Ransomware, as a specialized form of malicious software, has recently emerged as a major
threat in computer security. With an ability to lock out user access to their content, recent …

Adversarial examples are known to mislead deep learning models to incorrectly classify
them, even in domains where such models achieve state-of-the-art performance. Until …

In this paper, we present a generic, query-efficient black-box attack against API call-based
machine learning malware classifiers. We generate adversarial examples by modifying the …

We propose a hybrid machine learning architecture that simultaneously employs multiple
deep learning models analyzing contextual and behavioral characteristics of Windows …

A Comprehensive Tutorial and Survey of Applications of Deep Learning for Cyber Security
Page 1 P osted on 5 Jan 2020 — CC-BY 4.0 — h ttps://doi.org/10.36227/tech rxiv.11473377.v1 …

In addition to using signatures, antimalware products also detect malicious attacks by
evaluating unknown files in an emulated environment, ie sandbox, prior to execution on a …

Malicious scripts are an important computer infection threat vector for computer users. For
internet-scale processing, static analysis offers substantial computing efficiencies. We …

Antimalware products are a key component in detecting malware attacks, and their engines
typically execute unknown programs in a sandbox prior to running them on the native …

This tutorial provides a review of the state-of-the-art research and the applications of Artificial
Intelligence and Machine Learning for malware analysis. We will provide an overview …

Adversarial examples are known to mislead deep learning models so that the models will
classify them incorrectly, even in domains where such models have achieved state-of-the-art …