Authors : Dr. Koduru Prasadarao; Katikam Mahesh

Volume/Issue : Volume 11 - 2026, Issue 9 - September

Google Scholar : https://tinyurl.com/4srcexfb

DOI : https://doi.org/10.38124/ijisrt/26sep819

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Cyber attacks continue to evolve in volume, diversity, and complexity, making timely identification of malicious network activity an important cybersecurity requirement. This paper presents a deep learning framework for cyber attack detection and multi-class classification using transfer learning with convolutional neural networks. The proposed approach converts structured network-flow features into a representation suitable for convolutional feature extraction and adapts a pretrained convolutional backbone to the cybersecurity classification task. The workflow includes data preprocessing, feature normalization, class-label preparation, transfer learning, supervised fine-tuning, and evaluation using accuracy, precision, recall, F1-score, and confusion matrices. The approach is designed to reduce training requirements compared with learning a deep model entirely from random initialization while retaining the representation-learning capability of convolutional networks. An experimental protocol using a publicly available intrusion-detection dataset is described so that the results can be reproduced and compared with conventional machine-learning and non-transfer-learning deep models. The study also discusses class imbalance, domain shift, computational cost, and the need for careful validation before deployment in operational security environments.

Keywords : Cybersecurity; Intrusion Detection; Cyber Attack Classification; Transfer Learning; Convolutional Neural Network; Deep Learning; Network Traffic; Intrusion Detection System.

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