Abstract: A method of scoring alerts generated by a plurality of endpoints includes the steps of: in response to a new alert generated by a first endpoint of the plurality of endpoints, generating an anomaly score of the new alert; identifying a rule that triggered the new alert and determining a threat score associated with the rule; and generating a security risk score for the new alert based on the anomaly score and the threat score and transmitting the security risk score to a security analytics platform of the endpoints.

Abstract: Systems and methods are described for employing event context to improve threat detection. Systems and methods of embodiments of the disclosure measure both process deviation and path deviation to determine whether processes are benign or represent threats. Both a process deviation model and a path deviation model are deployed. The process deviation model determines the similarity of a process to past processes, and the path deviation model estimates whether processes have been called out of turn. In this manner, systems and methods of embodiments of the disclosure are able to detect both whether a process is in itself unusual, and whether it is called at an unusual time. This added context contributes to improved threat detection.

Abstract: A computer network includes a camera node, a network access node, a verification node, and a display node. Video content recorded by a camera at the camera node is transmitted to the display node and to the verification node for verification. The video content is verified at the display node and at the verification node. Recording metadata of the video content is stored in a distributed ledger and retrieved by the display node to verify the video content. The verification node receives, from the network access node, verification data for verifying the video content.

Abstract: A system for stateful detection of cyberattacks includes an endpoint computer and a backend computer system. The endpoint computer monitors for cyberattacks and sends out queries for detected security events. The backend computer system stores observation data that are included in the queries. The backend computer system combines current observation data from a current query, relevant observation data from previous queries, and relevant cybersecurity data. The combined data are evaluated for cyberattacks.

Abstract: A method for designating a given image as similar/dissimilar with respect to a reference image is provided. The method includes normalizing the image. Normalizing includes performing pre-processing and a lossy compression on the given image to obtain a lossy representation. The pre-processing includes at least one of cropping, fundamental extracting, gray scale converting and lower color bit converting. The method also includes comparing the lossy representation of the given image with a reference representation, which is a version of a reference spam image after the reference spam image has undergone a similar normalizing process as normalizing. The method further includes, if the lossy representation of the given image matches the reference representation, designating the given image similar to the reference image. The method yet also includes, if the lossy representation of the given image does not match the reference representation, designating the given image dissimilar to the reference image.

Abstract: A system for evaluating files for cyber threats includes a machine learning model and a locality sensitive hash (LSH) repository. When the machine learning model classifies a target file as normal, the system searches the LSH repository for a malicious locality sensitive hash that is similar to a target locality sensitive hash of the target file. When the machine learning model classifies the target file as malicious, the system checks if response actions are enabled for the target file. The system reevaluates files that have been declared as normal, and updates the LSH repository in the event of false negatives. The system disables response actions for files that have been reported as false positives.

Abstract: A computer network includes a camera node, a network access node, a verification node, and a display node. Video content recorded by a camera at the camera node is transmitted to the display node and to the verification node for verification. The video content is verified at the display node and at the verification node. Recording metadata of the video content is stored in a distributed ledger and retrieved by the display node to verify the video content. The verification node receives, from the network access node, verification data for verifying the video content.

Abstract: Abusive user accounts in a social network are identified from social network data. The social network data are processed to compare postings of the user accounts to identify a group of abusive user accounts. User accounts in the group of abusive user accounts are identified based on posted message content, images included in the messages, and/or posting times. Abusive user accounts can be canceled, suspended, or rate-limited.

Abstract: An email attempting to perpetrate a business email compromise (BEC) attack is detected based on similarity of the email to a known BEC email and on similarity of the email to a user email that would have been sent by the purported sender of the email. Metadata of the email is extracted and input to a BEC machine learning model to find the known BEC email among BEC email samples. The extracted metadata are also input to a personal user machine learning model of the purported sender to generate the user email.

Abstract: A method for designating a given image as similar/dissimilar with respect to a reference image is provided. The method includes normalizing the image. Normalizing includes performing pre-processing and a lossy compression on the given image to obtain a lossy representation. The pre-processing includes at least one of cropping, fundamental extracting, gray scale converting and lower color bit converting. The method also includes comparing the lossy representation of the given image with a reference representation, which is a version of a reference spam image after the reference spam image has undergone a similar normalizing process as normalizing. The method further includes, if the lossy representation of the given image matches the reference representation, designating the given image similar to the reference image. The method yet also includes, if the lossy representation of the given image does not match the reference representation, designating the given image dissimilar to the reference image.

