English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

As our professional, social, and financial existences become increasinglydigitized and as our government, healthcare, and military infrastructures relymore on computer technologies, they present larger and more lucrative targetsfor malware. Stealth malware in particular poses an increased threat because itis specifically designed to evade detection mechanisms, spreading dormant, inthe wild for extended periods of time, gathering sensitive information orpositioning itself for a high-impact zero-day attack. Policing the growingattack surface requires the development of efficient anti-malware solutionswith improved generalization to detect novel types of malware and resolve theseoccurrences with as little burden on human experts as possible. In this paper,we survey malicious stealth technologies as well as existing solutions fordetecting and categorizing these countermeasures autonomously. While machinelearning offers promising potential for increasingly autonomous solutions withimproved generalization to new malware types, both at the network level and atthe host level, our findings suggest that several flawed assumptions inherentto most recognition algorithms prevent a direct mapping between the stealthmalware recognition problem and a machine learning solution. The most notableof these flawed assumptions is the closed world assumption: that no samplebelonging to a class outside of a static training set will appear at querytime. We present a formalized adaptive open world framework for stealth malwarerecognition and relate it mathematically to research from other machinelearning domains.

A Survey of Stealth Malware: Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions