Intelligent Botnet Detection Approach in Modern Applications

Abstract

Innovative applications are employed to enhance human-style life. The Internet of Things (IoT) is recently utilized in designing these environ-ments. Therefore, security and privacy are considered essential parts to deploy and successful intelligent environments. In addition, most of the protection sys-tems 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 protec-tion 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.