2018.

Hamamoto, Anderson Hiroshi, et al., “Network anomaly detection system using genetic algorithm and fuzzy logic,” Expert Systems with Applications, Vol. 92, pp. 390-402, 2018.

Yu Wang, et al., “A fog-based privacy-preserving approach for distributed signature-based intrusion detection,” Journal of Parallel and Distributed Computing, Vol. 122, pp. 26-35, 2018.

Cohen, Yehonatan, Danny Hendler and Amir Rubin, “Detection of malicious webmail attachments based on propagation patterns,” Knowledge-Based Systems, Vol. 141, pp. 67-79, 2018.

Mohsen Rezvani, “Assessment Methodology for Anomaly-Based Intrusion Detection in Cloud Computing,” Journal of AI and Data Mining, Vol. 6, No. 2, pp. 387-397, 2018.

Soroush M. Sohi, Fatemeh Ganji, and Jean-Pierre Seifert, “Recurrent Neural Networks for Enhancement of Signature-based Network Intrusion Detection Systems,” arXivpreprintarXiv: 1807.03212, 2018.

Hajisalem, Vajiheh, and Shahram Babaie, “A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection,” Computer Networks, Vol. 136, pp.37-50, 2018.

Sainis, Nachiket, Durgesh Srivastava, and Rajeshwar Singh, “Feature Classification and Outlier Detection to Increased Accuracy in Intrusion Detection System,” International Journal of Applied Engineering Research, Vol. 13, No.10, pp.7249-7255, 2018. 27




How to Cite

Selvamani, D., & Selvi, V. (2018). A Literature Survey on the Importance of Intrusion Detection System for Wireless Networks. Asian Journal of Computer Science and Technology, 7(3), 20–27. https://doi.org/10.51983/ajcst-2018.7.3.1905