External Sinkhole Attack Detection in Large-Scale WSNs Using Metaheuristic Feature Selection

False alarms remain a major barrier to deploying network intrusion detection systems (NIDS). In high-volume environments, even a sub-1% false positive rate can generate tens of thousands of daily alerts. Filter-based feature selection is attractive because it operates upstream of the classifier and adds no inference-time cost. However, classical filters use class-symmetric criteria that ignore the asymmetry of intrusion detection, where benign traffic defines the baseline and attacks are deviations from it. A recent class-asymmetric filter, Classwise Mean Deviation (CMD), addresses this issue but anchors its score to a global mean that shifts toward attack distributions under class imbalance, weakening the deviations it aims to capture. We propose Benign-Anchored Ranking and Selection (BARS), a two-stage filter that replaces CMD's global anchor with the benign-class mean and applies an order-preserving decorrelation step. We evaluate BARS on CICIDS2017, CICDDoS2019, and UNSW-NB15 using feature budgets k = {5, 10, 20, 30, 40}. On attack-majority datasets, where global-anchor bias is strongest, BARS reduces false positive rate relative to CMD by 15.4% on UNSW-NB15 at k = 20 and by 21% to 23% on CICDDoS2019 at small feature budgets while preserving true positive rate and macro-F1. On benign-majority data, BARS and CMD converge, consistent with the theoretical limit where global- and benign-anchored scores coincide. BARS is a principled refinement of CMD rather than a universally dominant filter. Although Pearson Correlation and Mutual Information often achieve lower false positive rates, they exceeded 1 TB of memory on the largest benchmarks in our evaluation. BARS retains linear-time scoring and a low memory footprint, making it suitable for resource-constrained deployments.

Internet of Medical Things (IoMT) networks are hard to protect: devices are heterogeneous, computing resources are scarce, and traffic must be analyzed in real time. We present an intrusion detection system that addresses these constraints through feature selection. A Pearson correlation filter first removes redundant attributes; a hybrid strategy then combines model-based feature importance with SHAP attribution to pick a compact subset, on which we train Random Forest and LightGBM classifiers. SHAP and LIME explain what each retained feature contributes to the decisions. On CIC-IoMT 2024 and CIC-IDS 2017, the method cuts the feature space by up to 88% - from 40 to as few as 5 features - and accuracy and F1-score stay within a few points of models trained on all features. Compact, interpretable detectors of this kind are practical candidates for deployment on resource-limited medical networks.

Feature selection is critical for network intrusion detection systems (NIDS) operating under high-dimensional, highly imbalanced traffic, as found in operational and defense networks. Traditional filter methods rank features using global statistics computed symmetrically across classes and thus fail to capture the asymmetry of intrusion detection, where attacks are best characterized as deviations from dominant benign traffic. We propose benign-anchored Classwise Mean Deviation (nCMD), a lightweight and interpretable method that scores feature relevance based on the deviation of attack-class distributions from the benign-class mean, rather than a globally biased reference. This approach aligns feature selection with the operational semantics of NIDS at no additional computational cost. Across four benchmark datasets (CICIDS2017, CICDDoS2019, NSL-KDD, and UNSW-NB15), multiple feature budgets, and three downstream classifiers, nCMD matches or exceeds classical filter baselines in macro-averaged F1-score. It achieves the best result on three of the four datasets and under every classifier, with the strongest improvements observed under tight feature budgets and severe class imbalance. These results support benign-anchored ranking as a scalable and interpretable preprocessing component for resource-constrained NIDS.