Synthesis of Network Traffic Analysis, Security Frameworks, and IoT Threat Detection Using Machine Learning Techniques

Modern communication networks operate under constantly changing conditions, driven by large-scale digital services, cloud computing and the rapid growth of Internet of Things (IoT) deployments. This combination has led to networks that are both heavily loaded and increasingly exposed to cyber threats. Traditional monitoring and rule-based security mechanisms, which mainly depend on manually crafted thresholds or signatures, struggle to keep pace with this environment. Machine learning (ML) offers a different approach by learning patterns directly from data, supporting both proactive traffic prediction and dynamic threat detection. This paper brings together three domains that are often considered separately: network traffic analysis and forecasting, security frameworks, and IoT- focused threat detection. It discusses how statistical and ML- based techniques, including ARIMA, GARCH, LSTM, CNN and BLSTM-RNN, can be applied individually and in combination to enhance the resilience of modern networks. Particular emphasis is placed on realistic deployment contexts such as Internet service providers (ISPs), enterprise infrastructures and smart-city IoT systems. The study argues that future network security will be shaped by solutions that embed predictive capabilities into layered and distributed architectures, enabling earlier detection of anomalies and more informed mitigation strategies.