Unfortunately this talk is not focused on technical security aspects, but gives you a clear view on how Machine Learning could be used in Security applications. You will read a single DEFCON talk resume, but you can go deeper looking here. There are already softwares that use Machine Learning for Defensive purposes like firewalls for anomalous traffic detection, so we'll focus on the Offensive purposes in order to create of find already existing tools of this category. Den Petro and Ben Morris from Bishop Fox created a tool named "DeepHack" which uses ML to accomplish SQL injection attack. Language used to query a Database in order to add/remove/edit information collected inside of it as records.

Unfortunately this talk is not focused on technical security aspects, but gives you a clear view on how Machine Learning could be used in Security applications. You will read a single DEFCON talk resume, but you can go deeper looking here. There are already softwares that use Machine Learning for Defensive purposes like firewalls for anomalous traffic detection, so we'll focus on the Offensive purposes in order to create of find already existing tools of this category. Den Petro and Ben Morris from Bishop Fox created a tool named "DeepHack" which uses ML to accomplish SQL injection attack. Language used to query a Database in order to add/remove/edit information collected inside of it as records.

Weaponizing Machine Learning Against ISIS Will Tangle Military Chains of Command Everyone on the Internet had a great time with Tay, Microsoft's Twitter robot that became a racist Holocaust denier in a matter of a few hours (then came back and did it again). The company had created a public relations flap -- more incident than a disaster -- while giving the public an object lesson on the pros and cons of machine learning: Automation can harness patterns to fascinating effect at speed, but the results will be predictably hard to predict. As is often the case, the military is an early adopter of automation technology. It is -- at one time -- leading the charge toward machine learning and also trying desperately to keep up.