A Review on Deep Learning and Intrusion Detection System Technologies to Secure IoT
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Abstract
Today the usage of internet has increased and the emergence of new technologies has invaded computer systems and networks. IoT is a new technology which opens up opportunities for new services and new innovations that is enabled by the developments in RFID, smart sensors and communication technologies. The fundamental aspect is to have smart sensors that communicate directly without human intervention to deliver a new application. All objects will be connected and are able to communicate with each other, while they operate in unprotected environments. This aspect leads to major security challenges. Companies are increasingly investing in these areas of research to optimize the detection of these attacks. Intrusion Detection Systems (IDS) are a vital tool for the protection of networks and data. Insights derived from the raw IoT data is highly complex that goes beyond the competence of traditional data analytical paradigms. Deep Learning models are better than conventional machine learning paradigms in the following ways. First, they mitigate the requirement for supervised feature sets to be utilized for training so that the features that might not be recognizable to a human can be extracted smoothly by Deep Learning models. This work focuses on the review related to IoT, IDS and Deep learning, traversing different areas related to security issues in IoT domain.
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