The aim of this systematic literature review (SLR) is to provide a comprehensive overview of
the current state of Windows malware detection techniques, research issues, and future …

In this research, we compare malware detection techniques based on static, dynamic, and
hybrid analysis. Specifically, we train Hidden Markov Models (HMMs) on both static and …

The competing landscape between malware authors and security analysts is an ever‐
changing battlefield over who can innovate over the other. While security analysts are …

Research shows that over the last decade, malware have been growing exponentially,
causing substantial financial losses to various organizations. Different anti-malware …

Introduction to Machine Learning with Applications in Information Security, Second Edition
provides a classroom-tested introduction to a wide variety of machine learning and deep …

Previous research has shown that hidden Markov model (HMM) analysis is useful for
detecting certain challenging classes of malware. In this research, we consider the related …

After treating the notification of affected parties as mere side-notes in research, our
community has recently put more focus on how vulnerability disclosure can be conducted at …

The ever-increasing number of malware families and polymorphic variants creates a
pressing need for automatic tools to cluster the collected malware into families and generate …

Both the operational and academic security communities have used dynamic analysis
sandboxes to execute malware samples for roughly a decade. Network information derived …

Most modern malware download attacks occur via the browser, typically due to social
engineering and driveby downloads. In this paper, we study the “origin” of malware …