Machine learning models are immune to blandishments, threats, flattery and so - so why not set them against social engineers?
Read moreFireEye’s Data Science and Information Operations Analysis teams released this blog post to coincide with our Black Hat USA 2020 Briefing, which details how ...
Read moreFireEye’s Data Science and Information Operations Analysis teams released this blog post to coincide with our Black Hat USA 2020 Briefing, which details how ...
Read moreAttention is All They Need: Combatting Social Media Information Operations With Neural Language Models
November 14 2019Information operations have flourished on social media in part because they can be conducted cheaply, are relatively low risk, have immediate global reach, and can exploit the...
Read moreDefinitive Dossier of Devilish Debug Details – Part Deux: A Didactic Deep Dive into Data Driven Deductions
October 17 2019In Part One of this blog series, Steve Miller outlined what PDB paths are, how they appear in malware, how we use them to detect...
Read moreOpen Sourcing StringSifter
September 7 2019Malware analysts routinely use the Strings program during static analysis in order to inspect a binary's printable characters. However, identifying relevant strings by hand is time...
Read moreShowing Vulnerability to a Machine: Automated Prioritization of Software Vulnerabilities
August 13 2019Introduction If a software vulnerability can be detected and remedied, then a potential intrusion is prevented. While not all software vulnerabilities are known, 86 ...
Read moreIntroduction Machine learning (ML) is playing an increasingly important role in cyber security. Here at FireEye, we employ ML for a variety of tasks such as: antivirus, ...
Read moreWhat are Deep Neural Networks Learning About Malware?
December 13 2018An increasing number of modern antivirus solutions rely on machine learning (ML) techniques to protect users from malware. While ML-based approaches, like FireEye Endpoint Security’s MalwareGuard ...
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