Description
Machine learning based detection models can strengthen detection, but there remain some significant barriers to the widespread deployment of such techniques in operational detection systems. In this presentation, we identify the main challenges to overcome and we provide both methodological guidance and practical solutions to address them. The solutions we present are completely generic to be beneficial to any detection problem on any data type and are freely available in SecuML.The content of the presentation is mostly based on my PhD thesis “Expert-in-the-Loop Supervised Learning for Computer Security Detection Systems”.
Practical infos
Next sessions
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A non-comparison oblivious sort and its application to private k-NN
Speaker : Sofiane Azogagh - UQÀM
Sorting is a fundamental subroutine of many algorithms and as such has been studied for decades. A well-known result is the Lower Bound Theorem, which states that no comparison-based sorting algorithm can do better than O(nlog(n)) in the worst case. However, in the fifties, new sorting algorithms that do not rely on comparisons were introduced such as counting sort, which can run in linear time[…]-
Cryptography
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SoSysec
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Privacy
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Databases
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Secure storage
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