FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-110251

Research Article |

Anomaly Based Intrusion Detection through Efficient Machine Learning Model

Author(s): Archana R. Ugale1* and Amol D Potgantwar2

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 11, Issue 2,   Special Issue on Mobile Computing assisted by Artificial Intelligent for 5G/6G Radio Communication

Publisher : FOREX Publication

Published : 30 June 2023

e-ISSN : 2347-470X

Page(s) : 616-622

DOI: https://doi.org/10.37391/ijeer.110251

Abstract

Machine learning is commonly utilised to construct an intrusion detection system (IDS) that automatically detects and classifies network intrusions and host-level threats. Malicious assaults change and occur in high numbers, needing a scalable solution. Cyber security researchers may use public malware databases for research and related work. No research has examined machine learning algorithm performance on publicly accessible datasets. Data and physical level security and analysis for Data protection have become more important as data volumes grow. IDSs collect and analyse data to identify system or network intrusions for data prevention. The amount, diversity, and speed of network data make data analysis to identify assaults challenging. IDS uses machine learning methods for precise and efficient development of data security mechanism. This work presented intrusion detection model using machine learning, which utilised feature extraction, feature selection and feature modelling for intrusion detection classifier.

Keywords: Intrusion detection system, Machine learning, Network security, Feature extraction, Anomaly detection.




Archana R. Ugale*, School of Engineering & Technology, D Y Patil University Ambi Pune, Maharashtra, India; Email: ar.ugale@gmail.com

Amol D Potgantwar, Department of Computer Engineering, Sandip Institute of Technology and Research Centre Nashik, Maharashtra, India

