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/

Zendy Plus

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

Zendy Tools

Zendy Open

The popularity of Android means it is a common target for malware. Over theyears, various studies have found that machine learning models can effectivelydiscriminate malware from benign applications. However, as the operating systemevolves, so does malware, bringing into question the findings of these previousstudies, many of which report very high accuracies using small, outdated, andoften imbalanced datasets. In this paper, we reimplement 18 representative pastworks and reevaluate them using a balanced, relevant, and up-to-date datasetcomprising 124,000 applications. We also carry out new experiments designed tofill holes in existing knowledge, and use our findings to identify the mosteffective features and models to use for Android malware detection within acontemporary environment. We show that high detection accuracies (up to 96.8%)can be achieved using features extracted through static analysis alone,yielding a modest benefit (1%) from using far more expensive dynamic analysis.API calls and opcodes are the most productive static and TCP network trafficprovide the most predictive dynamic features. Random forests are generally themost effective model, outperforming more complex deep learning approaches.Whilst directly combining static and dynamic features is generally ineffective,ensembling models separately leads to performances comparable to the bestmodels but using less brittle features.

Investigating Feature and Model Importance in Android Malware Detection: An Implemented Survey and Experimental Comparison of ML-Based Methods