Advanced Deep Learning Techniques for Information Security Vulnerability Detection Using Machine Learning

Abstract

The increasing rate on the complexity and amount of security threats demand more advanced information security vulnerability detection techniques. Traditional methods have limited capability to respond to such diverse and evolving threats in real-time. This paper presents a complete solution by using new machine learning models in combine with advanced mathematical approaches to organize the early detection and prediction of vulnerabilities that later content will be about the information security systems. We investigate the accuracy of different models (with an emphasis on modern approaches that go beyond the standard SVM, CNN, and GNN). It initiates with Bayesian Networks that use probabilistic graphical models for showing the interactions of diverse security characteristics to support reasoning about vulnerabilities as a function on the conditional dependencies. Decision Trees and ensemble techniques like Extreme Gradient Boosting (XGBoost) are able to deal with both heterogeneous data types and are more capable of modelling complex interaction between security features. For unsupervised cases, we use things like Isolation Forests for anomaly detection and Gaussian Mixture Models (GMM) to detect rare patterns in network packets that may be a sign of security attacks. We further investigate the possibility of reinforcement learning (i.e., Q-Learning) to continue to evolve with network changes and identify threats in real-world threatening environment. The learning process is streamlined and the interpretability of results is improved by integrating mathematical techniques such as Markov Chains (for modelling probabilistic transitions within network states) and optimization methods like Lasso Regression (for feature selection). In addition to this, the approach explores Autoencoder-based models but most specifically Variational Autoencoders (VAEs) for their unsupervised learning ability in identifying rare and new zero-day vulnerabilities. Extensive experiments on a variety of cybersecurity datasets demonstrate the effectiveness and efficiency of our approach, where substantial enhancements in detection speed and accuracy are achieved. The cloud-based cybersecurity model introduced in this research, backed by advanced machine learning models and mathematical frameworks could potentially serve to lay a foundation for the future of real-time cyber security defence mechanisms.