Optimizing Internet-Wide Port Scanning for IoT Security and Network Resilience: A Reinforcement Learning-Based Approach in WLANs with IEEE 802.11ah

ABSTRACT

The rapid proliferation of Internet of Things (IoT) devices has spurred the need for robust security mechanisms within wireless local area network (WLAN) environments. Specifically, the IEEE 802.11ah (WiFi-HaLow) technology offers low-power, long-range communication capabilities ideally suited for IoT devices, but these devices face security threats due to constrained computational resources. This research addresses the challenge of optimizing Internet-wide port scanning to enhance IoT security while minimizing disruptions to network performance. In this paper, we propose a novel reinforcement learning-based approach to achieve this optimization. Our approach harnesses the power of the Proximal Policy Optimization (PPO) algorithm to guide decision-making for IoT security and network performance enhancement. The proposed solution entails training an agent to dynamically adjust the port scanning rate, ensuring the delicate balance between security improvement and network responsiveness. We establish a comprehensive system model, encompassing WiFi-HaLow infrastructure, IPv6-enabled IoT devices, and the integration of Internet-security (IPSec) protocols. Mathematical formulations encapsulate constraints such as resource limitations and security-performance trade-offs. Our experimental setup utilizes a carefully curated dataset and a simulation environment to rigorously evaluate the proposed solution’s effectiveness. The proposed approach demonstrates remarkable security enhancement through the prevention of unauthorized access attempts, data breaches, and improved intrusion detection. Moreover, network performance is optimized, with reductions in latency, improvements in throughput, and energy-efficient operation. By presenting a plethora of comparison scenarios, we validate the superiority of our solution against various benchmarks. This research contributes a comprehensive framework that not only advances IoT security within WLAN environments but also highlights the intricate relationship between security and network performance optimization.

KEYWORDS:

• IoT security
• wireless local area network
• IEEE 802.11ah
• WiFi-HaLow
• network performance
• Internet-security
• Ipsec protocols
• reinforcement learning
• Proximal policy optimization
• port scanning

Disclosure statement

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

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Ethics approval

Compliance with Ethical Standards

Human and animal rights

This article does not contain any studies with human or animal subjects performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Notes on contributors

Shanthi Komatnani Govindan

K. G. Shanthi received her BE degree in Electronics and Communication Engineering from Madras University and ME degree in VLSI Design from Anna University, India, in 1996 and 2005 respectively. She rec ineived PhD degree in Information and Communication from Anna University, India, in 2015. She is in the teaching profession for the past 22 years and is currently associated with R.M.K college of Engineering and Technology affiliated to Anna University,Chennai, India. Her research interests include Antennas, VLSI Signal processing, Wireless networks, IoT, and machine learning.

Hema Vijayaraghavan

HEMA V BE, ME., is working as Assistant professor in the Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, since August 2022. She obtained her BE (CSE) from Jerusalem College of Engineering and ME (CSE) from Sathyabama University. She has been in the teaching profession for the past 20 years and has handled various subjects in UG and PG programmes in eminent Institutions in India and in Dubai (UAE). Her areas of interest are machine learning, AI and deep learning. She is currently pursuing her PhD in the field of Deep learning at Sathyabama Institute of Science and Technology.

Kolandairaj Kishore Anthuvan Sahayaraj

Kishore Anthuvan Sahayaraj K, is an Assistant Professor with 12 years of experience at SRM Institute of Science and Technology. He completed his BTech and MTech from Pondicherry University-affiliated college and received his PhD. from Annamalai University in computer vision and deep learning. He has published numerous papers in reputed journals.

Alphonse Mary Joy Kinol

A. Mary Joy Kinol. She received her BE degree in Electronics and communication Engineering from Sathyabama university in 2006 and MTech degree in VLSI Design from Sathyabama university in 2008 India and PhD from Sathyabama University in 2020. She has 13 years of teaching experience in various Engineering Colleges. She is currently working as head of department of Electronics and Communication engineering in Saveetha School of engineering, Chennai, India.She has published more than 20 papers in various journals and Conferences Her research interests include wireless communication, UWB communication systems, and multiple antenna systems, RF Circuit design and Microwave Communication. She is a life member of the ISTE.