علم الرواسب والتسلسل الزمني الجيولوجي لتسلسلات الصخور الرسوبية في العصر الأركي الوسيط في منطقة سينغبوم بالهند وتداعياتها

BFP/URG/ICT/24/018

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Malware Detection & Analysis Using Machine Learning

Abstract

The process of dissecting, analysing, and detecting malware can be a long and error-prone process that involves a lot of risks to an organisation’s network and systems. Analysts may also miss patterns or signs that a complex malware implant may use – if not time-consuming in a situation where multiple Indicators of Compromise are present. Using machine learning, malware dissection, and detection can be enhanced using various tools and techniques that a malware analyst would employ, in addition to learning from other datasets to have a database of common patterns and behaviours that a piece of malware may use to perform its objective goals. This can be enhanced further by implementing Deep Neural Networks for large data processing and more accurate predictions. Since most enterprise organizations use Windows as their main operating system of choice, this project will focus on adapting an experimental methodology in which an AI model will be trained to analyse malware threats and give a deep analysis of les and will document the use of different training algorithms and will be later on connected to the graphical interface to ensure user-friendliness.