Abstract: Systems and methods are provided for malware scanning and detection in a computing system. In one exemplary embodiment, the method includes launching, in a computing device of the computing system, a virtual machine, and launching, in the virtual machine of the computing device, an internet browser. The method also includes requesting, by the internet browser, data from a web page, and performing, using one or more analysis tools, analysis on the web page. In the method, performing analysis on the web page includes performing monitoring and recording of system application programming interface (API) calls, and creating software objects associated with the web page. The method also includes performing antivirus scanning of the software objects, de-obfuscating JavaScript associated with the software objects, and correlating data associated with the performed analysis to determine if the web page is a malicious web page.

Abstract: Systems and methods are disclosed for identifying associations between binary samples, such as e-mail files and their attachments or a document and an executable program associated with the document. In one implementation, the method includes receiving a plurality of binary samples, and extracting metadata from the plurality of binary samples. The metadata for a binary sample from the plurality of binary samples includes a set of attributes of the binary sample. The method further includes identifying a set of associations between the plurality of binary samples based on the extracted metadata. Each association is characterized by at least one attribute the associated binary samples have in common, and each association has a confidence level indicative of a strength of the association. The method also includes identifying associations with a confidence level that exceeds a predefined threshold.

Abstract: Systems and methods are provided for malware scanning and detection in a computing system. In one exemplary embodiment, the method includes launching, in a computing device of the computing system, a virtual machine, and launching, in the virtual machine of the computing device, an internet browser. The method also includes requesting, by the internet browser, data from a web page, and performing, using one or more analysis tools, analysis on the web page. In the method, performing analysis on the web page includes performing monitoring and recording of system application programming interface (API) calls, and creating software objects associated with the web page. The method also includes performing antivirus scanning of the software objects, de-obfuscating JavaScript associated with the software objects, and correlating data associated with the performed analysis to determine if the web page is a malicious web page.

Abstract: Systems and methods are disclosed for identifying associations between binary samples, such as e-mail files and their attachments or a document and an executable program associated with the document. In one implementation, the method includes receiving a plurality of binary samples, and extracting metadata from the plurality of binary samples. The metadata for a binary sample from the plurality of binary samples includes a set of attributes of the binary sample. The method further includes identifying a set of associations between the plurality of binary samples based on the extracted metadata. Each association is characterized by at least one attribute the associated binary samples have in common, and each association has a confidence level indicative of a strength of the association. The method also includes identifying associations with a confidence level that exceeds a predefined threshold.

Abstract: Systems and methods are provided for malware scanning and detection in a computing system. In one exemplary embodiment, the method includes launching, in a computing device of the computing system, a virtual machine, and launching, in the virtual machine of the computing device, an internet browser. The method also includes requesting, by the internet browser, data from a web page, and performing, using one or more analysis tools, analysis on the web page. In the method, performing analysis on the web page includes performing monitoring and recording of system application programming interface (API) calls, and creating software objects associated with the web page. The method also includes performing antivirus scanning of the software objects, de-obfuscating JavaScript associated with the software objects, and correlating data associated with the performed analysis to determine if the web page is a malicious web page.

Abstract: Systems and methods are disclosed for identifying associations between binary samples, such as e-mail files and their attachments or a document and an executable program associated with the document. In one implementation, the method includes receiving a plurality of binary samples, and extracting metadata from the plurality of binary samples. The metadata for a binary sample from the plurality of binary samples includes a set of attributes of the binary sample. The method further includes identifying a set of associations between the plurality of binary samples based on the extracted metadata. Each association is characterized by at least one attribute the associated binary samples have in common, and each association has a confidence level indicative of a strength of the association. The method also includes identifying associations with a confidence level that exceeds a predefined threshold.

Abstract: Systems and methods are disclosed for identifying associations between binary samples, such as e-mail files and their attachments or a document and an executable program associated with the document. In one implementation, the method includes receiving a plurality of binary samples, and extracting metadata from the plurality of binary samples. The metadata for a binary sample from the plurality of binary samples includes a set of attributes of the binary sample. The method further includes identifying a set of associations between the plurality of binary samples based on the extracted metadata. Each association is characterized by at least one attribute the associated binary samples have in common, and each association has a confidence level indicative of a strength of the association. The method also includes identifying associations with a confidence level that exceeds a predefined threshold.

Abstract: Systems and methods are provided for malware scanning and detection in a computing system. In one exemplary embodiment, the method includes launching, in a computing device of the computing system, a virtual machine, and launching, in the virtual machine of the computing device, an internet browser. The method also includes requesting, by the internet browser, data from a web page, and performing, using one or more analysis tools, analysis on the web page. In the method, performing analysis on the web page includes performing monitoring and recording of system application programming interface (API) calls, and creating software objects associated with the web page. The method also includes performing antivirus scanning of the software objects, de-obfuscating JavaScript associated with the software objects, and correlating data associated with the performed analysis to determine if the web page is a malicious web page.

Abstract: Systems and methods are disclosed for identifying associations between binary samples, such as e-mail files and their attachments or a document and an executable program associated with the document. In one implementation, the method includes receiving a plurality of binary samples, and extracting metadata from the plurality of binary samples. The metadata for a binary sample from the plurality of binary samples includes a set of attributes of the binary sample. The method further includes identifying a set of associations between the plurality of binary samples based on the extracted metadata. Each association is characterized by at least one attribute the associated binary samples have in common, and each association has a confidence level indicative of a strength of the association. The method also includes identifying associations with a confidence level that exceeds a predefined threshold.

Abstract: Systems and methods are provided for malware scanning and detection in a computing system. In one exemplary embodiment, the method includes launching, in a computing device of the computing system, a virtual machine, and launching, in the virtual machine of the computing device, an internet browser. The method also includes requesting, by the internet browser, data from a web page, and performing, using one or more analysis tools, analysis on the web page. In the method, performing analysis on the web page includes performing monitoring and recording of system application programming interface (API) calls, and creating software objects associated with the web page. The method also includes performing antivirus scanning of the software objects, de-obfuscating JavaScript associated with the software objects, and correlating data associated with the performed analysis to determine if the web page is a malicious web page.

Abstract: Systems and methods are disclosed for identifying associations between binary samples, such as e-mail files and their attachments or a document and an executable program associated with the document. In one implementation, the method includes receiving a plurality of binary samples, and extracting metadata from the plurality of binary samples. The metadata for a binary sample from the plurality of binary samples includes a set of attributes of the binary sample. The method further includes identifying a set of associations between the plurality of binary samples based on the extracted metadata. Each association is characterized by at least one attribute the associated binary samples have in common, and each association has a confidence level indicative of a strength of the association. The method also includes identifying associations with a confidence level that exceeds a predefined threshold.

Abstract: Systems and methods are provided for malware scanning and detection in a computing system. In one exemplary embodiment, the method includes launching, in a computing device of the computing system, a virtual machine, and launching, in the virtual machine of the computing device, an internet browser. The method also includes requesting, by the internet browser, data from a web page, and performing, using one or more analysis tools, analysis on the web page. In the method, performing analysis on the web page includes performing monitoring and recording of system application programming interface (API) calls, and creating software objects associated with the web page. The method also includes performing antivirus scanning of the software objects, de-obfuscating JavaScript associated with the software objects, and correlating data associated with the performed analysis to determine if the web page is a malicious web page.