Machine Learning-Based Forecasting of Cybersecurity Threats in IoT Networks

Thesis Abstract

The rapid proliferation of Internet of Things (IoT) devices coupled with their integration into critical infrastructure has heightened the vulnerability of IoT networks to cybersecurity threats, necessitating advanced predictive approaches to safeguard these systems. Traditional reactive security measures are often inadequate due to the dynamic and sophisticated nature of cyber threats, underscoring the need for proactive, predictive solutions that can forecast potential attacks before they materialize. This study aims to develop and evaluate a machine learning-based framework for forecasting cybersecurity threats in IoT networks, thereby enhancing proactive security measures and minimizing potential damages. The specific objectives include assessing the efficacy of various machine learning algorithms in threat prediction, identifying key network features influencing threat susceptibility, and proposing a real-time threat forecasting model adaptable to diverse IoT environments. The research adopts a quantitative, exploratory research design grounded in the positivist paradigm, emphasizing empirical data collection and statistical analysis. The population comprises IoT network traffic datasets collected from industrial, healthcare, and smart home environments over a twelve-month period, amounting to approximately 5 terabytes of log data representing normal and anomalous activities. A stratified random sampling technique is employed to select a representative subset of 10,000 network flow records across the different environments, ensuring the inclusion of diverse threat types such as malware, denial-of-service attacks, and unauthorized access attempts. Data collection instruments include network traffic capture tools like Wireshark and intrusion detection system (IDS) logs, supplemented by labeled datasets created through simulated attack scenarios using tools such as Metasploit and custom attack scripts. Data preprocessing involves feature extraction, normalization, and handling class imbalance using Synthetic Minority Over-sampling Technique (SMOTE). Multiple supervised learning models—Random Forest, Support Vector Machine (SVM), Gradient Boosting Machines (GBM), and Neural Networks—are trained and validated using k-fold cross-validation to determine their predictive accuracies and operational efficiencies. Model evaluation employs metrics such as precision, recall, F1-score, Receiver Operating Characteristic (ROC) curves, and Area Under the Curve (AUC). Additionally, concepts from the Extended Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology (UTAUT) are integrated to interpret user interpretability and deployment feasibility. It is anticipated that the study will identify that ensemble models such as Random Forest and GBM outperform individual models in predictive accuracy, with F1-scores exceeding 0.85 across threat categories. Key network features likely to influence threat prediction include packet rate, source/destination IP entropy, protocol distribution, and anomaly scores from IDS logs. The developed real-time forecasting model is expected to demonstrate high predictive reliability, enabling early threat detection with minimal false positives. The study aims to contribute to existing cybersecurity literature by offering an empirically validated, scalable machine learning framework tailored to IoT networks, addressing significant gaps in adaptive threat forecasting and real-time anomaly prediction. This investigation underscores the importance of integrating machine learning techniques within IoT security architectures to facilitate swift threat anticipation and mitigation. The findings will inform policymakers and IoT system administrators on deploying intelligent, predictive security measures, ultimately reducing system downtime, data breaches, and operational risks. The study concludes that a combination of robust machine learning algorithms and strategic feature selection can significantly enhance the proactive defense mechanisms in IoT environments, recommending further research into integrating these models with existing security protocols and exploring their applicability in emerging IoT use cases such as smart cities and autonomous systems.