Cyber Attack Detection and Classification Using Transfer Learning with Convolutional Neural Networks

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.