Abstract
In an Internet of Things (IoT) network, miscellaneous devices exchange their resources as per their requirement via the Internet. The data aggregated from these IoT devices are stored in the cloud layer. Increment in the number of IoT devices and accessing real-time data leads to a huge latency issue. One solution is to use the fog layer which is an adjunct layer between the cloud layer and the end-user. It is very convenient for the user to access the data from the fog layer as it is at the brink of the network. Security in the fog layer is a major drawback. Easy accessing of resources from the fog layer makes the system more vulnerable to various attacks. In this research work, network intrusion detection system (NIDS) is developed based on the conception of deep learning. NIDS is a device implemented in the fog node for attack detection. This model proficiently detects any kind of malicious activity. UNSW-NB15 and NSL-KDD datasets are used to evaluate the performance of the proposed NIDS model. Accuracy of the model is also compared with these datasets. The result analysis shows that the model is working more efficiently for the NSL-KDD dataset. The accuracy of the proposed NIDS model for the UNSW-NB15 and NSL-KDD datasets is 91.20% and 95.40%, respectively.
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Sahar, N., Mishra, R., Kalam, S. (2021). Deep Learning Approach-Based Network Intrusion Detection System for Fog-Assisted IoT. In: Tiwari, S., Suryani, E., Ng, A.K., Mishra, K.K., Singh, N. (eds) Proceedings of International Conference on Big Data, Machine Learning and their Applications. Lecture Notes in Networks and Systems, vol 150. Springer, Singapore. https://doi.org/10.1007/978-981-15-8377-3_4
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DOI: https://doi.org/10.1007/978-981-15-8377-3_4
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