Review on Intrusion Detection System (IDS) for Network Security using Machine Learning Algorithms
International Research Journal on Advanced Science Hub,
2022, Volume 4, Issue 03, Pages 67-74
10.47392/irjash.2022.014
Abstract
With the advancement in the artificial intelligence technologies and develop- ment of fifth generation networks, a network may face many hazards and chal- lenges as the number of users are accessing the network simultaneously which makes the user to think of losing the confidentiality of the data and hence the network to be considered for security. Threats on the network can be classified in many ways and to detect such threats an Intrusion detection system (IDS) is the one which is mainly used. A network can be attacked in two ways as minor attack and major attack. Denial-of-Service (DoS) and Prob attacks belong to major kind and User-to-Root (U2R) and Remote-to-Login (R2L) goes to minor attack categories. The minor attacks are also called as rare attacks which are very injurious for a host and it is very difficult to recognize these attacks. This paper consists of a survey made on IDS and different algorithms used to imple- ment these IDSs using machine learning.
Anita, Chordia, S, and S Gupta. “An effective model for anomaly IDS to improve the efficiency”. 2015 International Conference on Green Computing and Internet of Things (ICGCIoT) (2015): 190– 194. 10.1109/ICGCIoT.2015.7380455.
Buczak, Anna L. and Erhan Guven. “A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection”. IEEE Communications Surveys & Tutorials 18.2 (2016): 1153–1176. 10 . 1109 / comst . 2015 .2494502.
Chen, Mingzhe, et al. “Artificial Neural Networks- Based Machine Learning for Wireless Net- works: A Tutorial”. IEEE Communications Sur- veys & Tutorials 21.4 (2019): 3039–3071. 10.1109/comst.2019.2926625.
Dias, L P, et al. “Using artificial neural network in intrusion detection systems to computer net- works”. 9th Computer Science and Electronic Engineering (CEEC) (2017): 145–150. 10.1109/ CEEC.2017.8101615.
Gu, Bin, et al. “Kernel Path for ν-Support Vec- tor Classification”. IEEE Transactions on Neural Networks and Learning Systems (2021): 1–12. 10. 1109/tnnls.2021.3097248.
Hindy, Hanan, et al. “A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems”. IEEE Access 8 (2020): 104650–104675. 10 . 1109 / access. 2020 .3000179.
Khosravi-Farmad, Masoud, Ali Ahmadian Ramaki, and Abbas Ghaemi Bafghi. “Risk-based intru- sion response management in IDS using Bayesian decision networks”. 2015 5th International Con- ference on Computer and Knowledge Engineer- ing (ICCKE) (2015): 307–312. 10.1109/ICCKE.2015.7365847.
Lansky, Jan, et al. “Deep Learning-Based Intru- sion Detection Systems: A Systematic Review”. IEEE Access 9 (2021): 101574–101599. 10.1109/ access.2021.3097247.
Masduki, Bisyron Wahyudi, et al. “Study on imple- mentation of machine learning methods combina- tion for improving attacks detection accuracy on Intrusion Detection System (IDS)”. 2015 Interna- tional Conference on Quality in Research (QiR) (2015): 56–64. 10.1109/QiR.2015.7374895.
Pattawaro, Apichit and Chantri Polprasert. “Anomaly-Based Network Intrusion Detec- tion System through Feature Selection and Hybrid Machine Learning Technique”. 2018 16th International Conference on ICT and Knowledge Engineering (ICT&KE) (2018): 1–6. 10.1109/ICTKE.2018.8612331.
Pressley, T. “A new paradigm for intrusion detec- tion systems”. Proceedings. Eleventh Interna- tional Conference on Computer Communications and Networks (2002): 390–390. 10.1109/ICCCN.
2002.1206523.
Shah, Ajay, et al. “Building Multiclass Classi- fication Baselines for Anomaly-based Network Intrusion Detection Systems”. IEEE 7th Interna- tional Conference on Data Science and Advanced Analytics (DSAA) (2020): 759–760. 10 . 1109 / DSAA49011.2020.00102.
Shi, Kansheng, et al. “An improved KNN text clas- sification algorithm based on density”. 2011 IEEE International Conference on Cloud Computing and Intelligence Systems (2011): 113–117. 10 . 1109/CCIS.2011.6045043.
Singh, Preeti, S P Singh, and D S Singh. “AN INTRODUCTION AND REVIEW ON MACHINE LEARNING APPLICATIONS IN MEDICINE AND HEALTHCARE”. 2019 IEEE Conference on Information and Communication Technology (2019): 1–6. 10 . 1109 / CICT48419 .
2019.9066250.
Tirumala, Sreenivas Sremath, Hira Sathu, and Abdolhossein Sarrafzadeh. “Free and open source intrusion detection systems: A study”. 2015 Inter- national Conference on Machine Learning and Cybernetics (ICMLC) (2015): 205–210. 10.1109/ ICMLC.2015.7340923.
Umbarkar, Swapnil and Sanyam Shukla. “Analysis of Heuristic based Feature Reduction method in Intrusion Detection System”. 2018 5th Interna- tional Conference on Signal Processing and Inte- grated Networks (SPIN) (2018): 717–720. 10 . 1109/SPIN.2018.8474283.
Wasi, Sarwar, et al. “Intrusion Detection Using Deep Learning and Statistical Data Analysis”. 2019 4th International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST) (2019): 1–5. 10 . 1109 / ICEEST48626 . 2019 . 8981688.
Zhengbing, Hu, Li Zhitang, and Wu Junqi. “A Novel Network Intrusion Detection System (NIDS) Based on Signatures Search of Data Mining”. First International Workshop on Knowledge Dis- covery and Data Mining (WKDD 2008) (2008): 10–16. 10.1109/WKDD.2008.48.
- Article View: 41
- PDF Download: 21