Model Development and Optimization of Data Security Attack Detection Using Neural Network Technology

Abstract

This research examines the development and optimization of a data security attack detection model utilizing an artificial neural network (ANN) approach. The primary objective is to enhance the robustness and effectiveness of data security systems in addressing the growing complexity of cyber threats. By leveraging state-of-the-art ANN technologies, this research seeks to improve the efficiency and accuracy of detecting various types of security attacks, including Malware, DDoS, and Intrusion, in dynamic network environments. The methodology involves an in-depth analysis of comprehensive security attack datasets, the application of advanced optimization techniques, and the implementation of cutting-edge ANN models. Additionally, this study integrates machine learning methods such as Convolutional Neural Networks (CNN), Random Forest (RF), and Support Vector Machines (SVM) to evaluate and compare their performance in threat detection. Through a rigorous analysis, the strengths and limitations of each model are assessed to identify the most effective approach for classifying and mitigating security threats. The findings underscore the potential of ANN technologies in ensuring data integrity and resilience against increasingly sophisticated cyberattacks. However, challenges such as dataset imbalances and model biases necessitate further refinement. This research contributes to the advancement of cybersecurity by providing critical insights into the application of ANN technologies and fostering innovative strategies for developing adaptive and reliable security solutions to meet the demands of the evolving digital landscape.