Mitigating Cybersecurity Threats in Modern Networks Using Intelligent Approach

Mahmoud A. Zaher¹*, Yahia B. Hassan² , Nabil M. Eldakhly³

¹ Faculty of Artificial Intelligence, Data Science department, Egyptian Russian University (ERU), Cairo, Egypt

² Electrical Eng. Dept, Higher Institute of Engineering, Minia, Egypt,

³ Faculty of Computers and Information, Sadat Academy for Management Sciences, Cairo, Egypt &

 French University in Cairo, Egypt

Emails: mahmoud.zaher@eru.edu.eg; dryahiabahaahassan@gmail.com; nabil.omr@sadatacademy.edu.eg

Abstract

The proliferation of botnet threats within Internet of Things (IoT) networks has underscored the critical need for robust detection mechanisms. This study addresses this imperative by presenting a comprehensive framework employing Machine Learning (ML) techniques for botnet detection. Leveraging a dataset sourced from authentically compromised IoT devices, the research delves into the intricate behaviors exhibited by botnets, emphasizing the encounters pretended by their polymorphic characteristics. A convolutional neural network architecture, featuring stacked layers with residual connections, serves as the cornerstone of the proposed detection system. The efficiency of the developed model is evaluated using meticulous visualization of data insights, learning behaviors, and detection performance, which demonstrate a great ability to discriminate between different botnet activities. This study presents a prominent improvement to the cybersecurity field by developing an effective solution for invigorating IoT network defenses against developing botnet threats, which highlights the essential role of ML-driven methods in the preservation of the integrity of interconnected devices.

Keywords: Cybersecurity; Network Security; Intrusion Detection; Anomaly Detection; Machine Learning (ML); Threat Detection; Behavioral Analysis.