Abstract: A system for evaluating a target file includes an endpoint computer that receives similarity digests of legitimate files, receives a target file, and generates a similarity digest of the target file. The endpoint computer determines whether or not the target file is legitimate based on a comparison of the similarity digest of the target file against the similarity digests of the legitimate files. The system further includes a backend computer system that receives the legitimate files, generates the similarity digests of the legitimate files, and provides the similarity digests of the legitimate files to the endpoint computer.

Abstract: Abusive user accounts in a social network are identified from social network data. The social network data are processed to compare postings of the user accounts to identify a group of abusive user accounts. User accounts in the group of abusive user accounts are identified based on posted message content, images included in the messages, and/or posting times. Abusive user accounts can be canceled, suspended, or rate-limited.

Abstract: Social messages sent or posted by users of a social networking service are collected. Compromised social networking accounts are identified from the collected social messages. Keywords indicative of compromised social networking accounts are extracted from social messages of identified compromised social networking accounts. The keywords are used as search terms in a search query for additional social messages. Additional compromised social networking accounts are identified from search results that are responsive to the search query.

Abstract: Social network usage in an enterprise environment is controlled by receiving and processing dynamic postings from a social network to identify indicators of prohibited content. The indicators of prohibited content are employed to identify and block prohibited postings from entering an enterprise network.

Abstract: Malicious shortened uniform resource locators are identified by collecting online messages in a computer. Senders and uniform resource locators are extracted from the collected online messages. Sender controlled components are identified in the uniform resource locators. Groups of sender controlled components and senders are formed. Shortened uniform resource locators associated with sender controlled components that are members of malicious groups are identified as malicious.

Abstract: Network system provides a real-time adaptive recommendation set of documents with a high statistical measure of relevancy to the requestor device. The recommendation set is optimized based on analyzing text of documents of the interest set, categorizing these documents into clusters, extracting keywords representing the themes or concepts of documents in the clusters, and filtering a population of eligible documents accessible to the system utilizing site and or Internet-wide search engines. The system is either automatically or manually invoked and it develops and presents the recommendation set in real-time. The recommendation set may be presented as a greeting, notification, alert, HTML fragment, fax, voicemail, or automatic classification or routing of customer e-mail, personal e-mail, job postings, and offers for sale or exchange.

Abstract: Network system provides a real-time adaptive recommendation set of documents with a high statistical measure of relevancy to the requestor device. The recommendation set is optimized based on analyzing text of documents of the interest set, categorizing these documents into clusters, extracting keywords representing the themes or concepts of documents in the clusters, and filtering a population of eligible documents accessible to the system utilizing site and or Internet-wide search engines. The system is either automatically or manually invoked and it develops and presents the recommendation set in real-time. The recommendation set may be presented as a greeting, notification, alert, HTML fragment, fax, voicemail, or automatic classification or routing of customer e-mail, personal e-mail, job postings, and offers for sale or exchange.

Abstract: Network system provides a real-time adaptive recommendation set of documents with a high statistical measure of relevancy to the requestor device. The recommendation set is optimized based on analyzing text of documents of the interest set, categorizing these documents into clusters, extracting keywords representing the themes or concepts of documents in the clusters, and filtering a population of eligible documents accessible to the system utilizing site and or Internet-wide search engines. The system is either automatically or manually invoked and it develops and presents the recommendation set in real-time. The recommendation set may be presented as a greeting, notification, alert, HTML fragment, fax, voicemail, or automatic classification or routing of customer e-mail, personal e-mail, job postings, and offers for sale or exchange.

Abstract: Uniform resource locators (URLs) that include strings matching known malicious budget codes are deemed to be malicious URLs. Compromised websites and compromised IP addresses are identified from the malicious URLs. URLs obtained from network traffic to compromised domain names or compromised IP addresses are inspected to identify candidate budget codes. Candidate budget codes that are confirmed to be malicious budget codes are included in a watch list, which may be distributed to endpoint computers to detect phishing attacks.

Abstract: One embodiment relates to an apparatus for in-the-cloud identification of spam and/or malware. The apparatus includes computer-readable code configured to be executed by the processor so as to receive queries, the queries including hash values embedded therein. The apparatus further includes computer-readable code configured to be executed by the processor so as to detect a group of hash codes which are similar and to identify the group as corresponding to an undesirable network outbreak. Another embodiment relates to an apparatus for in-the-cloud detection of spam and/or malware. The apparatus includes computer-readable code configured to be executed by the processor so as to receive an electronic message, calculate a locality-sensitive hash based on the message, embed the locality-sensitive hash into a query, and send the query to a central analysis system via a network interface. Other embodiments, aspects and features are also disclosed.