The paper proposes an SE ViT-BiLSTM hybrid model for enhanced intrusion detection in IIoT and IoMT environments, achieving superior performance on real-world datasets, especially after data balancing.

This paper proposes a novel, on-device, interpretable Tsetlin Machine (TM)-based Intrusion Detection System (IDS) for IoMT environments, achieving high classification accuracy while providing transpar…

The paper proposes an attention-guided hybrid framework combining 1D and 2D CNNs to robustly enhance ECG-based biometric recognition, achieving high accuracy across multiple datasets and demonstrating…

This paper proposes a novel Tsetlin Machine (TM)-based Intrusion Detection System (IDS) that effectively detects diverse cyberattacks in IoMT networks, achieving high accuracy and providing enhanced i…

The paper proposes the Distilled Explanation Model (DEM), a novel glass-box framework that achieves high-accuracy, clinically interpretable anomaly detection in physiological sensor data by distilling…

The paper proposes a Sovereign AI architecture for clinical triage that ensures maximum security by performing all inference on-device and receiving data only through physically unidirectional channel…

A hybrid deep learning model combining ResNet-1D, BiGRU, and Multi-Head Attention achieves high accuracy and low latency for robust cyberattack detection in Industrial IoT environments.

The paper introduces SAMD, an automated tool that uses STPA-Sec to identify potential false data injection attack scenarios in AI/ML-enabled medical devices during the design phase.

A-Live is a passive liveness detection framework that uses subtle neuromuscular micro-motion signatures captured by commodity IMU sensors to distinguish human users from non-human agents with high acc…

This paper proposes a unified framework for inference-time augmentation to improve the robustness of physiological signal classification in real-world deployments.

This paper proposes a unified framework for inference-time augmentation to improve the robustness of physiological signal classification in real-world deployments.

The paper introduces Dual-Spectral Flow Matching (DSFM), a novel generative framework that uses wavelet and cosine transforms to synthesize highly realistic, non-stationary fMRI time series for improv…

AEGIS introduces a novel physics-based system that analyzes encrypted network traffic flow dynamics, achieving state-of-the-art zero-day evasion detection with high accuracy and low latency.

The paper proposes Family-Grouped Hierarchical Federated Learning (Family-FL) combined with a highly optimized Tiny CNN-LSTM model to enable privacy-preserving ECG monitoring on ultra-resource-constra…

The paper introduces 'adversarial restlessness,' an activation-level signature in LLM residual streams, to detect multi-turn prompt injection attacks with high accuracy.

This paper models PIN entry as a stochastic communication channel, proposing a probabilistic inference framework to quantify reliability loss and QoS degradation caused by partial information leakage.

A-THENA is a lightweight, Transformer-based early intrusion detection system that significantly improves IoT security by incorporating time-aware hybrid encoding and network-specific augmentation, ach…

The paper proposes a medoid prototype alignment framework to enable robust unknown attack detection when transferring intrusion detectors between different industrial plants, achieving high performanc…

VitalAgent is a novel tool-augmented agentic framework that significantly improves physiological monitoring from wearable health data by enabling both reactive question answering and proactive, long-t…

The paper systematically evaluates 27 Spiking Neural Network (SNN) configurations to determine the optimal combination of neuron model and spike encoding scheme for network intrusion detection, findin…