Abstract:
A technique provides alert prioritization. The technique involves selecting attributes to use as alert scoring factors. The technique further involves updating, for an incoming alert having particular attribute values for the selected attributes, count data to represent encounter of the incoming alert from perspectives of the selected attributes. The technique further involves generating an overall significance score for the incoming alert based on the updated count data. The overall significance score is a measure of alert significance relative to other alerts. Scored alerts then can be sorted so that investigators focus on the alerts with the highest significance scores. Such a technique is well suited for adaptive authentication (AA) and Security Information and Event Management (SIEM) systems among other alert-based systems such as churn analysis systems, malfunction detection systems, and the like.
Abstract:
A processing device comprises a processor coupled to a memory and is configured to determine a first set of features from domain name system (DNS) information, the first set of features being defined over a domain, and to determine a second set of features from the DNS information, the second set of features being defined over internet protocol (IP) addresses returned for the domain. The processing device is further configured to compute a fast-flux score based on the first and second sets of features, and to utilize the fast-flux score to characterize fast-flux activity relating to the domain. For example, the processing device can be configured to compare the fast-flux score to a threshold, and to generate an indicator of the presence or absence of fast-flux activity based on a result of the comparison. The processing device may be implemented in a computer network or network security system.
Abstract:
An improved technique involves processing network traffic data to automatically establish whether a device on the network satisfies a particular set of constraints. Along these lines, a SIEM server observes and processes incoming and outgoing traffic data corresponding to a particular device at an address of the network. The SIEM server then analyzes this traffic data in order to determine whether the data satisfies a set of constraints satisfied by a client, or another set of constraints satisfied by a server. The SIEM server then applies the label of “client” or “server” to the device according to which set of constraints the SIEM server determines the data to have satisfied.
Abstract:
An authentication method and system to combat confirmation bias provides for an authentication system that upon matching an access request to a record for a given user in an authentication system further interrogates a set of secondary sources to determine that the individual requesting access is in fact the correct user.
Abstract:
There is disclosed some techniques for processing an authentication request. In one example, a method comprises the step of determining the velocity between authentication requests of a user associated with the requests. Additionally, the method determines the likelihood that a location associated with one of the requests is associated with the user location. Furthermore, the method generates an authentication result based on the likelihood that a location associated with one of the requests is associated with the user location.
Abstract:
An information processing system implements a security system. The security system comprises a classifier configured to process information characterizing events in order to generate respective risk scores, and a data store coupled to the classifier and configured to store feedback relating to one or more attributes associated with an assessment of the risk scores by one or more users. The classifier is configured to utilize the feedback regarding the risk scores to learn riskiness of particular events and to adjust its operation based on the learned riskiness, such that the risk score generated by the classifier for a given one of the events is based at least in part on the feedback received regarding risk scores generated for one or more previous ones of the events.
Abstract:
There is disclosed a technique for detecting risky domains. The technique comprises collecting information in connection with a domain. The technique also comprises generating a profile comprising at least one metric associated with the domain based on the collected information. The technique further comprises determining the riskiness in connection with the domain based on the generated profile.
Abstract:
Authentication systems are provided that select an authentication method to be applied to a given transaction from among a plurality of available authentication methods based on risk reasoning. An authentication request from an authentication requestor for a given transaction is processed by receiving the authentication request from the authentication requester and selecting an authentication method to be applied to the given transaction from among a plurality of available authentication methods based on an evaluation of one or more predefined risk reasons with respect to the available authentication methods. The predefined risk reasons associated with a given transaction comprise, for example, a set of risk reasons that contribute to a risk score that has been assigned to the given transaction. The evaluation may employ one or more of rule-based, heuristic and Bayesian techniques.
Abstract:
A technique detects riskiness of a communication in a network based on behavior profiling. The technique involves generating a network history baseline (e.g., normal and abnormal behavior profiles) from prior network communications occurring in the network. The technique further involves, for a new network communication, assigning the new network communication a risk score based on a comparison of the new network communication to the network history baseline. The risk score is a numerical measure of behavioral normalcy relative to the prior network communications occurring in the network. The technique further involves providing an output signal having a first value when the risk score is above a predefined risk threshold to indicate that the communication is risky, and a second value which is different than the first value when the risk score is below the predefined risk threshold to indicate that the communication is not risky.
Abstract:
Data driven device detection is provided, whereby a device is detected by obtaining a plurality of feature values for a given device; obtaining a set of device attributes for a plurality of potential devices; calculating a probability value that the given device is each potential device within the plurality of potential devices; identifying a candidate device associated with a maximum probability value among the calculated probability values; and labeling the given device as the candidate device if the associated maximum probability value satisfies a predefined threshold. The predefined threshold can be a function, for example, of whether the given user has previously used this device. The obtained feature values can be obtained for a selected set of features satisfying one or more predefined characteristic criteria. The device attributes can be obtained, for example, from a profile for each of the plurality of potential devices.