A Comprehensive Investigation on the Identification of Real and Encrypted Synthetic Network Attacks using Machine Learning Algorithms

	International Journal of P2P Network Trends and Technology (IJPTT)
	© 2024 by IJPTT Journal
	Volume-14 Issue-1
	Year of Publication : 2024
	Authors : Swati Chaudhari, Pratyush Shukla, Archana Thakur
	DOI : 10.14445/22492615/IJPTT-V14I1P401

How to Cite?

Swati Chaudhari, Pratyush Shukla, Archana Thakur. "A Comprehensive Investigation on the Identification of Real and Encrypted Synthetic Network Attacks using Machine Learning Algorithms" International Journal of P2P Network Trends and Technology, vol. 14, no. 1, pp. 1-6, 2024. Crossref, https://doi.org/10.14445/22492615/IJPTT-V14I1P401

Abstract

Network Intrusion Detection Systems (NIDS) are enhanced and updated consistently, but at the same the, network intruders and hackers are also modernizing and renovating their methodologies. Hence, it is very important to develop novel Intrusion Detection Systems which is constructive to deal with heterogeneous network attacks. Recent research indicates that the Intrusion Detection Systems powered by Machine Learning techniques are capable of curbing these issues up to a great extent but still, there is a long way to go. There are several distinguished models and algorithms exist which are capable of detecting network attacks. Most of the existing research is focused on building a robust system against common and prevalent network attack categories. These approaches do not extend to some peculiar and menacing network attacks, which are often encrypted to spoof the Intrusion Detection Systems. Hence, we have proposed an effective Decision Tree Model which is capable of detecting such attacks with nearly 100% accuracy. We have also investigated and presented a comparative study of more than 10 machine learning models using one of the latest datasets, the HIKARI-2021 [1] dataset. Moreover, the existing research work, particularly dealing with encrypted attacks, does not explicitly indicate the detection accuracy of the encrypted network attack category. Hence, we have also worked on individual network attack categories for various machine-learning approaches.

Keywords
Encrypted Network Attack, Network Intrusion Detection System (NIDS), Decision Tree Algorithm, Machine Learning, Cyber Security.

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