Abstract: Techniques, methods and/or apparatuses are disclosed that enable of cyber risks on assets of networks to be evaluated in presence of security controls on the assets. In this way, effect of security controls already in place may be quantified. A novel scoring technique is presented. Also, use of causal inference is in the context of security risk assessment is described.

Abstract: In an embodiment, a semantic model and a semantic model training method that obtains a textual description of one or more features associated with a first vulnerability that has been used in one or more attacks. Text is parsed from the first textual description in accordance with one or more rules. The system determines a first label for the first vulnerability that is associated with one or more of a plurality of stages of an attack chain taxonomy. The model is generated or refined to map the parsed text to the first label associated with the one or more stages of the attack chain taxonomy.

Abstract: In an embodiment, a semantic model and a semantic model training method that obtains a textual description of one or more features associated with a first vulnerability that has been used in one or more attacks. Text is parsed from the first textual description in accordance with one or more rules. The system determines a first label for the first vulnerability that is associated with one or more of a plurality of stages of an attack chain taxonomy. The model is generated or refined to map the parsed text to the first label associated with the one or more stages of the attack chain taxonomy.

Abstract: Techniques, methods and/or apparatuses are disclosed that enable of cyber risks on assets of networks to be evaluated in presence of security controls on the assets. In this way, effect of security controls already in place may be quantified. A novel scoring technique is presented. Also, use of causal inference is in the context of security risk assessment is described.

Abstract: The disclosed computer-implemented method for detecting anomalous system command line data may include (i) receiving command line data from a target computing system, (ii) building a baseline model that utilizes machine-learning to analyze the command line data, the baseline model comprising a support-vector machine (SVM), natural language processing, and a hashing function, (iii) assigning, utilizing the baseline model, a score to each of a plurality of instances of the command line data, and (iv) identifying, based on the score, anomalous commands comprising potentially malicious data when any of the instances of the command line data fails to exceed a threshold. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: In an embodiment, a semantic model and a semantic model training method that obtains a textual description of one or more features associated with a first vulnerability that has been used in one or more attacks. Text is parsed from the first textual description in accordance with one or more rules. The system determines a first label for the first vulnerability that is associated with one or more of a plurality of stages of an attack chain taxonomy. The model is generated or refined to map the parsed text to the first label associated with the one or more stages of the attack chain taxonomy.

Abstract: Each node of a metric tree comprises a similarity hash of a member of a dataset of known message threats, calculated using a given similarity hashing algorithm. The nodes are organized into the tree, positioned such that the differences between the similarity hashes are represented as distances between the nodes. Messages are received and tested to determine whether they are malicious. When a message is received, a similarity hash of the message is calculated using the same similarity hashing algorithm that is used to calculate the hashes of the members of the dataset. The tree is searched for a hash of a known message threat that is within a threshold of distance to the hash of the received message. Searching the tree can take the form of traversal from the root node, to determine whether the tree contains a node within the similarity threshold.