Abstract :

Fueled By Breakthroughs In Internet Technologies, Cyber-attacks Are Becoming Increasingly Sophisticated, With Botnet Attacks Emerging As One Of The Most Harmful Threats. Botnet Identification Is Challenging Due To The Wide Range Of Attack Vectors And The Continuous Evolution Of Malicious Software. As The Internet Of Things (IoT) Technology Expands, Many Network Devices Are Susceptible To Botnet Attacks, Leading To Significant Losses In Various Sectors. This Paper Proposes A Botnet Identification System Using A Long Short-Term Memory (LSTM) Model, A Popular Deep Learning Approach, To Effectively Distinguish Between Normal Network Traffic And Botnet Attacks. The Model Classifies Network Traffic Into Two Categories: Normal (0) And Botnet Attack (1). Experiments Were Conducted Using The UNSW-NB15 Dataset, Which Contains Nine Types Of Attacks, Including Normal, Generic, Exploits, Fuzzers, DoS, Reconnaissance, Analysis, Backdoor, Shell Code, And Worms. The LSTM-based Model Achieved An Impressive Testing Accuracy Of 90%. The Proposed Approach Demonstrates Strong Performance In Identifying Botnet Activities, With High Receiver Operating Characteristic (ROC) Area Under The Curve (AUC) And Precision-recall Area Under The Curve (PR-AUC) Scores, Indicating Its Effectiveness In Classifying Normal And Attack Traffic. Performance Comparisons With Existing State-of-the-art Models Further Validate The Robustness Of The Proposed LSTM-based Approach. This Research Contributes To Enhancing Cybersecurity Procedures By Providing A Reliable Tool For Detecting Botnet Attacks In Evolving Network Environments.

Published:

25-4-2025

Issue:

Vol. 25 No. 4 (2025)

Page Nos:

432-438

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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