Network Intrusion Detection System Based on Packet Filters

S. Karthikeyan*, M. Keerthivasan **, A. Lalitha ***, R. Karan ****
*-**** Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kattankulathur, Tamil Nadu, India.
Periodicity:March - May'2021


Rapid advancements in the field of computer networking and information storage has spread through many aspects of business and, thus have prompted an expansion in improvements to prevent powerful attacks on computers through the networks. Intrusion Detection Systems (IDS) have turned into a necessary way to guarantee the security of managing computer systems. IDS look to identify intrusions before networks can be influenced by malicious activities. It is achieved by logging the legitimate values on the network beforehand and scanning for any attempt in changing the values. The aim of this paper is to make a light-weight Network Intrusion Detection System (NIDS) to keep running at an ideal spot with the least system prerequisites. It guards against man-in-the-middle attacks on network systems. At the point when an attacker is found spoofing the Address Resolution Protocols (ARPs), defensive ARPs are manually created and sent to 'depoison' the victim using their unique logged L2 addresses. Given the increasing complexity of the current system environment, an everincreasing number of hosts are becoming vulnerable to attack vectors, and therefore methodological, productive and mechanized intrusion detection methodologies need to be carefully examined.


Intrusion Detection, ARP Spoofing, Packet Filtering, ARP Poisoning, Defensive ARP.

How to Cite this Article?

Karthikeyan, S., Keerthivasan, M., Lalitha, A., and Karan, R. (2021). Network Intrusion Detection System Based on Packet Filters. i-manager's Journal on Computer Science, 9(1), 27-32.


[1]. Almogren, A. S. (2020). Intrusion detection in Edge-of- Things computing. Journal of Parallel and Distributed Computing, 137, 259-265. 2019.12.008
[2]. Dubey, G. P., Gupta, N., & Bhujade, R. K. (2011). A novel approach to intrusion detection system using rough set theory and incremental SVM. International Journal of Soft Computing and Engineering (IJSCE), 1(1), 1448
[3]. Iguer, H., Medromi, H., Sayouti, A., Elhasnaoui, S., & Faris, S. (2014, August). The impact of cyber security issues on businesses and governments: A system for implementing a cyber security plan. In 2014, International Conference on Future Internet of Things and Cloud, (pp. 316-321). IEEE.
[4]. Kasongo, S. M., & Sun, Y. (2021). A deep gated recurrent unit based model for wireless intrusion detection system. ICT Express, 7(1), 81-87. j.icte.2020.03.002
[5]. Kushwaha, P., Buckchash, H., & Raman, B. (2017, November). Anomaly based intrusion detection using filter based feature selection on KDD-CUP 99. In TENCON 2017- 2017 IEEE Region 10 Conference, (pp. 839-844). IEEE.
[6]. Pervez, M. S., & Farid, D. M. (2014, December). Feature selection and intrusion classification in NSL-KDD cup 99 th dataset employing SVMs. In The 8 International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014), (pp. 1-6). IEEE.
[7]. Prabhu, G. N., Jain, K., Lawande, N., Kumar, N., Zutshi, Y., Singh, R., & Chinchole, J. (2014). Network intrusion detection system. International Journal of Engineering Research and Applications, 4(4), 69-72.
[8]. Shaout, A., Kaja, N., & Borovikov, M. (2014). Security solution for cloud computing using a hardware implementation of AES. In The International Arab Conference on Information Technology (ACIT-2014).
[9]. Shaout, A., Kaja, N., & Awad, S. (2015, December). A smart traffic sign recognition system. In 2015, 11th International Computer Engineering Conference (ICENCO), (pp. 157-162). IEEE. ICENCO. 2015.7416341
[10]. Subba, B., Biswas, S., & Karmakar, S. (2016, March). A neural network based system for intrusion detection and attack classification. In 2016, Twenty Second National Conference on Communication (NCC), (pp. 1-6). IEEE.
[11]. Zhang, J., Ling, Y., Fu, X., Yang, X., Xiong, G., & Zhang, R. (2020). Model of the intrusion detection system based on the integration of spatial-temporal features. Computers & Security, 89, 1-12. 101681
[12]. Zhang, L., Shi, L., Kaja, N., & Ma, D. (2018, August). A two-stage deep learning approach for can intrusion detection. In Proceedings of Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), (pp. 1-11).
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