PRE-COGNITIVE SECURITY INFORMATION AND EVENT MANAGEMENT

According to an example, pre-cognitive SIEM may include using trained classifiers to detect an anomaly in input events, and generating a predictive attack graph based on the detected anomaly in the input events. The predictive attack graph may provide an indication of different paths that can be taken from an asset that is related to the detected anomaly to compromise other selected assets in a network of the asset, and the other selected assets may be selected based on a ranking criterion and a complexity criterion. A rank list and a complexity list may be generated. The rank list, the complexity list, a depth of the predictive attack graph, and a weighted value may be used to generate a score that provides an indication of a number of assets that can be compromised and a difficulty of exploiting vulnerabilities related to services of the assets that can be compromised.