Enhancing Trojan Detection with Machine Learning and Deep Learning: Exploratory Data Analysis and Beyond - Aavula, Ravi; Sree lakshmi, A.; Madhu Babu, BNV; Rao, Bandla Srinivasa; Veeramallu, Bobba; V.R.R, V.R.R; Vengatesh, T. (Journal of Neonatal Surgery, 2025)
Trojans are cunning forms of malware that are constantly expanding, posing an increasing threat to the cyber security landscape. As a result, this study embarks on a mission to enhance trojan identification by fusing the strengths of Machine Learning (ML) and Deep Learning (DL) with the crucial methodology of Exploratory Data Analysis (EDA). Our objective is to create a robust trojan detection system that can adjust to the dynamic nature of trojans and act swiftly in the face of new dangers. In this study, we provide a thorough analysis of the trojan detection domain, focusing on malware, current detection methods, and the crucial function that ML and DL play in cybersecurity. We also stress the value of EDA in locating latent data patterns and enhancing feature engineering. In this study, we present a demonstration of our methodical approach, which entails data collection and pre processing, meticulous EDA, ML and DL model creation, and the fusion of these two paradigms. The experimental findings are then explained, focusing the performance indicators and actual case studies that show how beneficial our approach is in the real world. We show the significance of EDA, ML, and DL in defending computer systems against Trojans by explaining the research's findings