Implementing optimized classifier for distributed attack detection and BAIT-based attack correction in IoT
Springer Science and Business Media LLC - Trang 1-16 - 2021
Tóm tắt
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. Machine Learning based solution is more promising to protect and detect the scheme that present in the abnormal state. This paper aims to implement a new attack detection system in IoT using KDD cup dataset. Initially, the possible paths from node to destination are created based on the Euclidean distance and connectivity between the nodes. Further, the path with minimum distance is chosen as the shortest path and data transmission takes place accordingly. Two phases of work is done, initial one is finding the presence of attacker by a pre-trained Optimized Deep belief network (DBN). Subsequently, if the presence of attacker is detected by DBN, the control is given to the bait process, which removes the corresponding attacker node. To ensure the precise detection process, the weights in DBN will be optimally tuned by a new Whale with Distance based Update (W-DU) algorithm. Finally, the performance of proposed system is evaluated over other traditional schemes with respect to parameters accuracy, specificity, precision, FPR, FDR and FOR.
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