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Jul 30, 2020 · In this paper, we propose Metis, a framework that provides interpretability for two general categories of networking problems spanning local and global control.
In this paper, we present XNIDS, a novel framework that facilitates active intrusion responses by explaining DL-NIDS.
The proposed Metis framework provides interpretability for two general categories of networking problems spanning local and global control, ...
While many deep learning (DL)-based networking systems have demonstrated superior performance, the underlying Deep Neural. Networks (DNNs) remain blackboxes and ...
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Oct 9, 2019 · In this paper, we propose Metis, a framework that provides interpretability for two general categories of networking problems spanning local and global control.
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In this paper, we propose TranSys, a novel framework to explain DL-based networked systems for practical deployment. Transys categorizes current DL-based ...
LEMNA [27], which is specifically designed for deep learning-based security applications, can explain binary code analysis tools [63] and malware detection.
Deep learning, explained: Fundamentals, explainability, and ...
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This paper explains the 'whats' and 'whys' of DL from first principles and how they are different from those of process-based modelling.
Overall, this document will serve as an analysis of the combination between machine learning principles and computer network analysis in their ability to detect ...
Deep learning is a type of machine learning (ML) and artificial intelligence (AI) that trains computers to learn from extensive data sets.