Intrusion prevention system using convolutional neural network for wireless sensor network - Pankaj Ramchandra Chandre; Parikshit Mahalle; Gitanjali Shinde (IAES International Journal of Artificial Intelligence (IJ-AI), 2022)
Now-a-days, there is exponential growth in the field of wireless sensor network. In wireless sensor networks (WSN’s), most of communication happen through wireless media hence probability of attacks increases drastically. With the help of intrusion prevention system, we can classify user activities into two categories, normal and suspicious activity. There is need to design effective intrusion prevention system by exploring deep learning for WSN. This research aims to deal with proposing algorithms and techniques for intrusion prevention system using deep packet inspection based on deep learning. In this, we have proposed deep learning model using convolutional neural network. The proposed model includes two steps, intrusion detection and intrusion prevention. The proposed model learns useful feature representations from large amount of labeled data and then classifies them. In this work, convolutional neural network is used to prevent intrusion for WSN. To evaluate and check the effectiveness of the proposed system, the wireless sensor network dataset (WSNDS) dataset is used and the tests are performed. The test results show that proposed system has an accuracy of 97% and works better than existing system. The proposed work can be used as future benchmark for the deep learning and intrusion prevention research communities.