Open Text Corporation

Webroot Inc.

Mauritius A Schmidtler

Reza Yoosoofmiya

Chahm An

Gaurav Dalal

Kristina Theroux

Yuanhang Huang

Hal Case Lonas, Jr

Trung Tran

Catherine Yang

Kiran Kumar

Aspects of the present disclosure relate to threat detection of executable files. A plurality of static data points may be extracted from an executable file without decrypting or unpacking the executable file. The executable file may then be analyzed without decrypting or unpacking the executable file. Analysis of the executable file may comprise applying a classifier to the plurality of extracted static data points. The classifier may be trained from data comprising known malicious executable files, known benign executable files and known unwanted executable files. Based upon analysis of the executable file, a determination can be made as to whether the executable file is harmful.

Automatic threat detection of executable files based on static data analysis

Examples of the present disclosure describe systems and methods of automatic inline detection based on static data. In aspects, a file being received by a recipient device may be analyzed using an inline parser. The inline parser may identify sections of the file and feature vectors may be created for the identified sections. The feature vectors may be used to calculate a score corresponding to the malicious status of the file as the information is being analyzed. If a score is determined to exceed a predetermined threshold, the file download process may be terminated. In aspects, the received files, file fragments, feature vectors and/or additional data may be collected and analyzed to build a probabilistic model used to identify potentially malicious files.

Automatic inline detection based on static data

A computer system comprising a processor and a memory storing instructions that, when executed by the processor, cause the computer system to perform a set of operations. The set of operations comprises collecting domain attribute data comprising one or more domain attribute features for a domain, collecting sampled domain profile data comprising one or more domain profile features for the domain and generating, using the domain attribute data and the sampled domain profile data, a domain reputation assignment utilizing a neural network.

System and method for predicting domain reputation

Aspects of the present disclosure are operable to protect against malicious objects, such as JavaScript code, which may be encountered, downloaded, or otherwise accessed from a content source by a computing system. In an example, antivirus software implementing aspects disclosed herein may be capable of detecting malicious objects in real-time. Aspects of the present disclosure aim to reduce the amount of time used to detect malicious code while maintaining detection accuracy, as detection delays and/or a high false positive rate may result in a negative user experience. Among other benefits, the systems and methods disclosed herein are operable to identify malicious objects encountered by a computing system while maintaining a high detection rate, a low false positive rate, and a high scanning speed.

Real-time javascript classifier

Real-time JavaScript classifier

Growing community of inventors

San Diego, CA, United States of America

Mauritius A Schmidtler