Predictive Heap Overflow Protection

Abstract

A method for preventing malware attacks comprising: identifying a set of data whose malware status is not known to be safe; initiating an application that uses the data; determining that the application has created one or more previous determining that the application has created a new memory allocation; comparing the new memory allocation to a previous memory allocation; and based on the comparison, determining whether the data includes malware.

Description

technical field [0001] Generally, the present invention relates to computer security and malware protection, and more particularly, the present invention relates to predictive heap overflow protection. Background technique [0002] Malware infections on computers and other electronic devices are very intrusive and difficult to detect and fix. Anti-malware solutions may need to compare signatures of malicious code or files against evaluated software to determine that the software is harmful to the computing system. Malware may disguise itself by using polymorphic executable files, where the malware changes itself to avoid detection by anti-malware solutions. In such cases, anti-malware solutions may fail to detect new or variant malware in zero-day attacks. Malware may include, but is not limited to, spyware, rootware, password stealers, spam, phishing attacks, sources of denial of service attacks, viruses, loggers, Trojan horses, adware, or any other digital content. Co...

AI Technical Summary

Problems solved by technology

In such cases, anti-malware solutions may fail to detect new or variant malware in zero-day attacks

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