Ensemble Techniques for Malicious Threat Detection

• Main Authors: Raj, Kiran S, Tej, Krishna, S, Nithin Kumar, T, Senthil Kumar, Vajipayajula, Sulakshan
• Format: Conference Proceeding
• Language: English
• Subjects: Data Visualization; Deep Learning; Ensemble Learning; Exploratory Data Analysis; Feature extraction; Machine Learning; Machine learning algorithms; Manuals; Neural networks; Social networking (online); Threat assessment; Training
• ISSN: 2767-7788
• DOI: 10.1109/ICICT60155.2024.10544694

In the world that we live in today, malware and malicious messages circulate different systems causing havoc and issues. Hence in a cyber world where social media is prevalent and API requests simultaneously flooding, malicious content is a very serious concern. The traditional approach to the problem is done by comparing these messages with a core ruleset consisting of predefined signatures. This method is not accurate and has always fallen prey to updating the core set signatures. The project aims to develop a Machine Learning model capable of detecting these malicious messages and hence being more generalizable to detect the same. Various methods from linear, neural networks, and ensemble techniques are used to assess the difference in the performance of detecting these various malicious contents.