This blog post share insights from the stimulating discussions in our CSCE 689 ML-Based Cyberdefenses course led by the brilliant Dr. Marcus Botacin at Texas A&M University. Picture this — we’re…

SystemWeakness's platform is  dedicated to cybersecurity education and awareness, offering insights into common security vulnerabilities, attack techniques, and risk mitigation strategies. Through articles, tutorials, and security advisories, SystemWeakness offers insights into securing software applications, networks, and systems from cyber threats. Readers can learn about secure coding practices, penetration testing methodologies, and incident response procedures to strengthen their cybersecurity posture and protect against cyber attacks.

System Weakness

This post explores using sequence modeling, specifically LSTM and BERT, to detect Android malware in highly imbalanced datasets. The application of BERT achieves excellent results with an F1 score of 0.919 on imbalanced malware detection.

Using Sequence Modeling to Detect Android Malware in Highly Imbalanced Datasets

The Challenge of Imbalanced Malware Datasets

Sequence Modeling Retains Ordering Information

Applying BERT Language Model to Security Sequences