Long-term electroencephalography (EEG) seizure prediction on wearable hardware requires models that are small enough for local inference, responsive to patient-specific distribution shifts, and robust to catastrophic forgetting. We present a code-faithful continual-learning extension of SlimSeiz that targets the latter two requirements while keeping the compact base predictor frozen. The implemented six-channel network contains 22K parameters and combines multi-scale one-dimensional convolutions with a selective state-space (Mamba) block. Continual adaptation adds identity-initialized patient adapters and rank-one low-rank adaptation branches, resulting in 22,726 total parameters and 452 trainable parameters for one active patient. Adaptation is controlled by two mechanisms. First, a drift-aware update gate combines standardized feature displacement, predictive uncertainty, and a consecutive-window persistence rule to decide whether a patient stream warrants training. Second, output-consistent replay stores historical logits with selected replay samples and constrains later predictions through temperature-scaled distillation, optionally combined with elastic weight consolidation or memory-aware synapses. Twelve available CHB-MIT training logs, each containing ten stratified segment-level folds, yielded macro-average accuracy, sensitivity, and specificity of 94.93%, 95.84%, and 94.12%, respectively, for the static base network. Unit tests, gradient-flow checks, and end-to-end synthetic tests verified the software behaviour of the continual-learning modules. Because the available artifact set does not contain completed multi-patient real-data ablations, we do not report unsupported reductions in forgetting or update cost. Instead, we define a leakage-aware ablation and feature-visualization protocol for prospective evaluation. This separation of verified evidence from pending experiments keeps the framework reproducible while avoiding the misrepresentation of implementation tests as clinical validation.
Xinlei Chen, Xiaobin Zhang, Dongming Zhao et al.· Journal of Mechanics in Medi...· 0 citations
Real-world IoT network security generates traffic at big-data scale with extreme class imbalance, temporal non-stationarity, and continuously evolving attack strategies that overwhelm static supervised classifiers. This paper presents a cognitive computing framework for network intrusion detection: a CNN–LSTM–DQN architecture with Prioritized Experience Replay (PER) evaluated on a 5,000,000-flow naturalistic sample of the TON_IoT Processed_Network dataset (4,000,000 training/1,000,000 temporally held-out test flows; 94.5% attack ratio) under a strict temporal split. The cognitive agent optimizes detection decisions using an Alerts per Million Flows (ARMF)-aware reward function that encodes both alert-fatigue cost and missed-attack penalty. We conduct a cross-attack-family generalization study: the methodology—architecture template, reward design, and hyperparameter calibration—is inherited from a framework previously validated on CSE-CIC-IDS2018, re-instantiated and retrained on the structurally different TON_IoT environment, and compared against the previously published benchmark. Initialization sensitivity is characterized across five independent random seeds using paired Wilcoxon signed-rank and t-tests. Across the five seeds, the proposed X2 model attains recall 0.833 ± 0.306 and F1 0.874 ± 0.241 (mean ± sample SD), versus the supervised X1 baseline at 0.858 ± 0.178 and 0.912 ± 0.116; the best-performing seed (42) achieves 97.52% accuracy, 98.02% attack recall, 99.46% precision, and 98.73% F1-score on 1,000,000 held-out XSS flows—an attack family entirely absent from training—with temporal stability variances of 4.63 × 10−7 (recall) and 1.38 × 10−7 (F1). The X2 advantage observed among the four stable seeds is not statistically demonstrated at n = 5 (statistical power ≈ 5.1%); the initialization-sensitivity finding itself, including one degenerate alert-suppression seed, is reported as a primary contribution. A formal, exactly additive ARMF decomposition distinguishes the detected-attack (structural) component (99.46%) from the model-induced false-positive component (0.54%), and we report a multi-seed, ARMF-aware cognitive IDS evaluation on naturalistic TON_IoT traffic under an unseen-attack-family test condition that, to the best of our knowledge, has not been reported in the surveyed RL-based NIDS literature.
Xin Su, Zhiquan Bai, K. Ramli et al.· Big Data and Cognitive Compu...· 1 citation
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