A comprehensive survey on deep learning based malware detection techniques

M Gopinath, SC Sethuraman - Computer Science Review, 2023 - Elsevier
Recent theoretical and practical studies have revealed that malware is one of the most
harmful threats to the digital world. Malware mitigation techniques have evolved over the …

A survey of android malware detection with deep neural models

J Qiu, J Zhang, W Luo, L Pan, S Nepal… - ACM Computing Surveys …, 2020 - dl.acm.org
Deep Learning (DL) is a disruptive technology that has changed the landscape of cyber
security research. Deep learning models have many advantages over traditional Machine …

[HTML][HTML] DL-Droid: Deep learning based android malware detection using real devices

MK Alzaylaee, SY Yerima, S Sezer - Computers & Security, 2020 - Elsevier
The Android operating system has been the most popular for smartphones and tablets since
2012. This popularity has led to a rapid raise of Android malware in recent years. The …

Dynamic malware analysis in the modern era—A state of the art survey

O Or-Meir, N Nissim, Y Elovici, L Rokach - ACM Computing Surveys …, 2019 - dl.acm.org
Although malicious software (malware) has been around since the early days of computers,
the sophistication and innovation of malware has increased over the years. In particular, the …

Flowdroid: Precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for android apps

S Arzt, S Rasthofer, C Fritz, E Bodden, A Bartel… - ACM sigplan …, 2014 - dl.acm.org
Today's smartphones are a ubiquitous source of private and confidential data. At the same
time, smartphone users are plagued by carelessly programmed apps that leak important …

The evolution of android malware and android analysis techniques

K Tam, A Feizollah, NB Anuar, R Salleh… - ACM Computing …, 2017 - dl.acm.org
With the integration of mobile devices into daily life, smartphones are privy to increasing
amounts of sensitive information. Sophisticated mobile malware, particularly Android …

Mamadroid: Detecting android malware by building markov chains of behavioral models (extended version)

L Onwuzurike, E Mariconti, P Andriotis… - ACM Transactions on …, 2019 - dl.acm.org
As Android has become increasingly popular, so has malware targeting it, thus motivating
the research community to propose different detection techniques. However, the constant …

Iccta: Detecting inter-component privacy leaks in android apps

L Li, A Bartel, TF Bissyandé, J Klein… - 2015 IEEE/ACM 37th …, 2015 - ieeexplore.ieee.org
Shake Them All is a popular" Wallpaper" application exceeding millions of downloads on
Google Play. At installation, this application is given permission to (1) access the Internet (for …

Static analysis of android apps: A systematic literature review

L Li, TF Bissyandé, M Papadakis, S Rasthofer… - Information and …, 2017 - Elsevier
Context Static analysis exploits techniques that parse program source code or bytecode,
often traversing program paths to check some program properties. Static analysis …

[PDF][PDF] Information flow analysis of android applications in droidsafe.

MI Gordon, D Kim, JH Perkins, L Gilham, N Nguyen… - NDSS, 2015 - people.csail.mit.edu
We present DroidSafe, a static information flow analysis tool that reports potential leaks of
sensitive information in Android applications. DroidSafe combines a comprehensive …