Arthur Samuel, a pioneer in artificial intelligence, described machine learning as a set of methods and technologies that “gives computers the ability to learn without being explicitly programmed.” In a particular case of supervised learning for anti-malware, the task could be formulated as follows: given a set of object features X and corresponding object labels Y as an input, create a model that will produce the correct labels Y’ for previously unseen test objects X’. X could be some features representing file content or behavior (file statistics, list of used API functions, etc.) and labels Y could be simply “malware” or “benign” (in more complex cases, we could be interested in a fine-grained classification such as Virus, Trojan-Downloader, Adware, etc.). In case of unsupervised learning, we are more interested in revealing hidden structure of data - e.g., finding groups of similar objects or highly correlated features. ...Kaspersky Lab Artikel weiterlesen!
