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 …

Android malware familial classification and representative sample selection via frequent subgraph analysis

M Fan, J Liu, X Luo, K Chen, Z Tian… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
The rapid increase in the number of Android malware poses great challenges to anti-
malware systems, because the sheer number of malware samples overwhelms malware …

Gsdroid: Graph signal based compact feature representation for android malware detection

R Surendran, T Thomas, S Emmanuel - Expert Systems with Applications, 2020 - Elsevier
Android malwares have evolved in sophistications and intelligence to become more and
more evasive to existing detection systems especially those that are signature-based …

A qualitative analysis of android taint-analysis results

L Luo, E Bodden, J Späth - 2019 34th IEEE/ACM International …, 2019 - ieeexplore.ieee.org
In the past, researchers have developed a number of popular taint-analysis approaches,
particularly in the context of Android applications. Numerous studies have shown that …

Andrensemble: Leveraging api ensembles to characterize android malware families

O Mirzaei, G Suarez-Tangil, JM de Fuentes… - Proceedings of the …, 2019 - dl.acm.org
Assigning family labels to malicious apps is a common practice for grouping together
malware with identical behavior. However, recent studies show that apps labeled as …

AppSpear: Automating the hidden-code extraction and reassembling of packed android malware

B Li, Y Zhang, J Li, W Yang, D Gu - Journal of Systems and Software, 2018 - Elsevier
Code packing is one of the most frequently used protection techniques for malware to evade
detection. Particularly, Android packers originally designed to protect intellectual property …

TriDroid: a triage and classification framework for fast detection of mobile threats in android markets

A Amira, A Derhab, EMB Karbab, O Nouali… - Journal of Ambient …, 2021 - Springer
The Android platform is highly targeted by malware developers, which aim to infect the
maximum number of mobile devices by uploading their malicious applications to different …

[PDF][PDF] Blockchain-based Deep Learning Algorithm for Detecting Malware.

D Denysiuk, O Geidarova, M Kapustian, S Lysenko… - IntelITSIS, 2023 - ceur-ws.org
Nowadays malware detection is a very important task in information security. Criminals are
constantly looking for new ways to attack computer networks and systems, so it is important …

FloVasion: towards detection of non-sensitive variable based evasive information-flow in android apps

B Buddhadev, P Faruki, MS Gaur… - IETE Journal of …, 2022 - Taylor & Francis
Smartphones are enriched by applications (apps) available through the mobile ecosystem.
Various studies have reported that apps leaking sensitive user and device information are …

SVM-based technique for mobile malware detection

S Lysenko, K Bobrovnikova, A Nicheporuk… - CEUR Workshop …, 2019 - elibrary.ru
A paper presents a new technique for the mobile malware detection based on the malware's
network features analysis is proposed. It uses SVM for malicious programs detection. The …