Lightweight IoT IDS keeps accuracy with three to 15 features
On CICIoT2023, three features reached MCC 0.9622 and 15 preserved 99.97% of a 38-feature baseline.
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Stories tagged #shap
MLflow SHAP load_explainer bug allows RCE (CVE-2026-118627)
ZDI rates the deserialization flaw 7.8 and says the victim must open a malicious page or file.
Paper ties singing emotion recognition to interpretable vocal technique cues
SHAP and Grad-CAM link predictions to vibrato and pharyngeal technique, while singing-specific features reduce cross-singer variability.
Ensemble models predict heat-exposed concrete strength at R² 0.903
On 1,204 records, gradient boosting had the lowest error at 6.67 MPa RMSE and was packaged in a Python GUI.
Trigger Value Selection in Clean-Label Malware Backdoors: An Effectiveness-Detectability Analysis
Researchers show trigger-value choice changes success and detectability of clean-label backdoors in malware classifiers.
Systematically Optimized CNN-Transformer with Focal Loss for Imbalanced Intrusion Detection on NSL-KDD
A tuned CNN-Transformer with Focal Loss reaches 98.73% accuracy on imbalanced NSL-KDD detection.
Uncertainty and Explainability in Deep Rough Volatility: A Neural Information-Theoretic Posterior Approach
Neural posterior inference calibrates rough Heston models and uses Hellinger-SHAP to explain implied-volatility information gain.
Detecting GPT-Assisted Writing Using Interpretable Stylometric Features
Random Forest using stylometric features detected ChatGPT-assisted student writing with ROC-AUC 0.87.
Detecting Deceptive Recruitment: A Signal-theoretic Machine Learning Framework for Early Identification of Labour Exploitation
Researchers build a multimodal ML framework detecting deceptive job advertisements used for labour exploitation, achieving ROC-AUC 0.87–0.97 across individual modalities.
DynSHAP: Towards Explainable Dynamic Survival Analysis
DynSHAP extends SHAP explainability to dynamic survival analysis, treating time-feature pairs as Shapley players for longitudinal clinical predictions.
Self-Verifying Anomaly Detection using Explainable AI for Cybersecurity of DER Networks
ExCYDER framework self-verifies anomaly detection alerts for DER power grids using LightGBM and SHAP, reaching over 98% detection accuracy.
Why Is SHAP Not a Reliable Standalone Explanation Framework for Malware Detection?
arXiv paper shows SHAP gives unreliable standalone explanations for malware detection, with attribution dilution and sign reversal in dependent PE feature spaces.