Hybrid-Ids: An Approach for Intrusion Detection System with Hybrid Feature Extraction Technique Using Supervised Machine Learning
Keywords:
NIDS, HIDS, machine learning, supervise classification, network log dataset, KDDCUP99, feature extraction, feature selectionAbstract
At a breakneck pace, the IoT (i.e. Internet of Things) and networking technology, security has become a significant issue, such as data security, virtual machine hacking and various internal and external attacks. Conventional Intrusion Detection Systems (IDS) have a lot of limitations due to resource dependency and their complexity. Multiple researchers have implemented IDS systems with network logs or real-time network audit datasets. The KDDCUP99 and NSLKDD are the most popular datasets that existing authors use, but challenges persist in detecting unknown, active, passive, and others. In this paper, we proposed a heterogeneous extraction of attribute or feature and selection method for IDS, by using machine learning methodologies for the recognition of network intrusion as well as host intrusion. The numerous network log dataset has been used to detect the intruder in a vulnerable environment. The various heterogeneous feature extraction methods have been carried out for building a robust module. In the testing module, entire training rules validates the input packet data with training rules and executes the weight using a majority voting algorithm. Finally, it detects whether the current packet is normal or intruder based on majority voting values and eliminates that connection. In an extensive experimental analysis, three classifiers have been used for validation, such as ANN, SVM and RNN of different network log datasets. In observation, RNN produces the highest detection and classification accuracy over the SVM and ANN. It also reduces the time complexity and error rate with al datasets
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Ali, F.; Ali, A.; Imran, M.; Naqvi, R.A.; Siddiqi, M.H.; Kwak, K.S. Traffic accident detection and condition analysis based on social networking data. Accid. Anal. Prev. 2021, 151, 105973.
Sarkar, S.K.; Roy, S.; Alsentzer, E.; McDermott, M.B.A.; Falck, F.; Bica, I.; Adams, G.; Pfohl, S.; Hyland, S.L. Machine Learning for Health (ML4H) 2020: Advancing Healthcare for All. Proc. Mach. Learn. Res. 2020, 136, 1–11.
Ali, F.; El-Sappagh, S.; Islam, S.R.; Kwak, D.; Ali, A.; Imran, M.; Kwak, K.S. A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Inf. Fusion 2020, 63, 208–222.
Modi, C.; Patel, D.; Borisaniya, B.; Patel, H.; Patel, A.; Rajarajan, M. A survey of intrusion detection techniques in Cloud. J. Netw. Comput. Appl. 2013, 36, 42–57.
Wang, P.; Chao, K.; Lin, H.; Lin, W.; Lo, C. An Efficient Flow Control Approach for SDN-Based Network Threat Detection and Migration Using Support Vector Machine. In Proceedings of the 2016 IEEE 13th International Conference on e-Business Engineering (ICEBE), Macau, China, 4–6 November 2016; pp. 56–63.
Ikram, S.T.; Cherukuri, A.K. Improving Accuracy of Intrusion Detection Model Using PCA and optimized SVM. J. Comput. Inf. Technol. 2016, 24, 133–148.
Zolotukhin, M.; Hämäläinen, T.; Kokkonen, T.; Niemelä, A.; Siltanen, J. Data Mining Approach for Detection of DDoS Attacks Utilizing SSL/TLS Protocol. In Internet of Things, Smart Spaces, and Next Generation Networks and Systems; Balandin, S., Andreev, S., Koucheryavy, Y., Eds.; Springer: Cham, Switzerland, 2015; pp. 274–285.
Mehr, S.Y.; Ramamurthy, B. An SVM Based DDoS Attack Detection Method for Ryu SDN Controller. In Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Orlando, FL, USA, 9–12 December 2019; pp. 72–73.
Dey, S.K.; Rahman, M.M. Effects of Machine Learning Approach in Flow-Based Anomaly Detection on Software-Defined Networking. Symmetry 2020, 12, 7.
Khan, F.A.; Gumaei, A.; Derhab, A.; Hussain, A. A Novel Two-Stage Deep Learning Model for Efficient Network Intrusion Detection. IEEE Access 2019, 7, 30373–30385.
Malaiya, R.K.; Kwon, D.; Suh, S.C.; Kim, H.; Kim, I.; Kim, J. An Empirical Evaluation of Deep Learning for Network Anomaly Detection. IEEE Access 2019, 7, 140806–140817.
Yang Jia, M.W.; Wang, Y. Network intrusion detection algorithm based on deep neural network. IET Inf. Secur. 2019, 13, 48–53.
Yang, Y.; Zheng, K.; Wu, B.; Yang, Y.; Wang, X. Network Intrusion Detection Based on Supervised Adversarial Variational Auto-Encoder With Regularization. IEEE Access 2020, 8, 42169–42184.
Andresini, G.; Appice, A.; Mauro, N.D.; Loglisci, C.; Malerba, D. Multi-Channel Deep Feature Learning for Intrusion Detection. IEEE Access 2020, 8, 53346–53359.
Saikat Bose, Tripti Arjariya, Anirban Goswami, Soumit Chowdhury Multi-Layer Digital Validation of Candidate Service Appointment with Digital Signature and Bio-Metric Authentication Approach International Journal of Computer Networks & Communications (IJCNC) Vol.14, No.5, September 2022 DOI:10.5121/ijcnc.2022.14506
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