Abstract
Intelligent environments can improve the quality of human existence by reducing inconveniences and maximizing productivity. An Internet of Things (IoT) concept has recently established into a method for making smart atmospheres. Protecting users’ personal information and preventing unauthorized access are paramount in any IoT-based smart ecosystem used in the real world. Smart environment applications face security risks from Internet of Things-based technologies. Accordingly, intrusion detection systems (IDSs) that are adapted to IoT settings are crucial for reducing the risk of attacks that exploit vulnerabilities in the IoT. Due to the inadequate processing and storage abilities of IoT technology and the specific protocols in use, traditional IDSs may not be applicable. In this article, we will discuss the most up-to-date intrusion detection systems (IDSs) for the IoT, with an emphasis on the appropriate approaches, features, and procedures. This research presents a Deep Belief Network (DBN) based Intrusion Detection System (IDS) employing the K-Nearest Neighbor technique for monitoring IoT networks for intrusions (KNN). Step one of this process involved using an Absolute Maximum Scaling (AMS) notion of normalization to the UNSW-NB15 dataset in order to cut down on data loss. This dataset, which consists of both recent attacks and normal network activity, covers nine distinct attack types. The next step was to use Principal Component Analysis (PCA) to minimize the dimensionality (PCA). Additionally, we have compared our method to other state-of-the-art studies. Experiments showed that DBN-KNN was superior to other intrusion detection systems in IoT technology.
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Geetha, C., Gilda, A.J., Neelakandan, S. (2023). Deep Belief Network Algorithm-Based Intrusion Detection System in Internet of Things Environments. In: Hasteer, N., McLoone, S., Khari, M., Sharma, P. (eds) Decision Intelligence Solutions. InCITe 2023. Lecture Notes in Electrical Engineering, vol 1080. Springer, Singapore. https://doi.org/10.1007/978-981-99-5994-5_12
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