Deep Learning based Frameworks for Real-time Cyber Threat Analysis

Abstract

Developing efficient deep learning-based frameworks for real-time cyber threat analysis is needed to enhance cybersecurity defenses. This research investigates the effectiveness of Convolutional Neural Networks (CNNs) in real-time cyber threat analysis within the domain of Cyber Security. The primary objective is to assess the capabilities of CNN-based frameworks in swiftly detecting, categorizing, and mitigating cyber threats in dynamic network environments. The study employs the widely used "NSL-KDD" dataset, sourced from 'the University of New Brunswick's Canadian Institute for Cybersecurity,' to evaluate the CNN-based framework's performance to identify malicious activities, anomaly detection, and behavior analysis within network traffic. The NSL-KDD dataset's comprehensive coverage of various attack scenarios and normal traffic instances serves as a benchmark to train and evaluate the proposed model. The evaluation tool utilized in this study is the widely adopted "TensorFlow" framework for assessing the CNN-based framework's effectiveness due to its robustness in handling deep neural networks and facilitating real-time analysis. This research comprehensively analyzes the CNN-based approach's strengths and limitations in real-time cyber threat analysis, considering factors such as model interpretability, scalability, and computational efficiency. By elucidating the performance metrics and insights derived from this evaluation, the paper aims to contribute to the ongoing discourse on leveraging Deep Learning (DL) methodologies for proactive cyber threat identification and response mechanisms.