ASSESSMENT OF HYBRID MACHINE LEARNING ALGORITHMS FOR INTRUSION ANOMALY DETECTION IN CYBERSECURITY USING MATHEMATICAL MODELLING AND SIMULATION

Naseem Afzal Qureshi; Sana Batool; Muhammad Ali Khan; Muhammad Zohaib Khan; Muqaddas Salahuddin; Hussain Bux Marri; Faisal Hassan

ASSESSMENT OF HYBRID MACHINE LEARNING ALGORITHMS FOR INTRUSION ANOMALY DETECTION IN CYBERSECURITY USING MATHEMATICAL MODELLING AND SIMULATION

Authors

Naseem Afzal Qureshi
Sana Batool
Muhammad Ali Khan
Muhammad Zohaib Khan
Muqaddas Salahuddin
Hussain Bux Marri
Faisal Hassan

Keywords:

Cyber Security, Machine Learning, Mathematical Modelling, Simulation, Intrusion Anomaly Detection Systems (IADS), Fuzzy C-Means (FCM), Support Vector Machines Linear (SVML), Multi-layer Perceptron (MPL), Stochastic Gradient Descent (SGD) Algorithms.

Abstract

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Published

2026-03-31

How to Cite

Naseem Afzal Qureshi, Sana Batool, Muhammad Ali Khan, Muhammad Zohaib Khan, Muqaddas Salahuddin, Hussain Bux Marri, & Faisal Hassan. (2026). ASSESSMENT OF HYBRID MACHINE LEARNING ALGORITHMS FOR INTRUSION ANOMALY DETECTION IN CYBERSECURITY USING MATHEMATICAL MODELLING AND SIMULATION. Spectrum of Engineering Sciences, 4(3), 2494–2520. Retrieved from https://thesesjournal.com.medicalsciencereview.com/index.php/1/article/view/3486