AndroDex: Android Dex images of obfuscated malware

S Aurangzeb, M Aleem, MT Khan, G Loukas… - Scientific Data, 2024 - nature.com
With the emergence of technology and the usage of a large number of smart devices, cyber
threats are increasing. Therefore, research studies have shifted their attention to detecting …

ImageDroid: Using deep learning to efficiently detect Android malware and automatically mark malicious features

P Liu, W Wang, S Zhang, H Song - Security and …, 2023 - Wiley Online Library
The popularity of the Android platform has led to an explosion in malware. The current
research on Android malware mainly focuses on malware detection or malware family …

A large-scale investigation to identify the pattern of app component in obfuscated Android malwares

MOFK Russel, SSMM Rahman, T Islam - … 2020, Silchar, India, July 30-31 …, 2020 - Springer
Abstract Number of smartphone users of android based devices is growing rapidly. Because
of the popularity of the android market malware attackers are focusing in this area for their …

Utilizing obfuscation information in deep learning-based Android malware detection

J Wu, A Kanai - 2021 IEEE 45th Annual Computers, Software …, 2021 - ieeexplore.ieee.org
With the large number of Android applications being released, reliable Android malware
classifier is required. In recent years, machine learning as well as deep learning have …

[PDF][PDF] Droidvecdeep: Android malware detection based on word2vec and deep belief network

T Chen, Q Mao, M Lv, H Cheng, Y Li - KSII Transactions on Internet …, 2019 - koreascience.kr
With the proliferation of the Android malicious applications, malware becomes more capable
of hiding or confusing its malicious intent through the use of code obfuscation, which has …

Evaluation and classification of obfuscated Android malware through deep learning using ensemble voting mechanism

S Aurangzeb, M Aleem - Scientific Reports, 2023 - nature.com
With the rise in popularity and usage of Android operating systems, malicious applications
are targeted by applying innovative ways and techniques. Today, malware becomes …

Deep learning-based multi-classification for malware detection in IoT

Z Wang, Q Liu, Z Wang, Y Chi - Journal of Circuits, Systems and …, 2022 - World Scientific
Due to the open-source and versatility of the Android operating system, Android malware
has exploded, and the malware detection of Android IoT devices has become a research …

Obfuscated Malware Detection in IoT Android Applications Using Markov Images and CNN

KA Dhanya, P Vinod, SY Yerima, A Bashar… - IEEE Systems …, 2023 - ieeexplore.ieee.org
The threat of malware in the Internet of Things (IoT) is ever-present given that many IoT
systems today rely on the Android operating system. There has been a consistent rise in …

Android malware obfuscation variants detection method based on multi-granularity opcode features

J Tang, R Li, Y Jiang, X Gu, Y Li - Future Generation Computer Systems, 2022 - Elsevier
Android malware poses a serious security threat to ordinary mobile users. However, the
obfuscation technology can generate malware variants, which can bypass existing detection …

Aomdroid: detecting obfuscation variants of android malware using transfer learning

Y Jiang, R Li, J Tang, A Davanian, H Yin - … , DC, USA, October 21-23, 2020 …, 2020 - Springer
Android with its large market attracts malware developers. Malware developers employ
obfuscation techniques to bypass malware detection mechanisms. Existing systems cannot …