AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work presents ShortcutCatcher, an automated, model-agnostic framework that detects and mitigates shortcuts with the help of explainable AI, and exposes dataset limitations where collection artefacts act as silent shortcuts that have gone so far unnoticed.
This work distil one 101k-parameter student from two teachers of equal accuracy but different construction, a five-member ensemble and a single wider model, so that following one rather than the other is attributable to it.
Encrypted traffic analysis (ETA) plays a critical role in detecting malicious activities. Although machine learning-based ETA has shown significant promise, existing methods heavily depend on large-scale, manually labeled data. Meanwhile, the research community has released numerous publicly available encrypted traffic...
This paper presents a systematic framework for membership inference attacks, in which an adversary with only black-box query access to a deployed classifier determines whether a specific individual's record was part of its training set.
The next big jump will not be driven by larger parameters, but smaller, more visible and dynamic systems that can be trusted by security analysts, as well as a disconnect between lab and reality.
A UEBA-based machine learning framework that constructs per-user behavioral profiles from enterprise proxy and access log data, scoring sessions against a 30-feature behavioral representation spanning temporal patterns, data-transfer anomalies, domain interactions, HTTP characteristics, and session-device signals is pr...
It is argued that explanation privacy should be evaluated as an end-to-end disclosure problem, with defenses matched to the acquisition path and protected asset, with defenses matched to the acquisition path and protected asset.
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