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

Swathy Akshaya; Padmavathi Ganapathi

Author(s)

Swathy Akshaya Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamilnadu, India

Padmavathi Ganapathi Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamilnadu, India

Abstract

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.

Publication Details

Page(s) 1-104

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 3, Year: 2024

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Keywords

Resnet, Zero-Day Attacks, Hybrid Game Theory, Fruit Fly Optimization Algorithm, Zero-Day Attack Prediction

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