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

Android is undergoing unprecedented malicious threats daily, but the existingmethods for malware detection often fail to cope with evolving camouflage inmalware. To address this issue, we present HAWK, a new malware detectionframework for evolutionary Android applications. We model Android entities andbehavioural relationships as a heterogeneous information network (HIN),exploiting its rich semantic metastructures for specifying implicithigher-order relationships. An incremental learning model is created to handlethe applications that manifest dynamically, without the need forre-constructing the whole HIN and the subsequent embedding model. The model canpinpoint rapidly the proximity between a new application and existing in-sampleapplications and aggregate their numerical embeddings under various semantics.Our experiments examine more than 80,860 malicious and 100,375 benignapplications developed over a period of seven years, showing that HAWK achievesthe highest detection accuracy against baselines and takes only 3.5ms onaverage to detect an out-of-sample application, with the accelerated trainingtime of 50x faster than the existing approach.

HAWK: Rapid Android Malware Detection through Heterogeneous Graph Attention Networks