An Overview of Problems and Difficulties with ML in WSNs Protection

Abstract

There are serious, mutually exclusive problems with resources and security in wireless sensor networks. As security complexity rises, battery consumption will follow suit. It is useless to rely on the security of common protocols like encryption and key management because of the limited capacity of wireless sensor networks and their dynamic architecture. Algorithms for machine learning are one of the proposed ways to combine judgment, awareness and observation to deliver intelligence services in this kind of network. Machine learning algorithms provide new issues related to training and the amount of data required for training. This paper neatly covers the architecture of wireless sensor networks as well as the security challenges they face. It also goes into the challenges and recommended fixes for improving sensors' ability to identify dangers, assaults, hazards, and suspicious activity via their ability to learn and grow on their own using machine learning techniques. This might be achieved by reducing the cost of WSNs across several domains. This research also addresses open issues with machine learning algorithms that are pertinent to adapting them to the properties of sensors inside that kind of network.