Enhanced Security of IoT Devices Using AI Approach

Abstract

The use of the Internet of Things (loT) and concerns about cybersecurity have both grown significantly in recent years. Intricate algorithms are developed using artificial intelligence (Al), which is at the forefront of cybersecurity, to protect networks and systems, including Internet of Things (loT) devices. Nevertheless. cybercriminals have learned how to use Al to their advantage and have begun employing adversarial Al in cybersecurity attacks. It compiles information on loT, Al, and assaults on and against Al from a few different studies and research publications. Therefore, from this our project aims to predict the attack and observe the vulnerability from lot devices and notify them using Machine learnings. Additionally, it combines many network-connected devices to produce smart, complex services that assist protect user privacy from dangers including eavesdropping, jamming, denial of service (DOS), and spoofing attacks and it examines the paradigm used by loT systems in conjunction with Al to enhance device security. also investigates reinforcement learning, unsupervised learning, and supervised learning as machine learning based loT security solutions and focuses on approaches for loT virus detection, safe offloading, and access control based on machine learning. The objective of this project is to create an intelligent video surveillance system capable of effectively monitoring and analysing real-time video streams to find and recognise objects, events, and anomalies. Modern deep learning models, like as convolutional neural networks (CN Ns). are used by the system to perform a variety of tasks that need spatiotemporal features for object detection, tracking, activity recognition, and abnormal event detection. This project aims the challenges and it need to be investigated and resolve security issues in iot devices using Deep learning and machine learning technique