Advanced AI-Powered Malware Detection for Modern Threats. Protect your systems with cutting-edge technology that learns and adapts to new threats in real-time.
Get StartedInstant analysis of system behavior and network traffic using advanced AI models to identify potential threats before they can cause damage.
Deep learning algorithms that understand normal system patterns to detect anomalous activities with unprecedented accuracy.
Continuous learning from global threat data to stay ahead of emerging malware variants and protect against zero-day attacks.
PhD student at Drexel University studying under Dr. Spiros Mancoridis and Dr. Pavlos Protopapas at Harvard University.
M.Sc. in Computer Science from Drexel University
Router security, anomaly detection, machine learning, IoT security, and malware detection.
Co-author of "sysBERT: Improved Behavioral Malware Detection using BERT Trained on sys2vec Embeddings" (HICSS 2025)
Auerbach Berger Endowed Chair in Cybersecurity and Distinguished Professor of Computer Science at Drexel University's College of Computing & Informatics.
Ph.D. in Computer Science from the University of Toronto
Software security, reverse engineering, autonomic computing, software design and architecture, and genetic algorithms. Author of over 100 refereed technical publications.
National Science Foundation's CAREER Award (1998), Outstanding Researcher Award from Drexel's College of Engineering (2008)
Co-author of "sysBERT: Improved Behavioral Malware Detection using BERT Trained on sys2vec Embeddings" (HICSS 2025)
Scientific Program Director at the Institute for Applied Computational Science at Harvard University.
Ph.D. in Physics from the University of Pennsylvania
Physics, applied machine learning
Co-author of "sysBERT: Improved Behavioral Malware Detection using BERT Trained on sys2vec Embeddings" (HICSS 2025)
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