Abstract
With the prevalent technology like cloud computing, big data and Internet of things (IoT), a huge amount of data have been generated day by day. Presently, most of the data are stored in the digital form and transfer to others by the mean of digital communication media. Hence, to provide security to data and network is one of the main concerns for everyone. Several intrusion detection systems (IDS) have been proposed in the last few years, but accuracy and false alarm rate are still most challenges issue for the researchers. Nowadays, an intruder is used to design new types of attack day by day, which is challenging to identify. Recently, machine learning is emerging as the most powerful tool for the development of IDS. This paper discusses three different machine learning approach, namely decision tree, random forest and support vector machine (SVM). KDD-99 dataset is used to train the model. Due to the unbalance data and duplicate feature, recursive feature extraction technique is being used to reduce the number of features. The experiment result shows that proposed IDS performs well as compared to the base model with the accuracy of 99.1.
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References
Magán-Carrión R et al (2020) Towards a reliable comparison and evaluation of network intrusion detection systems based on machine learning approaches. Appl Sci 1–21
Buczak AL, Guven E (2017) A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun Surv Tutor 18(2):1153–1176
Khraisat A et al (2019) Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity 2–20
Shen C, Liu C, Tan H, Wang Z, Xu D, Su X (2018) Hybrid-augmented device fingerprinting for intrusion detection in industrial control system networks. IEEE Wirel Commun 25(6):26–31
Jabbar MA, Aluvalu R, Reddy SS (2017) RFAODE: a novel ensemble intrusion detection system. Procedia Comput Sci 115:226–234
Gupta ARB, Agrawal J (2020) A comprehensive survey on various machine learning methods used for intrusion detection system. In: Proceeding of IEEE 9th international conference on communication systems and network technologies, April 2020, pp 282–289
Raj Jennifer S (2019) A comprehensive survey on the computational intelligence techniques and its applications. J ISMAC 1(03):147–159
Mugunthan SR (2019) Soft computing based autonomous low rate DDOS attack detection and security for cloud computing. J Soft Comput Paradig (JSCP) 1(02):80–90
Anish et al (2019) Machine learning based intrusion detection system. In: Proceedings of the third international conference on trends in electronics and informatics, pp 916–920
Rahaman A et al (2020) Scalable machine learning-based intrusion detection system for IoT-enabled smart cities. Sustainable Cities and Society
Almseidin M et al (2017) Evaluation of machine learning algorithms for intrusion detection system. In: Proceeding of IEEE 15th international symposium on intelligent systems and informatics, September 2017, pp 277–282
Kumar G et al (2020) MLEsIDSs: machine learning-based ensembles for intrusion detection systems—a review. J Supercomput 76(2), Feb 2020
Alrowaily M et al (2019) Effectiveness of machine learning based intrusion detection systems. In: Proceeding of international conference on security, privacy and anonymity in computation, communication and storage, pp 277–288
Bay SD (1999) The UCI KDD archive. Department of Information and Computer Science, University of California, vol 404, pp 405. http://kdd.ics.uci.edu.irvine.ca
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Gupta, A.R.B., Agrawal, J. (2021). Machine Learning-Based Intrusion Detection System with Recursive Feature Elimination. In: Smys, S., Balas, V.E., Kamel, K.A., Lafata, P. (eds) Inventive Computation and Information Technologies. Lecture Notes in Networks and Systems, vol 173. Springer, Singapore. https://doi.org/10.1007/978-981-33-4305-4_13
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DOI: https://doi.org/10.1007/978-981-33-4305-4_13
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