Kernel-Level Network Threat Detection using Windows Filtering Platform and Machine Learning-Based Traffic Analysis

Driven by the exponential expansion of global digital communication networks and internet service dependency, contemporary cyber threats continue to scale in architectural complexity, making them exceptionally challenging to neutralize via legacy perimeter security models. Conventional intrusion detection models remain fundamentally restricted by their structural reliance on static signature-matching frameworks; while highly reliable when filtering historically cataloged exploits, these systems remain incapable of dynamically identifying modified variants or novel zero-day attack matrices. To address these architectural shortcomings, this treatise introduces an end-to-end hybrid network intrusion detection paradigm that couples realtime kernel-level packet collection using the Windows Filtering Platform (WFP) with adaptive machine learning-driven traffic analysis engines. The proposed defense framework captures inbound and outbound data streams directly within the operating system kernel network stack via custom WFP filters, thereby securing complete, unmodified traffic visibility before data payloads interact with user-space software layers. Captured data frames are continuously streamed into highly structured, metadatarich PCAPNG archives to facilitate deterministic analysis, reproducible research execution, and native compatibility with enterprise-grade network forensic suites. These serialized logs are sequentially ingested by an automated, multi-stage processing pipeline consisting of network flow aggregation, high-dimensional feature extraction, feature scaling normalization, and hybrid classification layers. The framework implements advanced ensemble and boosting classifiers, specifically Random Forest and XGBoost models, to categorize active communication sessions as either benign transactions or malicious behaviors. To minimize computational bottlenecks and suppress false-positive alerts, a fast, deterministic rule validation matrix operates in parallel with the machine learning models. This dual-engine strategy enables the architecture to efficiently flag both recognized, pattern-matched exploits and previously unseen structural anomalies. The underlying behavioral feature vector tracks precise packet transmission intervals, protocol metadata, cumulative session lifespans, interarrival time distributions, and specialized telemetry benchmarks modeled directly on the CICIDS2017 flow guidelines. A responsive, asynchronous graphical dashboard implemented in PySide6 delivers a user-centric console for viewing live system notifications, reconstructed network paths, transactional graphs, and hardware processing rates. The framework is engineered around a modular blueprint that isolates packet collection, inference logic, and interface rendering loops to support independent feature scaling and component drop-ins. Empirical benchmarking confirms that the designed system achieves exceptional threat classification accuracy alongside accelerated throughput velocities and low tracking latency, establishing its viability for active deployment within modern enterprise cybersecurity operations and live incident response workflows.