Uncriminal presumption (IUPG): anti-opponent and anti-false positive deep learning model

• Main Authors: COOTE BRIAN J, STAROV OLEG, ZHOU YAN, HULETT WILLIAM ROBERT II
• Format: Patent
• Language: Chinese; English
• Published: 07.04.2023
• Subjects: ELECTRIC COMMUNICATION TECHNIQUE; ELECTRICITY; TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION

Techniques are disclosed for providing an IUPG solution for constructing and using an anti-opponent and anti-false positive deep learning model. In some embodiments, a system, process, and/or computer program product includes storing a set including one or more criminal inference (IUPG) models for static analysis of a sample; performing a static analysis of content associated with the sample, wherein performing the static analysis includes using at least one stored IUPG model; and determining that the sample is malicious based at least in part on a static analysis of content associated with the sample, and in response to determining that the sample is malicious, performing an action based on a security policy. 公开了用于提供无罪推定(IUPG)解决方案的技术，该解决方案用于构建和使用抗对手和抗假肯定的深度学习模型。在一些实施例中，系统、过程和/或计算机程序产品包括存储包括用于样本的静态分析的一个或多个无罪推定(IUPG)模型的集合；执行与样本相关联的内容的静态分析，其中执行静态分析包括使用至少一个存储的IUPG模型；以及至少部分地基于对与该样本相关联的内容的静态分析来确定该样本是恶意的，并且响应于确定该样本是恶意的，基于安全策略来执行动作。