In daily life, the role of non-verbal communication is significant, and in overall communication, its involvement is around 55% to 93%. The proposed IDS achieved an accuracy of 99.7% using Thermostat, GPS Tracker, Garage Door, and Modbus datasets via voting classifier.įacial emotion recognition (FER) is an emerging and significant research area in the pattern recognition domain. This paper employed several machine learning classifiers and a deep learning model for intrusion detection using seven datasets of the TON_IoT telemetry dataset. The output of machine learning could be used to detect anomalies in IoT-network systems. As a result, machine learning algorithms generate correct outputs from a large and complicated dataset. The threads concerned with IoT need to be reduced for efficient Intrusion Detection Systems (IDSs). As the hackers use the Internet, several IoT vulnerabilities are introduced, demanding new security measures in the IoT devices of the smart city. Cyber threats are advancing day by day, causing insufficient measures of security and confidentiality. IoT enhances productivity and efficacy intelligently using remote management, but the risk of security and privacy increases. The idea of a smart city is to connect physical objects or things with sensors, software, electronics, and Internet connectivity for data communication through the Internet of Things (IoT) devices. Furthermore, existing challenges and possible solutions for networks security and privacy have been discussed. Deep learning techniques for IDS have been critically evaluated based on different performance metrics (accuracy, precision, recall, f-1 score, false alarm rate, and detection rate). In this review, public network-based datasets of IDS are fully explored and analyzed. This research aims to provide an inclusive analysis of intrusion detection based on deep learning techniques followed by different intrusion detection systems. Advanced deep learning techniques have been proposed for automatic intrusion detection and abnormal behavior identification of networks. Conventional techniques are not effective enough to cope the advanced attacks. Data confidentiality, integrity, and availability damage will occur in case of IDS prevention failure. Many Intrusion Detection Systems (IDS) have been developed recently due to most computer networks’ exposure to security and privacy threats. Smart homes, cities, grids, devices, objects, e-commerce, e-banking, e-government, etc., are different advanced applications of the evolving networks. However, intrusion detection in such big data is challenging. Consequently, security issues have also been arisen with the network growth. In the last decade, huge growth is recorded globally in computer networks and Internet of Things (IoT) networks due to the exponential data generation, approximately zettabyte to a petabyte.
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