Abstract
In this fourth industrial revolution era, cyber-attacks are constantly increasing. A method called network traffic monitoring blueprint has been used to detect these unusual suspicious activities in the system. Fuzzers, Backdoors, DoS, Exploits, Reconnaissance, Shellcode, Worm, etc., are known as attacks that disrupt the functioning of a system. This paper explores the performance of various machine learning (ML) algorithms and develops an ensemble-based model to detect cyber-attacks. We first implement eight different machine learning models such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), Logistic Regression (LR), K-Nearest Neighbor (KNN), Decision Tree (DT), AdaBoosting, Random Forest (RF), Naive Bayes by using network intrusion dataset named UNSW-NB15 containing multi-type data. Based on the performance of indiviaul machine learning models, we construct an ensemble model by taking into account the top four machine learning models. We also take into account a set of optimal features while building our ensemble model. The experimental outcomes demonstrate that our presented ensemble model produces good accuracy of 98.48% while detecting diverse attacks.
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Uddin, M.A., Shahriar, K.T., Haque, M.M., Sarker, I.H. (2023). Cyber-Attack Detection Through Ensemble-Based Machine Learning Classifier. In: Satu, M.S., Moni, M.A., Kaiser, M.S., Arefin, M.S. (eds) Machine Intelligence and Emerging Technologies. MIET 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 491. Springer, Cham. https://doi.org/10.1007/978-3-031-34622-4_31
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