Mohammed Tawfik

Hybrid rank fusion of a group-token transformer with gradient boosting improves detection of evasive and novel-family malware

Static machine-learning malware detectors report near-perfect closed-set accuracy, but the malware that matters operationally is the malware that slips past detection and the malware that belongs to families never seen in training. We study both on three longitudinal benchmarks:...

HIFN-Mamba: hybrid information flow networks with selective state-space sequence modeling for intrusion detection in the Internet of Medical Things

The exponential growth of the Internet of Medical Things (IoMT)—a multi-billion-device infrastructure of clinically critical sensors, monitors, and connected medical equipment—has dramatically expanded the attack surface for ransomware, lateral-movement, and protocol-specific exp...

SemioKAT-FL: explainable, background-invariant, and privacy-preserving video seizure detection with a Kolmogorov–Arnold Transformer and federated learning

Abstract Automatic detection of epileptic seizures from video promises non-invasive, continuous patient monitoring, yet web-sourced seizure corpora are dominated by background confounds : recording-specific cues (studio, home, clinic) that are spuriously correlated with the seizu...

Multistage advanced persistent threat detection and malware attribution using Mamba state space models with Kolmogorov Arnold networks

Advanced Persistent Threats (APTs) represent the most sophisticated class of cyberattacks, characterized by multi-stage, stealthy, and persistent intrusion campaigns orchestrated by well-funded, often state-sponsored adversaries. Existing detection methods suffer from three criti...