A Machine Learning Framework for Cybersecurity Threat Prediction in Distributed Systems
Keywords:
Cybersecurity, Machine Learning, Threat Prediction, Distributed Systems, Intrusion Detection, Artificial Intelligence, Network Security.Abstract
The rapid adoption of distributed systems, including cloud computing platforms, edge networks, Internet of Things (IoT) infrastructures, and microservice-based architectures, has significantly transformed modern computing environments. While these systems provide scalability, flexibility, and high availability, they also introduce complex cybersecurity challenges due to their decentralized nature, heterogeneous components, and expanded attack surfaces. Traditional security mechanisms often rely on signature-based detection techniques, which are insufficient for identifying evolving and sophisticated cyber threats in real time. Consequently, there is a growing need for intelligent and proactive threat prediction mechanisms capable of anticipating malicious activities before significant damage occurs.
This research proposes a machine learning-based framework for cybersecurity threat prediction in distributed systems. The framework integrates data collection, preprocessing, feature engineering, model training, and threat prediction modules to analyze network traffic and system behavior. Multiple machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks, are employed to identify patterns associated with cyberattacks and predict potential threats. The framework is evaluated using benchmark cybersecurity datasets and assessed through performance metrics such as accuracy, precision, recall, F1-score, and false positive rate.
Experimental results demonstrate that the proposed framework achieves high prediction accuracy and effectively detects various attack categories, including denial-of-service, malware, and intrusion attempts. The study contributes a scalable and intelligent cybersecurity solution that enhances early threat detection, reduces response time, and improves the overall resilience of distributed computing environments against emerging cyber threats.
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