Anomaly Based Intrusion Detection For IoT: (A Deep Learning Approach)
Abhishek Meena, Deepanshu Nigam, Deepesh Sharma, Anamika Chauhan · 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · 2021
The dependence of the world on the Internet is steadily increasing. Information is the world’s most valuable resource. It is most important to protect the information from unwanted hands. Information is stolen when the network breaks down. To implement such a world we require high-security Privacy, Authentication, and Recovery from attacks therefore the security of IoT has become a Critical Concern. Instead of traditional and Classical machine learning method used in previous research such as (K-Means Clustering, Decision tree, and K-Nearest Neighbour) because it does not grab all the aspects related to this new paradigm of communication and actuation, we have built an advanced Network Intrusion Detection System (NIDS) based on deep learning methodology. In this paper, we are focusing on exploring a new approach for security mechanisms design. We are using the "NSL-KDD" dataset for the development of a Deep learning model with the methodology of a Convolutional Neural Network.