Symantec Corporation

Kent E Griffin

Carey S Nachenberg

Scott D Schneider

Tzi-cker Chiueh

Xin Hu

Pratyusa Kumar Manadhata

Sandeep B Bhatkar

Susanta Kumar Nanda

Sourabh Satish

Zulfikar Ramzan

Matthew Conover

Sanjay Sawhney

Shane Pereira

Abubakar A Wawda

Jim Casaburi

Vijay Anand Seshadri

Darren Michael Sanders

Jing Zhou

Techniques for classifying non-process threats are disclosed. In one particular exemplary embodiment, the techniques may be realized as a method for classifying non-process threats comprising generating trace data of at least one observable event associated with execution of a process, representing a first feature of the at least one observable event of the trace data, calculating, using a computer processor, a similarity between the first feature and at least one sample feature, and classifying the process based on the similarity.

Techniques for classifying non-process threats

A reputation server is coupled to multiple clients via a network. Each client has a security module that detect malware at the client. The security module computes a hygiene score based on detected malware and provides it to the reputation server. The security module monitors client encounters with entities such as files, programs, and websites. When a client encounters an entity, the security module obtains a reputation score for the entity from the reputation server. The security module evaluates the reputation score and optionally cancels an activity involving the entity. The reputation server computes reputation scores for the entities based on the clients' hygiene scores and operations performed in response to the evaluations. The reputation server prioritizes malware submissions from the client security modules based on the reputation scores.

Hygiene based computer security

A computer-implemented method to deploy a pre-boot environment in a computing system is described. A protected area may be created at a first location on a data storage device of the computing system. An operating system may be installed in the protected area at the first location on the data storage device. The pre-boot environment is modified to enable an address offset mode. The pre-boot environment is installed in an unprotected area at a second location on the data storage device.

Systems and methods for deploying a pre-boot environment to enable an address offset mode after execution of system bios for booting a operating system in a protected area

A computer-implemented method for facilitating automatic malware signature generation may comprise disassembling a malware program, identifying one or more byte sequences within the disassembled malware program that have a likelihood of being representative of one or more library functions contained within the malware program, and preventing the one or more byte sequences from being included within one or more malware signatures. Corresponding systems and computer-readable storage media are also disclosed.

Systems and methods for library function identification in automatic malware signature generation

Machine language instruction sequences of computer files are extracted and encoded into standardized opcode sequences. The standardized opcodes in the sequences are of the same length and do not include operands. A multi-dimension vector is generated as a static feature for each computer file, where each element in the vector corresponds to the number of occurrences of a unique N-gram (i.e., unique sequence of N consecutive standardized opcodes) in the standardized opcode sequence for that computer file. The computer files are clustered into clusters of similarly classified files based on similarities of their static features. An unknown computer file can be classified by first grouping the file into a cluster of files with similar static features (e.g., into the cluster with the shortest average distance), and then determining the classification of that file based on the classifications of other files that belong to the same cluster.

Encoding machine code instructions for static feature based malware clustering

Installer package information is presented to a user in response to an attempted installation of an application on an endpoint. The attempted installation is detected and the installer package is identified to an information server. The installer package may be identified using a hash key or other unique identifier. In response, the information server provides to the endpoint information associated with the identified installer package based on information received from a plurality of other endpoints. The endpoint may also provide installation and application information related to the installer package to the information server. In one embodiment, when the information server obtains more than the threshold amount of information for an installer package, the information server may analyze the information and provide the analysis to requesting endpoints. The analysis may include the risk or performance impact of the installer package, or the category or functionality of the application.

Providing installer package information to a user

A computer-implemented, server-side method for classifying unknown files based on user actions may include (1) identifying at least one file whose trustworthiness is unknown, (2) identifying a report received from at least one client device that identifies at least one action taken by a user within a user community when informed by security software on the client device that the trustworthiness of the file is unknown, (3) determining that the action taken by the user indicates that the user believes the file is trustworthy, (4) classifying the file as trustworthy based at least in part on the action taken by the user, and then (5) providing the file's classification to at least one computing device in order to enable the computing device to evaluate the trustworthiness of the file. Corresponding systems, encoded computer-readable media, and client-side methods are also disclosed.

