Anomaly Based Intrusion Detection Using Machine Learning Techniques

Authors

  • M. Ahana Aslin GOVERNMENT COLLEGE OF TECHNOLOGY, Affilated to Anna University, Coimbatore.
  • A. Meena Kowshalya Government College Of Technology, Affilated to Anna university, Coimbatore

DOI:

https://doi.org/10.37591/jons.v10i2.919

Keywords:

Intrusion detection system, machine learning, data pre-processing, classification, decision tree, anomaly attacks

Abstract

Detection of Cyberattacks/anomalies in a network to build an efficient Intrusion Detection System (IDS) is very important. A system called an intrusion detection system (IDS) monitors network traffic in order to find suspicious activity and sends out signals when it is noticed. Monitoring and data analysis are designed with the objective of finding any network or system intrusions. Machine learning methods can anticipate both known and unidentified attacks. This project implements an Intrusion Detection Tree machine learning based security model to detect anomalies in the system. Decision Tree is used for classification purpose. This in turn reduced the amount of data required for analysis there by reducing computational complexity. Experiment results using Python yielded an accuracy of 99.5%.

Author Biographies

M. Ahana Aslin, GOVERNMENT COLLEGE OF TECHNOLOGY, Affilated to Anna University, Coimbatore.

Ahana Aslin M is currently pursuing Full-Time M.E in Government College of Technology, Coimbatore,TamilNadu, India. Her area of interest includes Image processing and Machine Learning

A. Meena Kowshalya, Government College Of Technology, Affilated to Anna university, Coimbatore

Dr. A Meena Kowshalya graduated from Bharathiar University. She obtained her Masters and Ph.D degree in Computer Science and Engineering from Anna University. She is a gold medal winner for the academic year 2011. She is the winner of AICTE Viswakarma Awards for the year 2019. She currently serves as an Assistant professor in Government College of Technology, Coimbatore, TamilNadu, India. She has total of 14 years teaching experience. Her area of research interest includes Information Retrieval, Image Captioning, Artificial Intelligence and Machine Learning

References

Kim Gisung, Seungmin Lee, Sehun Kim. A novel hybrid intrusion detection method integrating anomaly detection with misuse detection. Expert Syst Appl. 2014; 41(4): 1690–1700.

Guan Yu, Ghorbani Ali A, Nabil Belacel. Y-means: A clustering method for intrusion detection. CCECE 2003-Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No. 03CH37436), IEEE. 2003; 2: 1083–1086.

Sangkatsanee Phurivit, Naruemon Wattanapongsakorn, Chalermpol Charnsripinyo. Real-time intrusion detection and classification. IEEE Netw. 2009; 1–5.

Saranya T, et al. Performance analysis of machine learning algorithms in intrusion detection system: A review. Procedia Comput Sci. 2020; 171: 1251–1260.

Kruegel C, Mutz D, Robertson W, Valeur F. Bayesian event classification for intrusion detection. In Proc. of the 19th Annual Computer Security Applications Conference, Las Vegas, NV. 2003; 14–23.

Wicaksana Aditya K, Cahyani Denis E. Modification of a Density-Based Spatial Clustering Algorithm for Applications with Noise for Data Reduction in Intrusion Detection Systems. Int J Fuzzy Log Intell Syst. 2021; 21(2): 189–203.

Mohamad Tahir Hatim, et al. Hybrid machine learning technique for intrusion detection system. 2015; 464–472.

Balogun Abdullateef Oluwagbemiga, Rasheed Gbenga Jimoh. Anomaly intrusion detection using an hybrid of decision tree and K-nearest neighbor. Journal of Advances in Scientific Research & Applications (JASRA). 2015; 2: 67–74.

Singh Amrit Pal, Manik Deep Singh. Analysis of Host-Based and Network-Based Intrusion Detection System. Int J Comput Netw Inf Secur. 2014; 6(8): 41–47.

Kumar Roshan, Deepak Sharma. HyINT: signature-anomaly intrusion detection system. 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE. 2018; 1–7.

Pacheco Jesus, et al. Artificial neural networks-based intrusion detection system for internet of things fog nodes. IEEE Access. 2020; 8: 73907–73918.

Sarker Iqbal H. CyberLearning: Effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks. Internet Things. 2021; 14: 100393.

Nageswararao K, Rajya Lakshmi D, Venkateswara Rao T. Robust Statistical Outlier based Feature Selection Technique for Network Intrusion Detection. Int J Soft Comput Eng (IJSCE). 2012; 2(1): 454–459.

Visumathi J, Shunmuganathan KL. Detecting Security threats in the Router using Computational Intelligence. arXiv preprint arXiv:1005.0967. 2010.

Hoque Mohammad Sazzadul, et al. An implementation of intrusion detection system using genetic algorithm. arXiv preprint arXiv:1204.1336. 2012.

Sampada Bhosale. Network Intrusion Detection. [Online]. Available: https://www.kaggle.com/datasets/sampadab17/network-intrusion-detection

Wells David. UNSW-NB15. [Online]. Available: https://www.kaggle.com/datasets/mrwellsdavid/unsw-nb15

Downloads

Published

2022-08-25

Issue

Vol. 10 No. 2 (2022)

Section

Review Articles

License

Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  •  I/We also agree to the authorship of the article in the following order:

Author’s name                                    

 

1. ________________

2. ________________

3. ________________

4. ________________

 

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.