Open AccessIntelligent Botnet Detection Approach in Modern Applications
Author(s) -
Khattab M. Ali Alheeti,
Ibrahim S. Alsukayti,
Mohammed Alreshoodi
Publication year - 2021
Publication title -
international journal of interactive mobile technologies
Language(s) - English
Resource type - Journals
ISSN - 1865-7923
DOI - 10.3991/ijim.v15i16.24199
Subject(s) - computer science , robustness (evolution) , botnet , intrusion detection system , internet of things , reliability (semiconductor) , computer security , fuzzy logic , the internet , artificial intelligence , world wide web , biochemistry , chemistry , power (physics) , physics , quantum mechanics , gene
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.