Unsafe cyber dating

In this presentation we will demonstrate use-cases and examples of black-box analyses of CAN network and ECU devices.This framework based on modules and libraries that can be used all together in different combos to get exactly what researcher/tester needs.The networks trained to recognize the characteristics of malicious code by looking at ten million of examples of malware and non-malware files, could offer a far better way to catch such malicious code.We build a deep learning system for Android anti-malware.Nowadays we have the computational power and mechanisms to process huge amounts of data.Machine learning give us the algorithms to analyse network data in order to find specific types of behaviour.The malware landscape is characterised by its rapid and constant evolution.

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We illustrate the detection performance of each algorithm presenting real examples of malware detected by algorithms described in this work.We also elaborate on how the found infections would have been otherwise missed using traditional detection tools.This talk will introduce our work on AI based Antivirus using deep learning.We select high-quality app features data with only a little size, and use innovative normalization preprocessing, unique activation function and advanced multilayer artificial neural network to recognize the unknown malware variants and defense zero-day attacks.Our deep learning system has high precision (99.96%) and high recall (88%).

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