6 citations · 8 across the 7 of their papers we have counts for
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cs.AI2021★ 6 cited
End-To-End Anomaly Detection for Identifying Malicious Cyber Behavior through NLP-Based Log Embeddings
Andrew Golczynski, John A. Emanuello
Rule-based IDS (intrusion detection systems) are being replaced by more robust neural IDS, which demonstrate great potential in the field of Cybersecurity. However, these ML approa…
cs.CR2021
Proceedings - AI/ML for Cybersecurity: Challenges, Solutions, and Novel Ideas at SIAM Data Mining 2021
John Emanuello, Kimberly Ferguson-Walter, Erik Hemberg +5
Malicious cyber activity is ubiquitous and its harmful effects have dramatic and often irreversible impacts on society. Given the shortage of cybersecurity professionals, the ever-…
cs.CR2021★ 1 cited
Robustness of ML-Enhanced IDS to Stealthy Adversaries
Vance Wong, John Emanuello
Intrusion Detection Systems (IDS) enhanced with Machine Learning (ML) have demonstrated the capacity to efficiently build a prototype of "normal" cyber behaviors in order to detect…