Journal Article

Augmenting Cyber Defense Counter To Zero-Day Attacks Through Predictive Analysis- A Fusion Methodology Assimilating Game Theory and RESNet Inspired Optimization Techniques

Akshaya, Swathy, Ganapathi, Padmavathi

2024

Overview

Zero-day attacks pose a significant threat to software vendors, as they exploit previously unknown vulnerabilities, making them insidious and challenging to defend against. Predictive analysis offers a proactive approach to zero-day attack detection, enabling organizations to anticipate and mitigate threats before they manifest. By leveraging advanced techniques such as machine learning and game theory, predictive models can identify emerging attack patterns and adapt in real time to evolving threats. This paper proposes a zero-day attack optimization technique using supervised learning algorithms to identify system disruptions effectively. This paper presents innovative approaches to zero-day attack identification using advanced techniques such as Probabilistic Graph-based Back Propagation Neural Networks, Modified Bi-LSTM with Game Theory, ANN Auto Encoder, Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) and Residual Network (RESNET50). Multiple machine learning approaches were used to determine the most suited model for predicting zero-day assaults. A deep-convolutional n-zero-day network is introduced to distinguish zero-day malware from legitimate software, employing feature selection techniques and a diverse range of machine learning algorithms. This paper presents a novel methodology integrating Hybrid Game Theory (HGT) with Transfer Learning (TL), incorporating feature selection strategies and building upon earlier research. This paper contributes to the field by offering a comprehensive methodology for zero-day attack prediction and highlights areas for further research to address existing limitations and optimize outcomes in network security. Results demonstrate the efficacy of the proposed approach, achieving high detection rates and accuracy in identifying disruptions to network systems.

• Publisher: Kohat University of Science and Technology (KUST)
• Subject: Accuracy / Algorithms / Artificial neural networks / Back propagation / Back propagation networks / Deep learning / Defense / Exploitation / Feature selection / Game theory / Intrusion detection systems / Machine learning / Malware / Methodology / Neural networks / Optimization / Optimization techniques / Prediction models / Programmers / Propagation / Software / Supervised learning / Threat evaluation