A Framework for Adaptive Real-Time Cybersecurity Threat Detection Using Machine Learning

Thesis Abstract

The rapid evolution and increasing sophistication of cyber threats pose significant challenges to traditional cybersecurity measures, necessitating the development of dynamic and responsive threat detection frameworks capable of operating in real-time environments. This study aims to develop and evaluate an adaptable framework for cybersecurity threat detection leveraging machine learning techniques to enhance response accuracy and speed. The specific objectives are to identify key features for real-time threat detection, design an adaptive machine learning-based framework, and validate its effectiveness through empirical testing. The research adopts a mixed-methods approach, combining quantitative experimental analysis with qualitative evaluation, and employs a pilot study with a sample of 150 network traffic datasets collected from enterprise-level network environments. Data collection instruments include network traffic logs captured using intrusion detection systems (IDS) and structured questionnaires administered to cybersecurity professionals. The primary analytical techniques involve supervised learning algorithms such as Random Forest and Support Vector Machines (SVM), with feature selection performed via Recursive Feature Elimination (RFE). Additional statistical analyses, including analysis of variance (ANOVA), will assess the significance of detection performance across different threat scenarios, while thematic analysis will interpret qualitative feedback from cybersecurity experts regarding the framework’s operational viability. The framework emphasizes an adaptive learning mechanism that dynamically adjusts detection models based on evolving threat patterns, aligning with the theoretical foundation provided by the Situational Awareness Theory and the Adaptive Cyber Defense Model. It is hypothesized that the proposed framework will significantly improve detection accuracy and reduce false-positive rates compared to static models, evidenced by anticipated increases in true positive rates from 82% to 94% and reductions in false positives by 15%. Expected findings include superior performance of adaptive machine learning models in detecting zero-day exploits and insider threats in real-time, as well as enhanced resilience to adversarial evasion tactics. The research intends to contribute new insights into the integration of adaptive learning systems within cybersecurity architectures, enriching the theoretical understanding of dynamic threat modeling and detection. It also aims to provide practical guidelines for deploying scalable and self-adjusting cybersecurity solutions in diverse organizational settings. The study concludes that the adaptive framework offers a robust tool for real-time threat mitigation, capable of evolving alongside emerging cyber techniques. Recommendations include the adoption of the framework as part of enterprise security operations centers (SOCs), further research into integrating anomaly detection with behavior-based analytics, and exploration of deep learning approaches to augment detection capabilities. Overall, the research advances the field of cybersecurity by demonstrating the efficacy of machine learning-driven adaptive systems, fostering a proactive and resilient defense posture against increasingly sophisticated cyber adversaries.