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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

A lot of malicious applications appears every day, threatening numerous users. Therefore, a surge of studies have been conducted to protect users from newly emerging malware by using machine learning algorithms. Albeit existing machine or deep learning-based Android malware detection approaches achieve high accuracy by using a combination of multiple features, it is not possible to employ them on our mobile devices due to the high cost for using them. In this paper, we propose  MAPAS , a malware detection system, that achieves high accuracy and adaptable usages of computing resources.  MAPAS  analyzes behaviors of malicious applications based on API call graphs of them by using convolution neural networks (CNN). However,  MAPAS  does not use a classifier model generated by CNN, it only utilizes CNN for discovering common features of API call graphs of malware. For efficiently detecting malware,  MAPAS  employs a lightweight classifier that calculates a similarity between API call graphs used for malicious activities and API call graphs of applications that are going to be classified. To demonstrate the effectiveness and efficiency of  MAPAS , we implement a prototype and thoroughly evaluate it. And, we compare  MAPAS  with a state-of-the-art Android malware detection approach, MaMaDroid. Our evaluation results demonstrate that  MAPAS  can classify applications 145.8% faster and uses memory around ten times lower than MaMaDroid. Also,  MAPAS  achieves higher accuracy (91.27%) than MaMaDroid (84.99%) for detecting unknown malware. In addition,  MAPAS  can generally detect any type of malware with high accuracy.

MAPAS: a practical deep learning-based android malware detection system