Developing Machine Learning Models for Real-Time Network Anomaly Detection

Thesis Abstract

The increasing prevalence of sophisticated cyber threats and the exponential growth of network traffic necessitate the development of robust, real-time anomaly detection mechanisms to safeguard critical digital infrastructure. Traditional signature-based intrusion detection systems are often inadequate against novel and evolving threats, underscoring the need for intelligent, adaptable solutions rooted in machine learning. This study aims to develop and evaluate effective machine learning models capable of detecting network anomalies in real-time, thereby enhancing cybersecurity resilience. The specific objectives include (1) analyzing the characteristics of network traffic to identify indicative features of anomalies; (2) designing and implementing various machine learning algorithms, including Random Forest, Support Vector Machine (SVM), and Neural Networks, tailored for real-time anomaly detection; (3) optimizing these models for speed and accuracy to operate within the constraints of live network environments; and (4) providing a comprehensive comparison of model performances in detecting diverse types of network anomalies including DoS attacks, port scans, and data exfiltration attempts. The research adopts a quantitative, cross-sectional research design to facilitate systematic data collection and analysis. The population comprises network traffic data captured from a large corporate network environment encompassing approximately 1000 endpoints over a span of six months, representing diverse traffic patterns. A stratified random sampling technique was employed to select 10,000 network traffic sessions for analysis, ensuring representative inclusion of benign and malicious traffic. Data collection instruments included network flow logs, packet capture data, and labeled datasets obtained from open-source repositories such as DARPA Intrusion Detection Data Sets and UNSW-NB15. Preprocessing involved feature extraction and normalization, with emphasis on flow-based features, packet size, inter-arrival times, and protocol-specific attributes. Validity and reliability of the dataset were validated through cross-referencing with ground truth labels and inter-rater reliability measures. Analytical techniques employed include supervised machine learning classification algorithms, with model performance evaluated using metrics such as accuracy, precision, recall, F1-score, and Receiver Operating Characteristic (ROC) curves. Model selection and development were guided by the theoretical framework of anomaly detection utilizing the Anomaly Detection Paradigm and the Signal Detection Theory, which inform the differentiation of normal and abnormal network behaviors. The models were optimized via hyperparameter tuning using grid search and cross-validation to prevent overfitting and ensure robustness. The impact of feature selection was assessed through Principal Component Analysis (PCA), and models were integrated into a real-time detection system prototype to evaluate operational feasibility. Expected findings suggest that machine learning models, particularly ensemble techniques such as Random Forest and advanced neural network architectures exemplified by Deep Learning models, will outperform traditional rule-based systems in both detection accuracy and adaptability. The models are anticipated to achieve detection accuracies exceeding 95% with minimal false-positive rates under real-time constraints, effectively identifying a broad spectrum of network anomalies. The comparative analysis is expected to reveal trade-offs between model complexity, computational efficiency, and detection performance, guiding best practices for deployment in live network environments. This study contributes significantly to the field of cybersecurity by advancing the application of machine learning for real-time network anomaly detection, offering empirically validated models that can be integrated into existing security frameworks. It demonstrates a practical approach toward developing scalable, adaptive detection systems capable of responding swiftly to emerging threats. The research concludes with recommendations for deploying optimized models in production systems, considering computational resource limitations, and emphasizes the need for continuous model retraining to adapt to evolving network behaviors. Future research avenues include exploring unsupervised learning techniques for zero-day threat detection and integrating threat intelligence feeds to enhance contextual awareness in anomaly detection systems.