Optimizing a Feature Selection Intrusion Detection Algorithm with Data Mining
DOI:
https://doi.org/10.58599/IJSMCSE.2024.1106Keywords:
Intrusion detection, Feature selection, Data mining, Optimization, Machine learning, CybersecurityAbstract
Intrusion detection safeguards computer systems against unauthorized access and malicious activities. Feature selection plays a pivotal role in enhancing the efficiency and effectiveness of intrusion detection algorithms by identifying the most relevant features from vast datasets. In this study, we propose a novel approach to optimize feature selection in intrusion detection algorithms using data mining techniques. We explore various data mining algorithms, including decision trees, genetic algorithms, and particle swarm optimization, to identify the optimal feature subset that maximizes detection accuracy while minimizing computational overhead. Experimental results demonstrate our approach’s efficacy in improving intrusion detection systems’ performance across different datasets, achieving higher detection rates with reduced computational complexity. Our work advances state-of-the-art intrusion detection by leveraging data mining for efficient feature selection.
References
[1]. Skhumbuzo Zwane, Paul Tarwireyi, and Matthew Adigun. Performance analysis of machine learning classifiers for intrusion detection. In 2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC), pages 1–5. IEEE, 2018.
[2]. Shengyi Pan, Thomas Morris, and Uttam Adhikari. Developing a hybrid intrusion detection system using data mining for power systems. IEEE Transactions on Smart Grid, 6(6):3104–3113, 2015.
[3]. Inadyuti Dutt, Samarjeet Borah, and Indrakanta Maitra. A proposed machine learning based scheme for intrusion detection. In 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), pages 479–483. IEEE, 2018.
[4]. Komal Rasane, Laxmi Bewoor, and Vishal Meshram. A comparative analysis of intrusion detection techniques: Machine learning approach. In Proceedings of International Conference on Communication and Information Processing (ICCIP), 2019.
[5]. Pedro Garcia-Teodoro, Jesus Diaz-Verdejo, Gabriel Maci´a-Fern´andez, and Enrique V´azquez. Anomalybased network intrusion detection: Techniques, systems and challenges. computers & security, 28(1-2):18–28, 2009.
[6]. Venkata Krishna Chaithanya Manam. Efficient disambiguation of task instructions in crowdsourcing. PhD thesis, Purdue University Graduate School, 2023.
[7]. Hongzhu Tao, Jieying Zhou, and Sen Liu. A survey of network security situation awareness in power monitoring system. In 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), pages 1–3. IEEE, 2017.
[8]. Paul Dokas, Levent Ertoz, Vipin Kumar, Aleksandar Lazarevic, Jaideep Srivastava, and Pang-Ning Tan. Data mining for network intrusion detection. In Proc. NSF Workshop on Next Generation Data Mining, pages 21–30. Citeseer, 2002.
[9]. VK Chaithanya Manam, Joseph Divyan Thomas, and Alexander J Quinn. Tasklint: Automated detection of ambiguities in task instructions. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, volume 10, pages 160–172, 2022.
[10]. Sugwon Hong, Jae-Myeong Lee, Mustafa Altaha, and Muhammad Aslam. Security monitoring and network management for the power control network. system, 2:3, 2020.
[11]. V Suresh Kumar, Sanjeev Kulkarni, Naveen Mukkapati, Abhinav Singhal, Mohit Tiwari, and D Stalin David. Investigation on constraints and recommended context aware elicitation for iot runtime workflow. International Journal of Intelligent Systems and Applications in Engineering, 12(3s):96–105, 2024.
[12]. Zeeshan Ahmad, Adnan Shahid Khan, Cheah Wai Shiang, Johari Abdullah, and Farhan Ahmad. Network intrusion detection system: A systematic study of machine learning and deep learning approaches. Transactions on Emerging Telecommunications Technologies, 32(1):e4150, 2021.
[13]. Sanjeev Kulkarni, Sachidanand S Joshi, AM Sankpal, and RR Mudholkar. Link stability based multipath video transmission over manet. International Journal of Distributed and Parallel Systems, 3(2):133, 2012.
[14]. V K. Chaithanya Manam, Dwarakanath Jampani, Mariam Zaim, Meng-Han Wu, and Alexander J. Quinn. Taskmate: A mechanism to improve the quality of instructions in crowdsourcing. In Companion Proceedings of The 2019 World Wide Web Conference, pages 1121–1130, 2019.
[15]. Fadi Salo, Mohammadnoor Injadat, Ali Bou Nassif, Abdallah Shami, and Aleksander Essex. Data mining techniques in intrusion detection systems: A systematic literature review. IEEE Access, 6:56046–56058, 2018.
[16]. Sanjeev Kulkarni, Aishwarya Shetty, Mimitha Shetty, HS Archana, and B Swathi. Gas spilling recognition and prevention using iot with alert system to improve the quality service. Perspectives in Communication, Embedded-systems and Signal-processing-PiCES, 4(4):34–38, 2020.
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Methods in Computational Science and Engineering
![Creative Commons License](http://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.