Leveraging Artificial Intelligence Methodologies to Improve WSN Security

Wireless Sensor Network has become increasingly pervasive in various applications, extending from environmental monitoring to industrial automation. Due to the high uses of Wireless Sensor Network, there is a high chance of attacks and to make it secure from attacks Artificial Intelligence is extensively explored by the research community. Artificial Intelligence methodologies have shown a great impact to improve security measures in different domains. This paper explores the potential of leveraging Artificial Intelligence Methodologies to improve Wireless Sensor Network Security. The applications of artificial methodologies are identified as the tool to detect various types of anomalies in the network at prior manner that leads for provision of action for network administrators. The flow of the machine learning and deep learning techniques for accurately detecting attacks is presented in this paper. The recent work in the direction of incorporating machine learning and deep learning methods for wireless sensor network security is discussed at broad manner to find out the trending techniques in the field. The existing research gap is also presented in this article to lay down the future directions to boost the security policies in wireless sensor network technology. Furthermore, the paper addresses the challenges and the limitation of incorporating artificial intelligence into WSN security, like algorithm complexity, and the need for robustness against confrontational attacks. This paper also discusses cost-effectiveness and energy efficiency, and how these parameters will impact the process of utilizing WSNs with artificial intelligence.