Abstract
<p class="0abstract">Innovative applications are employed to enhance human-style life. The Internet of Things (IoT) is recently utilized in designing these environments. Therefore, security and privacy are considered essential parts to deploy and successful intelligent environments. In addition, most of the protection systems of IoT are vulnerable to various types of attacks. Hence, intrusion detection systems (IDS) have become crucial requirements for any modern design. In this paper, a new detection system is proposed to secure sensitive information of IoT devices. However, it is heavily based on deep learning networks. The protection system can provide a secure environment for IoT. To prove the efficiency of the proposed approach, the system was tested by using two datasets; normal and fuzzification datasets. The accuracy rate in the case of the normal testing dataset was 99.30%, while was 99.42% for the fuzzification testing dataset. The experimental results of the proposed system reflect its robustness, reliability, and efficiency.</p>
Highlights
Internet of Things (IoT) provides a technological movement towards effective physical-digital convergence
The presented work in this paper provides an effective intrusion detection based on deep learning using the Bot-IoT dataset
The attacks considered in Bot-IoT include Distributed DoS (DDoS), Denial of Service (DoS), OS and Service Scan, Keylogging, and Data exfiltration attacks
Summary
Internet of Things (IoT) provides a technological movement towards effective physical-digital convergence. It enables the development of intelligent systems interconnecting physical things surrounding us over the Internet. Such technological advancements have resulted in a broad scope of IoT applications in varying domains. Examples of these applications include smart homes, e-healthcare, smart grid, and intelligent transportation systems. This poses serious challenges concerning IoT security and privacy, which can delay the effective deployment of IoT systems, for data-sensitive applications
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