Authors:
R. Shyam, G. Chiranjeevi, R. Abhishek Reddy, Ali A. Khala, Swati Sah, Sandeep Kautish, Rejwan Bin Sulaiman

Addresses:
Department of Computer Science/Information Technology, Presidency College, Kempapura, Bengaluru, Karnataka, India. Department of Computer Science/Information Technology, Jain (Deemed-to-be University), Bengaluru, Karnataka, India. Department of Computer, University of Baghdad, Baghdad, Iraq. School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh, India. Department of Computer Science specializing in Intelligent Systems, Chandigarh University, Punjab, India. Department of Computer Science and Technology, Northumbria University, London, United Kingdom.                

Abstract:

In the present study, we tackle the problem of improving cybersecurity by creating a sophisticated artificial intelligence model that can precisely detect and categorize malware. In this work, we apply an autoencoder algorithm specifically designed to handle the high-dimensional and intricate data the Ember dataset contains. The objective is to develop an artificial intelligence system that can accurately identify benign and malicious executables and advance our knowledge of malware traits. Our goal is to capture the complex representations of the data by using an autoencoder. This will enable a more robust feature learning process, which is necessary to identify advanced cyber threats precisely. Although a thorough explanation of the model’s statistical metrics is saved for the paper’s main body, the abstract refers to encouraging findings that point to the autoencoder’s ability to produce a highly accurate AI for malware classification. This lays the groundwork for a safer online environment by utilizing machine learning to combat new and emerging cyber threats.

Keywords: Cyberattack Detection; Malware Detection; Autoencoders and Anomaly Detection; Endgame Malware Benchmark for Research (EMBER); Reinforcement Learning (RL); Recurrent Neural Networks (RNNs); Internet of Things (IoT).

Received on: 02/07/2024, Revised on: 07/09/2024, Accepted on: 30/10/2024, Published on: 14/12/2024

DOI: 10.69888/FTSIN.2024.000292

FMDB Transactions on Sustainable Intelligent Networks, 2024 Vol. 1 No. 4, Pages: 252-264

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