Systems and methods for classifying unknown files/spam based on a user actions, a file's prevalence within a user community, and a predetermined prevalence threshold

A method may include detecting initiation of a power-management mode that suspends the functionality of at least one component of a computing device while maintaining the functionality of the device's memory. The method may also include, before the device enters the power-management mode, (1) identifying, within the device's memory, an encryption key that is required to access encrypted data stored in the device's storage device, and (2) removing the encryption key from the device's memory in order to protect against unauthorized access of the encrypted data during implementation of the power-management mode. The method may also include, upon detecting discontinuation of the power-management mode, (1) obtaining user credentials from a user of the device in order to authenticate the user and, upon successfully authenticating the user, (2) using the user credentials to regenerate the encryption key in order to enable access to the encrypted data stored in the storage device.

Systems and methods for protecting against unauthorized access of encrypted data during power-management modes

A computer-implemented method for data loss prevention on mobile computing systems may include (1) identifying a mobile computing system configured to execute only one application at a time as a foreground application, (2) determining that the mobile computing system has begun executing a sensitive application as the foreground application, (3) identifying a first enumeration of screenshots stored on the mobile computing system when the mobile computing system began executing the sensitive application as the foreground application, (4) identifying a second enumeration of screenshots stored on the mobile computing system, (5) determining that at least one new screenshot was taken on the mobile computing system while the sensitive application was the foreground application by detecting a difference between the first enumeration and the second enumeration, and (6) performing a security action upon detecting the difference. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for data loss prevention on mobile computing systems

A candidate signature for a known malware entity is selected for analysis. A set of malware entities that contain the candidate signature is identified. A diversity measurement for the candidate signature is determined. The diversity measurement describes the diversity of the set of malware entities that contain the candidate signature. A determination is made whether to use the candidate signature to identify the known malware entity based at least in part on the diversity measurement. Responsive to the determination, the candidate malware signature is stored as a signature for the known malware entity.

Selecting malware signatures based on malware diversity

Hygiene-based computer security

A signature is identified in association with an entity at a client. A reputation score associated with the entity is identified, the reputation score indicating a likelihood that the entity will compromise the client. Whether the signature detection event is a false positive signature detection event is evaluated based on the reputation score and reported.

Reputation based identification of false positive malware detections

A security module detects attempted exploitations of vulnerabilities of applications executing on a computer. The security module hooks an application on the computer. The hook transfers control flow to the security module if execution reaches a hooked location. When a hook is followed, the security module saves the state of the computer and activates an analysis environment. A virtual machine within the analysis environment executes signatures that programmatically analyze the state of the computer to determine whether a vulnerability in the application is being exploited. If a signature detects an exploit, the security module blocks the exploit by skipping over the one or more instructions that constitute the exploit, terminating the application, or performing a different action. The security module reports the detected exploit attempt to the user of the client. The security module returns control flow back to the application if it does not detect an exploit.

Control flow redirection and analysis for detecting vulnerability exploitation

A computer-implemented method for facilitating automatic malware signature generation may comprise providing a byte sequence marked for possible inclusion within one or more malware signatures, determining a context diversity of the byte sequence within malware files each containing the byte sequence in accordance with a diversity-based heuristic, and preventing the byte sequence from being included within the one or more malware signatures in accordance with the determined context diversity. Corresponding systems and computer-readable storage media are also disclosed.

Systems and methods for byte-level context diversity-based automatic malware signature generation

A server provides a reduced set of malware signatures to clients. The reduced set of malware signatures has the same scope of coverage as a comprehensive set of malware signatures stored on the server, but with a higher rate of false positive detections. The server receives signature detection event reports from the clients. A signature detection event report identifies the signature in the reduced set that was detected, and includes information describing the suspicious entity in which the signature was detected. Upon receiving a signature detection event report from a client, the server evaluates the information describing the suspicious entity using one or more signatures in the comprehensive set to determine whether the signature detection event is a false positive or a legitimate malware detection. The security server provides the result of the evaluation to the client from which the report was received.

Growing community of inventors

Santa Monica, CA, United States of America

Kent E Griffin