Volume 17, Number 4

Enhancing Cyber Defense Against Zero-Day Attacks using Ensemble Neural Networks

  Authors

Swathy Akshaya and Padmavathi, Avinashilingam University, India

  Abstract

Zero-Day Attacks (ZDAs) are a significant concern for cybersecurity as they take advantage of previously unknown vulnerabilities in software systems. This lack of prior knowledge makes ZDAs extremely difficult to detect as they operate in stealth mode, often evolving as new ideas and approaches emerge in the cybersecurity landscape. Herein, we introduce a hybrid deep learning framework comprising four models, including Artificial Neural Network – Auto Encoder (ANN-AE), ResNet50, CNN-LSTM, and Modified Bi-LSTM with Game Theory (GT), to improve the prediction and detection of ZDAs. Each model is used in a particular manner: ANN-AE for feature compression and anomaly detection, ResNet50 for feature extraction, CNN-LSTM for capturing spatio-temporal patterns, and Bi-LSTM with GT for modelling attacker-defender interactions. To enhance accuracy and model reliability, we applied the Optimised Levy Flight-based Optimisation Algorithm (OLFOA) in hyperparameter optimisation. We empirically evaluated the proposed approach on two publicly available benchmark datasets, achieving favourable results, specifically high detection accuracy, low false alarm rates, and low computational cost. Our results substantiate the proposed approach to facilitate real-time ZDA prediction and detection and denote the potential for future application in cybersecurity.

  Keywords

Zero-Day Attack Prediction, Hybrid Game Theory, Transfer Learning, ResNet50, ANN-AE, CNN-LSTM, Bi-LSTM, Ensemble Neural Networks, OLFOA.