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Found 3 papers, 0 papers with code
Securing from Unseen: Connected Pattern Kernels (CoPaK) for Zero-Day Intrusion Detection
The digitization and public cloud adoption has led to a massive data surge, necessitating the development of efficient intrusion detection systems.
Attention to Patterns is all you need for Insider threat detection
This research proposes a novel approach that leverages the power of deep learning models to capture complex, hierarchical patterns in user behavior, enabling the early detection of malicious insider activity.
Ranked #1 on
Classification
on Insider Threat Test Dataset
No means ‘No’; a non-im-proper modeling approach, with embedded speculative context
Through this paper, we investigate various bio model’s embeddings(BioBERT, BioE- LECTRA, PubMedBERT) on their understanding of "negation and speculation context" wherein we found that these models were unable to differentiate "negated context" vs "non-negated context".
