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Subject: search.filters.subject.Machine learning

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    Approach to Improving Machine Learning Models for Intrusion Detection System
    (International Islamic University Chittagong, 2022-07) Labib, Ahmad Ibtisam; Chy, Shamsuddin Ahmmed; Hossain, Md. Shahriar
    In today's digital world, there are several security risks that digital assets must contend with. Systems for detecting intrusions (IDS) are essential security tools that protect digital assets. But their usefulness depends on meeting strict accuracy requirements, and their effectiveness depends on timely alarms. This study offers a novel IDS model that combines deep learning and machine learning methods as a solution to these problems. The study applies several classification techniques, such as Gaussian Naive Bayes (GNB), Random Forest (RF), Decision Tree, K-Nearest Neighbors (KNN), Soft Voting, and Hard Voting, using the well known KDD Cup-1999 dataset. After a large-scale dataset was processed, the Decision Tree method performed better than the others, with a 99.9% accuracy rate. This study aims to investigate the effects of soft voting and hard voting, a novel application in IDS. Decision Tree proved to be the better performance in spite of these efforts. By offering information about algorithmic efficacy, the research advances the field of intrusion detection and helps decision-makers in the design and deployment of intrusion detection systems. These findings have implications for improving digital asset protection against changing cyber threats.
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    Malware Detection System using Machine Learning & Deep Learning Technique
    (International Islamic University Chittagong, 2023-07) Karim, Miskatul; Hossain, Md Jubair
    These days, with the amount and variety of malware increasing at an exponential rate, it is imperative to use innovative methods for quickly and precisely detecting this dangerous software. The fast velocity at which malware spreads makes manual heuristic inspections of malware analyses inefficient for both discovering new malware and keeping up with it. Machine-learning techniques have therefore become increasingly popular. By automating the examination of static and dynamic studies, these methods can classify unknown malware according to how close it is to recognized families and combine malware that exhibits similar behavior. While data mining and machine learning approaches have been applied in the past, this paper demonstrates how deep learning networks can improve accuracy even further. By building neural networks that have more potentially varied.