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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

As Android malware is growing and evolving, deep learning has been introducedinto malware detection, resulting in great effectiveness. Recent work isconsidering hybrid models and multi-view learning. However, they use onlysimple features, limiting the accuracy of these approaches in practice. In thispaper, we propose DeepCatra, a multi-view learning approach for Android malwaredetection, whose model consists of a bidirectional LSTM (BiLSTM) and a graphneural network (GNN) as subnets. The two subnets rely on features extractedfrom statically computed call traces leading to critical APIs derived frompublic vulnerabilities. For each Android app, DeepCatra first constructs itscall graph and computes call traces reaching critical APIs. Then, temporalopcode features used by the BiLSTM subnet are extracted from the call traces,while flow graph features used by the GNN subnet are constructed from all thecall traces and inter-component communications. We evaluate the effectivenessof DeepCatra by comparing it with several state-of-the-art detectionapproaches. Experimental results on over 18,000 real-world apps and prevalentmalware show that DeepCatra achieves considerable improvement, e.g., 2.7% to14.6% on F1-measure, which demonstrates the feasibility of DeepCatra inpractice.

DeepCatra: Learning Flow- and Graph-based Behaviors for Android Malware Detection