ZeroAnomaly

AI-Powered Anomaly Detection

Advanced behavioral anomaly detection using transformer models trained on device data. Built on research from Drexel University and Harvard University, our technology provides unprecedented accuracy in identifying and neutralizing modern cyber threats.

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Network Traffic Analysis

Advanced behavioral analysis of network traffic patterns to detect malicious activities and anomalous communications in real-time environments.

System Call Analysis

Advanced behavioral analysis using system call sequences to detect malicious patterns and anomalous activities in real-time.

Zero-Day Protection

Machine learning models that can identify previously unseen malware variants through behavioral pattern recognition.

Recent Research & Publications

sysBERT: Improved Behavioral Malware Detection using BERT Trained on sys2vec Embeddings

Conference: Hawaii International Conference on System Sciences (HICSS'25)

Authors: J. Carter, S. Mancoridis, P. Protopapas

Novel approach using BERT architecture trained on system call embeddings for behavioral malware detection, achieving superior accuracy compared to traditional sequence-based methods.

Behavioral Malware Detection using Language Model Classifier Trained on sys2vec Embeddings

Conference: Hawaii International Conference on System Sciences (HICSS'24)

Authors: J. Carter, S. Mancoridis, P. Protopapas, E. Galinkin

Advanced language model classifier approach for behavioral malware detection using system call embeddings and transformer architectures.

IoT Malware Data Augmentation using Generative Adversarial Network

Conference: Hawaii International Conference on System Sciences (HICSS'24)

Authors: J. Carter, S. Mancoridis, P. Protopapas, E. Galinkin

Innovative use of GANs for data augmentation in IoT malware detection, addressing the challenge of limited training data in cybersecurity applications.

Meet Our Team

John Carter

John Carter

Co-Founder

PhD student at Drexel University studying under Dr. Spiros Mancoridis and Dr. Pavlos Protopapas at Harvard University.

Education

M.Sc. in Computer Science from Drexel University

Research

Behavioral malware detection, system call analysis, machine learning, IoT security, and transformer architectures for cybersecurity.

Recent Work

Co-author of multiple HICSS papers on behavioral malware detection, including sysBERT (2025), language model classifiers (2024), and IoT malware data augmentation using GANs (2024).

Dr. Spiros Mancoridis

Spiros Mancoridis, Ph.D.

Co-Founder

Auerbach Berger Endowed Chair in Cybersecurity and Distinguished Professor of Computer Science at Drexel University's College of Computing & Informatics.

Education

Ph.D. in Computer Science from the University of Toronto

Research

Software security, reverse engineering, autonomic computing, software design and architecture, genetic algorithms, and behavioral malware detection. Author of over 100 refereed technical publications.

Awards

National Science Foundation's CAREER Award (1998), Outstanding Researcher Award from Drexel's College of Engineering (2008)

Recent Work

Co-author of multiple HICSS papers on behavioral malware detection, including sysBERT (2025), language model classifiers (2024), and IoT malware data augmentation using GANs (2024).

Pavlos Protopapas

Pavlos Protopapas, Ph.D.

Co-Founder

Scientific Program Director at the Institute for Applied Computational Science at Harvard University.

Education

Ph.D. in Physics from the University of Pennsylvania

Research

Physics, applied machine learning, and cybersecurity applications of AI

Recent Work

Co-author of multiple HICSS papers on behavioral malware detection, including sysBERT (2025), language model classifiers (2024), and IoT malware data augmentation using GANs (2024).

Get in Touch

Let's discuss your security needs

Interested in our AI-powered threat detection technology? Whether you're looking to integrate our solution, explore partnerships, or learn more about our research, we'd love to hear from you.

Email

john@zeroanomaly.com

Location

Philadelphia, PA

Response Time

Within 24 hours