Performance and security Evaluation of Ad Hoc Networking Technologies for IoT Environments

Abstract

A new age of unparalleled connectedness has been ushered in by the fast growth of Internet of Things (IoT) devices, allowing everything from smart homes and industrial automation to healthcare and agriculture. Ad hoc networking technologies play a pivotal role in facilitating communication among IoT devices, providing the flexibility and scalability required to support the diverse and dynamic IoT ecosystem. However, this connectivity comes with significant challenges related to both performance and security. This paper presents a comprehensive study focused on the performance and security evaluation of ad hoc networking technologies in the context of IoT environments. The need for enhancing security in ad hoc networking has seen a significant growth over the last decade. There are several security systems that prioritise the detection of assaults. Various machine learning mechanisms are taken into consideration for the purpose of identifying attacks. However, a significant concern associated with current research endeavours is to the constrained aspects of security and performance. There is still a need to improve the security of ad hoc networking. To accomplish this purpose, hybrid mechanisms have been devised. Advanced machine learning techniques are used to classify attacks such as man-in-the-middle attacks, denial of service attacks, and brute force attacks. In order to improve the precision of decisions and the categorization of assaults in ad hoc networks, the suggested research makes use of the Long Short-Term Memory (LSTM) model.