A Hybrid Machine Learning approach for Analysis & Identification of Cyber Security Attacks
A Hybrid Machine Learning approach for Analysis & Identification of Cyber Security Attacks |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-6 |
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| Year of Publication : 2025 | ||
| Author : Satya Srinivas Maddipati, A Siva Naga Ram Gopal, Rakesh Kancharla, PVVS Eswar Rao, DSV Prasad Uppalapati | ||
| DOI : 10.14445/22315381/IJETT-V73I6P113 | ||
How to Cite?
Satya Srinivas Maddipati, A Siva Naga Ram Gopal, Rakesh Kancharla, PVVS Eswar Rao, DSV Prasad Uppalapati, "A Hybrid Machine Learning approach for Analysis & Identification of Cyber Security Attacks," International Journal of Engineering Trends and Technology, vol. 73, no. 6, pp.149-156, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I6P113
Abstract
Providing security for resources in the internet is an essential task. Now a days, the major threats in the cyber world are Denial of Service (DoS), Malware and Intruders. These types of attacks must be predicted in advance with high accuracy using machine learning techniques. This research work analyses the network traffic patterns for cyber-attacks and identifies the type of attack. This work proposes apriori algorithm to extract frequent patterns from network traffic for cyber-attacks and also applies logistic regression to identify the type of attack. The results of proposed work compared with other machine learning algorithms like Decision trees, Random forest and support vector machines. The results of this work identified the network traffic frequent patterns with above 65% confidence and proved that the average accuracy was increased by 5% using proposed work.
Keywords
Denial of Service, Cyber-attacks, Machine Learning algorithms, Network traffic frequent patterns, Logistic regression
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