A Lightweight CNN with LSTM Malware Detection Architecture for 5G and IoT Networks

Abstract

Fifth-generation (5G) mobile networks are being deployed all over the world and researchers are concerned about the network's security against hacking. Researchers have focused heavily on this topic in recent years as new technologies attempt to integrate into numerous sectors of corporate and social organizations. Malicious software referred to as malware, is an unwanted application that attackers frequently use to execute online attacks. Advanced packing and obfuscation methods are being used by malware variants to continue their evolution. Due to several application features in 5G networks such as APIs, calls and SMS, it is difficult to detect and classify malware attacks. In a variety of research areas, deep learning (DL) techniques are effectively utilized to address malware-related solutions. Especially, convolutional neural networks (CNN) played a crucial role in the classification of malware detection in 5G networks and the Internet of Things (IoT). In this work, a lightweight CNN architecture with a sequential Long Short-Term Memory (LSTM) layer is developed for malware detection and classification trained on the Malimg dataset. The results prove that the proposed approach achieves 99.8% accuracy with an F1-score of 0.9925 for malware detection and outperforms state-of-the-art approaches in the literature. The classification performance is improved up to 12.8% and 14% in terms of accuracy and F1-score, respectively when compared with existing malware classification models.

KEYWORDS:

• CNN
• 5G networks
• IoT
• LSTM
• Malware detection

Disclosure statement

No potential conflict of interest was reported by the author(s).

DATA AVAILABILITY STATEMENT

The details of the datasets used for the validation of the proposed method are provided in the manuscript itself.

Additional information

Notes on contributors

S. Dhanasekaran

S Dhanasekaran received his BE degree in electronics and communication engineering in 2008 from Sri Balaji Chockalingam Engineering College, Arani, Tamil Nadu, India. He completed his ME in communication systems in 2010 from PSG College of Technology, Coimbatore, Tamil Nadu, and India. He completed his PhD in 2022 from Anna University Chennai in the area of communication systems, MIMO, OFDM, etc., He is currently working as an assistant professor in the department of electronics and communication engineering, Sri Eshwar College of Engineering, Coimbatore. He has around 12 years of teaching experience. He is a lifetime member of ISTE. Corresponding author. Email: [email protected]

T. Thamaraimanalan

T Thamaraimanalan received his BE degree in electronics and communication engineering from Anna University, Chennai in 2006, an ME degree in VLSI design from Anna University of Technology, Coimbatore in 2010, and a PhD degree from Anna University in 2020. He is currently working as an assistant professor in the ECE department at Sri Eshwar College of Engineering, Coimbatore. His research area includes low-power VLSI design, testing of VLSI circuits, biomedical signal processing, internet of things (IoT), etc. He has published several papers in national and international journals. He is a lifetime member of ISTE and IAENG. Email: [email protected]

P. Vivek Karthick

P Vivek Karthick received a bachelor's degree in electronics and communication engineering from Anna University of Technology, Tamil Nadu, India, in 2011 and a master's degree in VLSI design from Anna University, Tamil Nadu, India in 2013. He has 9+ years of experience in teaching and at present he is working as an assistant professor in the department of ECE, Sona College of Technology, Salem, Tamil Nadu and pursuing his PhD degree in faculty information and communication engineering under the guidance of Dr. Ramesh Jayabalan in PSG College of Technology Coimbatore, India. His main research interests include the design of VLSI data path elements, high-speed VLSI signal processing, and low-power VLSI design for wireless communication architectures. Email: [email protected]

D. Silambarasan

D Silambarasan received his BE in electronics and communication and ME-VLSI design from Anna University. Currently, he is doing research in reversible architecture design for image restoration and he is currently with Sri Venkateswara College of Engineering, Chennai working as an assistant professor. Formerly, he was working as a teaching fellow at Anna University, Coimbatore. His research interests include image processing, low power circuits, approximate, and parallel computing. Email: [email protected]