Predicting cyber hacking breaches using machine learning: a proactive approach to cyber security - Manisha B. Thombare, Ms.; Sunil T. Datir, Mr. (International Journal of Engineering and Technology, 2024)
Cyber security breaches pose significant threats to organizations and individuals, necessitating proactive measures for prediction and prevention. This paper proposes a novel approach for Cyber Hacking Breaches Prediction using Machine Learning (CHBPM), leveraging state of-the-art techniques to anticipate and mitigate potential breaches. Building upon previous research, we integrate machine learning algorithms, specifically Support Vector Machine (SVM) and Random Forest (RF), into a robust framework for real-time monitoring and analysis of website security. The system is trained on historical attack data, adaptively learning from successful and unsuccessful attempts to breach security protocols. Our methodology encompasses ensemble classification methods, including Random Forest, for efficient classification of network traffic data, thereby facilitating the identification of anomalous behaviour indicative of potential cyber threats. Through experimental analysis and evaluation owe demonstrate the efficacy of the proposed CHBPM in predicting cyber hacking breaches with improved accuracy and proactive response capabilities. This research contributes to the advancement of cyber security measures by providing a comprehensive framework for predicting and mitigating cyber threats using machine learning techniques, thereby safeguarding digital assets and enhancing resilience in an increasingly interconnected world. Real-time detection mechanisms are devised, employing the best-performing models to continuously monitor network traffic and issue alerts when suspicious activity or potential breaches are identified. The paper explores ensemble methods, such as Random Forest, to fortify detection accuracy and delves into deep learning techniques, including Neural Networks, to unearth intricate patterns within network data. Interpretability and visualization techniques aid security analysts in comprehending the models’ decision-making processes. A feedback loop ensures on-going system refinement, adapting to emerging threats and incorporating real-world feedback. Ethical considerations guide responsible data handling and model deployment, upholding privacy and consent standards. By project completion, ”Cyber Hacking Breaches Prediction and Detection using Machine Learning” aims to equip organizations and individuals with an adaptive, real-time defence against cyber-attacks. It aspires to render network breaches an increasingly formidable challenge, reinforcing our collective cyber security posture amidst a dynamic threat landscape.