Emc IP Holding Company LLC

Emc Corporation

Ereli Eran

Eyal Kolman

Alon Kaufman

Alex Vaystikh

Eyal Yehowa Gruss

Yael Villa

Marcelo Blatt

Alex Zaslavsky

Liron Liptz

Shachar Israeli

An improved technique involves inputting data in postings from social media or news websites into a risk engine. A posting extraction device continually observes postings aggregated in social media and news websites, such as Twitter, Facebook, CNN, and the like. The posting extraction device parses postings that contain specified keywords such as 'credit card,' 'account number,' and the like. The posting extraction device also parses these postings for metadata such as user identifiers, times, and locations. The posting extraction device then stores the parsed information in a transaction database that is accessed by an adaptive authentication engine for risk score assignment.

Open data collection for threat intelligence posture assessment

A system that permits authentication based on a partial password, in which a risk score is assigned to an authentication request, and a minimum partial password size is generated based on the risk score. User-entered password characters are compared to one or more partial passwords having lengths equal to or greater than the minimum partial password size. If a match is found, the user is authenticated. A password similarity threshold for the request may also be generated based on the risk score, indicating a minimum level of similarity required between the user-entered password characters and the characters in a partial password, in order for there to be a match. When the user-entered password characters match a partial password, and the requesting user is authenticated, the system may stop inputting user-entered password characters, and/or transmitting the user-entered password characters to a server computer.

Partial password-based authentication using per-request risk scores

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.

Data-driven alert prioritization

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.

Information processing systems with security-related feedback

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.

User authentication

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.

Detecting risky domains

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.

Fast-flux detection utilizing domain name system information

A processing device comprises a processor coupled to a memory and is configured to obtain at least one rule set utilized to detect malicious activity in a computer network, to determine one or more trigger conditions for each of a plurality of rules of the at least one rule set, to identify alerts generated responsive to the determined trigger conditions, to compute correlations between respective pairs of the plurality of rules based on the identified alerts, and to aggregate groups of two or more of the plurality of rules into respective aggregated rules based at least in part on the computed correlations. The aggregated rules are illustratively applied in conjunction with remaining unaggregated ones of the plurality of rules of the one or more rule sets to detect malicious activity in the computer network. The processing device may be implemented in a computer network or network security system.

Unsupervised aggregation of security rules

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.

Detecting risky network communications based on evaluation using normal and abnormal behavior profiles

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.

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Tel Aviv, Israel

Ereli Eran