A Survey on Attack Detection Methods For IOT Using Machine Learning And Deep Learning
Meenigi Ramesh Babu, K N Veena
Abstract
The Internet of Things (IoT) models are getting more complicated day by day with the rising demand in IoT automated network system. As the devices use wireless medium for broadcasting the data, it is easy to target for an attack. Due to the addition of different protocols in IoT, lakhs of attacks are emerging every day, which often provokes the computing process worsen, unstable, non-effective as well. In the local network, the normal communication attack is restricted to small local domain or local nodes. However, the attack present in IoT devices gets expanded to a large area that would cause destructive effects. The heterogeneity, distribution of IoT services/applications make the security of IoT a more challenging and complex one. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Thus, existing security techniques should be improved to secure the IoT environment viably. (ML/DL) have progressed impressively throughout the most recent couple of years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in various significant applications. The objective of this work is to give a thorough study of ML techniques and ongoing advances in DL methods that can be utilized to create upgraded attack detection models for IoT frameworks. We discuss the features and research gaps for each method in applying Machine learning and deep learning to IoT security.