Date of Defense
6-11-2025 1:30 PM
Location
E1-1022
Document Type
Thesis Defense
Degree Name
Master of Science in Information Security
College
CIT
Department
Information Systems and Security
First Advisor
Prof.Mohammad Mehedy Masud
Keywords
Intrusion Detection System (IDS), Machine Learning (ML), Adaptive Threat Management, Cybersecurity, IT Security, Anomaly Detection, Intelligent Security Systems.
Abstract
The fast-changing landscape of cyber threats continues to challenge the development of strong and reliable security frameworks for IT management systems. Traditional defense tools, such as Intrusion Detection Systems (IDS), often struggle to keep up with today’s advanced and constantly evolving attack methods. This thesis explores these ongoing challenges and looks into how machine learning (ML) and explainable artificial intelligence (XAI) can be used to boost IDS performance.
The research outlines a smart, adaptive system that combines supervised learning for real-time threat detection, unsupervised models for anomaly analysis, and proactive defense strategies. The goal is to improve detection accuracy, cut down on false alarms, and enable systems to respond automatically to emerging threats.
Through testing and evaluation, the thesis shows that Machine Learning powered IDS can significantly enhance an organization’s resilience against cyberattacks. Overall, the findings offer a meaningful step forward in modern IT security, pointing toward more adaptive and intelligent approaches to managing threats.
Included in
ENHANCING IT SECURITY MANAGEMENT WITH AN ADVANCED INTRUSION DETECTION SYSTEM BASED ON MACHINE LEARNING AND EXPLAINABLE AI
E1-1022
The fast-changing landscape of cyber threats continues to challenge the development of strong and reliable security frameworks for IT management systems. Traditional defense tools, such as Intrusion Detection Systems (IDS), often struggle to keep up with today’s advanced and constantly evolving attack methods. This thesis explores these ongoing challenges and looks into how machine learning (ML) and explainable artificial intelligence (XAI) can be used to boost IDS performance.
The research outlines a smart, adaptive system that combines supervised learning for real-time threat detection, unsupervised models for anomaly analysis, and proactive defense strategies. The goal is to improve detection accuracy, cut down on false alarms, and enable systems to respond automatically to emerging threats.
Through testing and evaluation, the thesis shows that Machine Learning powered IDS can significantly enhance an organization’s resilience against cyberattacks. Overall, the findings offer a meaningful step forward in modern IT security, pointing toward more adaptive and intelligent approaches to managing threats.