The dynamic contexts of heterogeneous Internet of Things (HetIoT) adversely affect the performance of learning-based network intrusion detection systems (NIDS) resulting in increased misclassification rates—necessitating an expert knowledge correlated evaluation framework. The proposed generalizable framework includes intrusion root cause analysis, correlation model, and correlated classification metrics that can be generalized over any NID dataset, corresponding expert knowledge, detection technique, and learning-based algorithm to facilitate context-awareness in reducing false alerts. To achieve this, we perform experimentations on the Bot-IoT dataset—with generalized traffic behaviors from multiple existing NID datasets—employing the Support Vector Machine (SVM) machine learning and Multilayer Perceptron (MLP) shallow neural network classifiers, demonstrating the generalizability, robustness, and improved performance of the propounded framework compared to the existing literature. Furthermore, the proposed framework offers minimal processing overhead on the classifier algorithms.

Due to demand for information ubiquity and large-scale automation, proliferating Internet-connected heterogeneous devices exhibit significant variations in data processing capacities, purposes, operating principles, underlying protocols, and dynamic contexts. As a result, adversarial entities exploit the increasing heterogeneous network (HetIoT) vulnerabilities, leading to frequent high-impact attacks due to anomalous device interactions and scarce knowledgebase. This paper presents a two-fold solution to the problem through a network intrusion detection and prevention framework for HetIoT, called \textit{HetIoT-NIDPS}. Firstly, we assign fault scores to the Expert-curated Knowledgebase (EK) framework, correlating with low-level alerts to assess threat severity and achieve context-awareness. Secondly, the proposed Beta distribution-based HetIoT traffic behavior approximation facilitates class imbalance invariance and improves classifier performance. Additionally, the HetIoT-NIDPS can detect zero-day attacks by identifying known attack variations upon encountering unseen traffic instances. Furthermore, the dynamic HetIoT contexts necessitate real-time threat assessment through online training—performed by analyzing small batches of network traffic samples. We propound the \textit{CorrELM} classifier based on the extreme learning machine algorithm and test the hypotheses on the Bot-IoT dataset. Finally, we prioritize the correlated alerts based on their severity, determined from root cause analysis and threat severity assessment tables. The results obtained prove that the proposed HetIoT-NIDPS framework is context-aware—producing reduced false alerts, class imbalance invariant—facilitating near real-time threat assessment with unbiased classifier performance, and generalizable—applicable to many NID datasets, which the existing techniques lack when combined.