Intelligent Botnet Detection Approach in Modern Applications

Authors

  • Khattab M. Ali Alheeti Computer Networking Systems Department, College of Computer Sciences and Information Technology, University of Anbar,
  • Ibrahim Alsukayti Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia
  • Mohammed Alreshoodi Department of Applied Science, Unizah Community College, Qassim University, Buraydah, Saudi Arabia

DOI:

https://doi.org/10.3991/ijim.v15i16.24199

Keywords:

, IDS, IoT, deep neural networks, DDoS, Bot-IoT.

Abstract


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.

Author Biographies

Khattab M. Ali Alheeti, Computer Networking Systems Department, College of Computer Sciences and Information Technology, University of Anbar,

Computer Networking Systems Department, College of Computer Sciences and Information Technology, University of Anbar,

Ibrahim Alsukayti, Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia

Department of Computer Science, College of Computer, Qassim University, Buraydah,

Saudi Arabia

Mohammed Alreshoodi, Department of Applied Science, Unizah Community College, Qassim University, Buraydah, Saudi Arabia

Department of Applied Science, Unizah Community College, Qassim University, Buraydah, Saudi Arabia

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Published

2021-08-23

How to Cite

M. Ali Alheeti, K., Alsukayti, I., & Alreshoodi, M. (2021). Intelligent Botnet Detection Approach in Modern Applications. International Journal of Interactive Mobile Technologies (iJIM), 15(16), pp. 113–126. https://doi.org/10.3991/ijim.v15i16.24199

Issue

Vol. 15 No. 16 (2021)

Section

Papers

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