One of the main challenges in security today is defending against malware attacks. As
trends and anecdotal evidence show, preventing these attacks, regardless of their …

Conventional attacks of insider employees and emerging APT are both major threats for the
organizational information system. Existing detections mainly concentrate on users' behavior …

Machine learning (ML) based malicious traffic detection is an emerging security paradigm,
particularly for zero-day attack detection, which is complementary to existing rule based …

A processing device in one embodiment comprises a processor coupled to a memory and is
configured to obtain internal log data of a computer network of an enterprise, to extract …

VirusTotal provides malware labels from a large set of anti-malware engines, and is heavily
used by researchers for malware annotation and system evaluation. Since different engines …

Our current defenses against IoT malware may not be adequate to remediate an IoT
malware attack similar to the Mirai botnet. This work seeks to investigate this matter by …

Recent years have seen the rise of sophisticated attacks including advanced persistent
threats (APT) which pose severe risks to organizations and governments. Additionally, new …

Learning on execution behaviour, ie, sequences of API calls, is proven to be effective in
malware detection. In this paper, we present CruParamer, a deep neural network based …

In this paper, we propose HyperVision, a realtime unsupervised machine learning (ML)
based malicious traffic detection system. Particularly, HyperVision is able to detect unknown …

New and unseen polymorphic malware, zero-day attacks, or other types of advanced
persistent threats are usually not detected by signature-based security devices, firewalls, or …