Detection and Classification of Network Traffic in Bot Network Using Deep Learning

K. Srinarayani, B. Padmavathi, Kavitha Datchanamoorthy, T. Saraswathi, S. Maheswari and R. Fatima Vincy
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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

Journal of Information & Knowledge Management (JIKM), 2024, vol. 23, issue 06, 1-22

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.

Keywords: Botnet; CTU-13 dataset; long short-term memory (LSTM); residual neural network (ResNet) (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219649224500862

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