Machine Learning for Malware Detection

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Machine Learning for Malware DetectionLearn more on #bringonthefutureContentsBasic approaches to Malware Detection 1Machine Learning : concepts and definitions 2Unsupervised Learning 2Supervised Learning 2Deep Learning 3Machine Learning application specifics in cybersecurity 4Large representative datasets are required 4The trained model has to be interpretable 4False positive rates must be extremely low 4Algorithms must allow us to quickly adapt them to Malware writers counteractions 5Kaspersky Lab Machine Learning application 6Detecting new Malware in pre-execution with similarity hashing 6Two-stage pre-execution Detection on users computers with similarity hash

to malware detection An efficient, robust and scalable malware recognition module is the key component of every cybersecurity product. Malware recognition modules decide if an object is a threat, based on the data they have collected on it. This data may be collected at different phases:

  Malware