Tonight, we will have the second Paris Machine Learning meetup of Season 5. We'll talk about Reinforcement Learning for Malware detection, Fraud detection, video scene detection and Climate Change. The video streaming is here:
Wednesday, October 18, 2017
Tonight: Paris Machine Learning Meetup #2 Season 5: Reinforcement Learning for Malware detection, Fraud detection, video scene detection and Climate Change
Thanks to Artefact for hosting and sponsoring us. They will provide the room, food and beverage. Capacity of the room is +/- 90 seats. As usual, we are on a first-come-first-serve, then doors close
Schedule :
6:45PM doors open / 7-9:00PM talks / 9-10:00PM drinks/foods / 10:00PM end
Presentations (the blog post should have the slides before the meetup happens)
Hyrum Anderson, (Endgame) -Remote-, Reinforcement Learning for Evading Machine Learning Malware Detection
Machine learning is a popular approach to signatureless malware detection because it can generalize to never-before-seen malware families and polymorphic strains. This has resulted in its practical use for either primary detection engines or for supplementary heuristic detection by anti-malware vendors. Recent work in adversarial machine learning has shown that deep learning models are susceptible to gradient-based attacks, whereas non-differentiable models that report a score can be attacked by genetic algorithms that aim to systematically reduce the score.
We propose a more general framework based on reinforcement learning (RL) for attacking static portable executable (PE) anti-malware engines. The general framework does not require a differentiable model nor does it require the engine to produce a score. Instead, an RL agent is equipped with a set of functionality-preserving operations that it may perform on the PE file. This enables completely black-box attacks against static PE anti-malware, and produces functional (with practical caveats) evasive malware samples as a direct result.
The purpose of this project is to find a method to automatically detect opening sequences in a set of episodes of a TV Show. In fact, when users are watching comics series, skipping generics can make up to 10% of saved time. Furthermore, in very short episodes, when users are watching them one after another, generics become quickly irritating. This presentation shows how basis of anomaly detection and perceptual hashing techniques of a video sequences can lead to a fast effective system able to detect a common part in episodes.
Climate Change is the biggest challenge of our time, as the overwhelming majority of our daily activities still are based on processes that release greenhouse gases, thus causing climate change.
Olivier will present the mission of Tomorrow which is quantify, and make widely accessible, the climate impact of the daily choices we make.
He will present their first initiative: electricitymap.org
Companies seek new techniques to face innovative fraud attempts. Artificial intelligence provides solutions detecting weak signals and exploiting complex correlations between large number of features. But the resulting decisions modify the fraud behaviours and the nature of generated data. This issue imply a selection bias, which results in the degradation of the algorithms learning data. We faced this new challenge and are going to focus on by presenting the so-called techniques of “inference of the refused”
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