Advanced Malware Analysis and Detection Using Deep Neural Networks
Artificial Intelligence; Cyber-attack; Deep Learning; Machine Learning; Malware Detection; Neural Network; Random Forest
Malware is malicious software that is used to cause harm to the computer systems, networks or users across several operating systems, such as Windows, macOS, iOS, Android and Linux. The identification and categorisation of malware is a difficult subject with no one-size-fits-all solution due to the constant evolution of the malware and lack of standardised detection frameworks. The use of deep learning models in cybersecurity encourages the growth of Explainable Artificial Intelligence (XAI) and Interpretable Machine Learning (IML) techniques. The goal of this research is to automate the way of analysing malware using a simple framework without the need of complex software. This article focuses on the application of Neural Networks, a deep learning model in detecting and analysing the behavior of malware. The model performed better when compared to other techniques by achieving an accuracy of 99.43%, precision of 99.05% and a F1 score of 0.99, which was trained on a large dataset containing 1,38,048 samples. 2025 IEEE.
Dominic Andrew, P.; Jose, Anna Mariya; Jayapandian, N.; Angel, Chinnapalli Neha; Brar, Krishjeet
Conference Proceedings - 2025 IEEE 4th International Conference on Data, Decision and Systems, ICDDS 2025;pp.7-12
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICDDS67737.2025.11344689 https://www.scopus.com/pages/publications/105033355386?origin=resultslist
Dominic Andrew P., Christ University, Department of Cse, Bangalore, India; Jose A.M., Christ University, Department of Cse, Bangalore, India; Jayapandian N., Christ University, Department of Cse, Bangalore, India; Angel C.N., Christ University, Department of Cse, Bangalore, India; Brar K., Christ University, Department of Cse, Bangalore, India