    [1] M. V. Mahoney and P. K. Chan, "An analysis of the 1999 DARPA/Lincoln Laboratory evaluation data for network anomaly detection" in Recent Advances in Intrusion Detection, Berlin, Germany:Springer, vol. 2820, pp. 220-237, 2003. [Cross Ref]
    [2] M. Sabhnani and G. Serpen, "Why machine learning algorithms fail in misuse detection on KDD intrusion detection data set", Intell. Data Anal., vol. 8, no. 4, pp. 403-415, 2004. [Cross Ref]
    [3] Y. Bouzida and F. Cuppens, "Neural networks vs. decision trees for intrusion detection", Proc. IEEE/IST Workshop Monitoring Attack Detection Mitigation (MonAM), pp. 1-29, Sep. 2006.
    [4] Ajani, S., Amdani, S.Y. (2022). Obstacle Collision Prediction Model for Path Planning Using Obstacle Trajectory Clustering. In: Sharma, S., Peng, SL., Agrawal, J., Shukla, R.K., Le, DN. (eds) Data, Engineering and Applications. Lecture Notes in Electrical Engineering, vol 907. Springer, Singapore. https://doi.org/10.1007/978-981-19-4687-5_8. [Cross Ref]
    [5] X. Glorot, A. Bordes and Y. Bengio, "Deep sparse rectifier neural networks", Proc. 14th Int. Conf. Artif. Intell. Statist., pp. 315-323, 2011.
    [6] A. Alazab, M. Hobbs, J. Abawajy and M. Alazab, "Using feature selection for intrusion detection system", Proc. Int. Symp. Commun. Inf. Technol. (ISCIT), pp. 296-301, Oct. 2012. [Cross Ref]
    [7] S. N. Ajani and S. Y. Amdani, "Probabilistic path planning using current obstacle position in static environment," 2nd International Conference on Data, Engineering and Applications (IDEA), 2020, pp. 1-6, doi: 10.1109/IDEA49133.2020.9170727. [Cross Ref]
    [8] G. Creech and J. Hu, "A semantic approach to host-based intrusion detection systems using contiguousanddiscontiguous system call patterns", IEEE Trans. Comput., vol. 63, no. 4, pp. 807-819, Apr. 2014. [Cross Ref]
    [9] S. Ioffe and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift", Proc. Int. Conf. Mach. Learn., pp. 448-456, 2015.
    [10] N. Moustafa and J. Slay, "UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)", Proc. IEEE Mil. Commun. Inf. Syst. Conf. (MilCIS), pp. 1-6, Nov. 2015. [Cross Ref]
    [11] Manzoor MA, Morgan Y. Real-time support vector machine based network intrusion detection system using Apache Storm. In: IEEE 7th annual information technology, electronics and mobile communication conference (IEMCON), 2016. Piscataway: IEEE. 2016; p. 1–5. [Cross Ref]
    [12] Wang H, Xiao Y, Long Y. Research of intrusion detection algorithm based on parallel SVM on Spark. In: 7th IEEE International conference on electronics information and emergency communication (ICEIEC), 2017. Piscataway: IEEE; 2017. p. 153–156. [Cross Ref]
    [13] Vimalkumar K, Radhika N. A big data framework for intrusion detection in smart grids using Apache Spark. In: International conference on advances in computing, communications and informatics (ICACCI), 2017. Piscataway: IEEE; 2017. p. 198–204. [Cross Ref]
    [14] W. Wang et al., "HAST-IDS: Learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection", IEEE Access, vol. 6, pp. 1792-1806, 2018. [Cross Ref]
    [15] Ajani, S.N., Amdani, S.Y. (2021). Agent-Based Path Prediction Strategy (ABPP) for Navigation Over Dynamic Environment. In: Muthu Kumar, P., Sarkar, D.K., De, D., De, C.K. (eds) Innovations in Sustainable Energy and Technology. Advances in Sustainability Science and Technology. Springer, Singapore. [Cross Ref]
    [16] M. N. Kurt, Y. Yılmaz and X. Wang, "Real-time detection of hybrid and stealthy cyber-attacks in smart grid", IEEE Trans. Inf. Forensics Security, vol. 14, no. 2, pp. 498-513, Feb. 2019. [Cross Ref]
    [17] Ali Sadiqui, "Putting in Place an Intrusion Prevention System (IPS)," in Computer Network Security, Wiley, 2020, pp.101-124, doi: 10.1002/9781119706762.ch6. [Cross Ref]
    [18] Ariani and M. Salman, "Modeling Study of Priority Intrusion Response Selected on Intrusion Detection System Alert," 2020 6th International Conference on Science and Technology (ICST), Yogyakarta, Indonesia, 2020, pp. 1-6, doi: 10.1109/ICST50505.2020.9732867. [Cross Ref]
    [19] S. Bhadauria and T. Mohanty, "Hybrid Intrusion Detection System using an Unsupervised method for Anomaly-based Detection," 2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), Hyderabad, India, 2021, pp. 1-6, doi: 10.1109/ANTS52808.2021.9936919. [Cross Ref]
    [20] Prasanthi Rathnala, M.S. Pradeep Kumar Patnaik, Srinivasa Rao Sura, Bolla Prasad, N Siva Mallikarjuna Rao and Delione N Rayan (2022), Design of an Efficient Face Recognition system using Deep Learning Technique. IJEER 10(3), 689-693. DOI: 10.37391/IJEER.100345. [Cross Ref]
    [21] K. Sood, M. R. Nosouhi, D. D. N. Nguyen, F. Jiang, M. Chowdhury and R. Doss, "Intrusion Detection Scheme With Dimensionality Reduction in Next Generation Networks," in IEEE Transactions on Information Forensics and Security, vol. 18, pp. 965-979, 2023, doi: 10.1109/TIFS.2022.3233777. [Cross Ref]
    [22] Shengjie Xu; Yi Qian; Rose Qingyang Hu, "Edge Intelligence for Intrusion Detection," in Cybersecurity in Intelligent Networking Systems , IEEE, 2023, pp.45-54, doi: 10.1002/9781119784135.ch4. [Cross Ref]
    [23] M. L. Han, B. I. Kwak and H. K. Kim, "TOW-IDS: Intrusion Detection System Based on Three Overlapped Wavelets for Automotive Ethernet," in IEEE Transactions on Information Forensics and Security, vol. 18, pp. 411-422, 2023, doi: 10.1109/TIFS.2022.3221893. [Cross Ref]
    [24] https://www.unb.ca/cic/datasets/ids-2017.html

Archana R. Ugale and Amol D Potgantwar (2023), Anomaly Based Intrusion Detection through Efficient Machine Learning Model. IJEER 11(2), 616-622. DOI: 10.37391/ijeer.110251.