Author
Listed:
- K. Srinarayani
(Department of Computer Science and Engineering, Easwari Engineering College, Ramapuram, Chennai 600089, India)
- B. Padmavathi
(Department of Computer Science and Engineering, Easwari Engineering College, Ramapuram, Chennai 600089, India)
- Kavitha Datchanamoorthy
(Department of Computer Science and Engineering, Easwari Engineering College, Ramapuram, Chennai 600089, India)
- T. Saraswathi
(��Department of Information Technology, Easwari Engineering College, Ramapuram, Chennai 600089, India)
- S. Maheswari
(Department of Computer Science and Engineering, Easwari Engineering College, Ramapuram, Chennai 600089, India)
- R. Fatima Vincy
(Department of Computer Science and Engineering, Easwari Engineering College, Ramapuram, Chennai 600089, India)
Abstract
One of the most dangerous threats to computer networks is the use of botnets, which can seriously harm systems and steal private data. They are remote-controlled networks of compromised computers that an individual or group of individuals is using for malicious purposes. These infected computers are frequently called “bots†or “zombies†. A wide variety of malicious activities, including the distribution of malware and credential theft, can be carried out using botnets. The CTU-13 dataset is a collection of network traffic information that includes examples of various botnet types. Using this, our study compares the abilities of decision trees, random forests, 1D convolutional neural networks, and a proposed system based on long short-term memory and residual neural networks to detect botnets. According to our findings, the suggested system performs better than every other algorithm, achieving a higher accuracy rate. Our suggested system has the ability to precisely identify botnet traffic patterns, which can assist organisations in proactively preventing botnet attacks.
Suggested Citation
K. Srinarayani & B. Padmavathi & Kavitha Datchanamoorthy & T. Saraswathi & S. Maheswari & R. Fatima Vincy, 2024.
"Detection and Classification of Network Traffic in Bot Network Using Deep Learning,"
Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 23(06), pages 1-22, December.
Handle:
RePEc:wsi:jikmxx:v:23:y:2024:i:06:n:s0219649224500862
DOI: 10.1142/S0219649224500862
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