Implementation of Signature based Intrusion Detection System with Snort Rule on E-Voting System

Muhammad Adnan Khairi A.S., Eddy Prasetyo Nugroho, Rizky Rachman J.

Abstract


Security is an important thing for everyone, including network security, which everyone needs, including the security at web server, there are problems encountered on the server one of which is on the E-voting site server, this server serves to store all the data storage of votes in an election between registered candidates. In this paper we propose a solution to detect these attacks using SNORT IDS. snort will detect an attack by adding a special rule to handle the attack. We tested the proposed solution by comparing the system against four different attacks, the result was that DDoS attacks had the greatest number of data packets compared to other attacks.

Keywords


Computer security; E-voting; Intrusion detection system; Snort

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References


Abhipraya, F. A., Yogar, B. N. A., and Prasetyo, S. I. (2023). Toward Effective Electoral Affairs: The Implementation of E-Voting in the Village Chief Executive Election 2021. Indonesian Governance Journal: Kajian Politik-Pemerintahan, 6(1), 28-36.

Abomhara, M., and Køien, G. M. (2015). Cyber security and the internet of things: Vulnerabilities, threats, intruders, and attacks. Journal of Cyber Security, 4(1), 65-88.

Bhosale, D. A., and Mane, V. M. (2015, October). Comparative study and analysis of network intrusion detection tools. In 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), 2016, 312-315.

Chanthakoummane, Y., Saiyod, S., Benjamas, N., and Khamphakdee, N. (2016). Improving intrusion detection on snort rules for botnets detection. In Information Science and Applications (ICISA) 2016, 765-779.

Chavarro, F. A. C., Homes, C. D. C., and Mora, D. C. F. (2018). Implementation of confidentiality and anonymity as services in an e-voting system for educational institutions. International Journal of Applied Engineering Research, 13(18), 13555-13565.

Dewi, E. K., Harini, D., and Miftachurohmah, N. (2017, February). Snort IDS sebagai tools forensik jaringan universitas Nusantara PGRI Kediri. Seminar Nasional Inovasi Teknologi, 1(1), 397-404.

Goel, A., and Vasistha, A. K. (2017). The implementation and assessment of snort capabilities. International Journal of Computer Applications, 167(13), 15-23.

Gupta, K., Singh, R. R., and Dixit, M. (2017, June). Cross site scripting (XSS) attack detection using intrustion detection system. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), 2018, 199-203.

Jim, L. E., Islam, N., and Gregory, M. A. (2022). Enhanced MANET security using artificial immune system-based danger theory to detect selfish nodes. Computers and Security, 113, 102538.

Khamphakdee, N., Benjamas, N., and Saiyod, S. (2015). 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, 8(3), 234-250.

Kizza, J. M., and Kizza, J. M. (2013). Security in wireless networks. Guide to Computer Network Security, 387-411.

Kohno, T., Stubblefield, A., Rubin, A. D., and Wallach, D. S. (2004, May). Analysis of an electronic voting system. In IEEE Symposium on Security and Privacy, 2004. Proceedings. 2004, 27-40.

Kumar, V., and Sangwan, O. P. (2012). Signature based intrusion detection system using SNORT. International Journal of Computer Applications and Information Technology, 1(3), 35-41.

Le Jeune, L., Goedeme, T., and Mentens, N. (2021). Machine learning for misuse-based network intrusion detection: overview, unified evaluation, and feature choice comparison framework. IEEE Access, 9, 63995-64015.

Li, S., Dai, Y., and Chen, Y. (2001). Intrusion detection system. Computer Engineering, 27, 7-9.

Mahmoud, T. M., Ali, A. A., and Elshafie, H. M. (2016). A hybrid snort-negative selection network intrusion detection technique. International Journal of Computer Applications, 146(5), 24-31.

Maseer, Z. K., Yusof, R., Bahaman, N., Mostafa, S. A., and Foozy, C. F. M. (2021). Benchmarking of machine learning for anomaly-based intrusion detection systems in the CICIDS2017 dataset. IEEE Access, 9, 22351-22370.

Moloja, D., and Mpekoa, N. (2017, July). Towards a cloud intrusion detection and prevention system for M-voting in South Africa. In 2017 International Conference on Information Society (i-Society), 2018, 34-39.

Olanrewaju, R. F., Khan, B. U. I., Najeeb, A. R., Zahir, K. N. A. K., and Hussain, S. (2018). Snort-based smart and swift intrusion detection system. Indian Journal of Science and Technology, 11(4), 1-9.

Roesch, M. (1999, November). Snort: Lightweight intrusion detection for networks. In Lisa, 99(1), 229-238.

Saied, M., Guirguis, S., and Madbouly, M. (2024). Review of artificial intelligence for enhancing intrusion detection in the internet of things. Engineering Applications of Artificial Intelligence, 127, 107231.

Sekhar, M., Tulasi, K., Amulya, V., Teja, D., and Kumar, M. (2015). Implementation of IDS using Snort on bayesian network. International Journal of Computer Science and Mobile Computing, 4(4), 790-795.

Smith, A. D., and Clark, J. S. (2005). Revolutionising the voting process through online strategies. Online Information Review, 29(5), 513-530.

Sobh, T. S. (2006). Wired and wireless intrusion detection system: Classifications, good characteristics, and state-of-the-art. Computer Standards and Interfaces, 28(6), 670-694.

Vuppala, R., and Farik, M. (2016). Intrusion detection and prevention systems-sourcefire snort. International Journal of Scientific and Technology Research, 5(7), 5-8.

Yamamoto, Y., and Yamaguchi, S. (2023). Defense mechanism to generate ips rules from honeypot logs and its application to log4shell attack and its variants. Electronics, 12(14), 3177.

Zissis, D., and Lekkas, D. (2011). Securing e-Government and e-Voting with an open cloud computing architecture. Government Information Quarterly, 28(2), 239-251.




DOI: https://doi.org/10.17509/jcs.v4i1.71176

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