International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | An Efficient Deep Learning based Prediction Model for Botnet Attack Detection |
|---|---|
| ABSTRACT | Botnet attacks are a major threat to computer networks, allowing hackers to control many infected devices at once to launch attacks like DDoS, steal data, and spread spam. Older detection methods, such as signature-based systems and basic machine learning models, often fail to detect new and hidden botnet attacks because these attacks keep changing their patterns. To solve this problem, this paper presents an efficient deep learning model using Convolutional Neural Networks (CNN) to detect botnet attacks accurately. The proposed method first cleans and prepares the network traffic data, then extracts important features that help identify unusual or harmful traffic. The CNN model is then used to automatically learn patterns from this data and classify network traffic as either normal or botnet-related. The model is tested on a standard botnet dataset, and the results show that it achieves high accuracy, precision, recall, and F-measure, along with a low error rate. This proves that the proposed CNN-based model is an effective and reliable solution for detecting botnet attacks in real-world network systems. |
| AUTHOR | SURAJ KUMAR, DR. DAYASHANKAR PANDEY M. Tech Scholar, Department of CSE, Sarvepalli Radhakrishnan University, Bhopal, India Professor, Department of CSE, Sarvepalli Radhakrishnan University, Bhopal, India |
| VOLUME | 187 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1408016 |
| pdf/16_An Efficient Deep Learning based Prediction Model for Botnet Attack Detection.pdf | |
| KEYWORDS | |
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