Results 271 to 280 of about 306,676 (309)

DeepAMD: Detection and identification of Android malware using high-efficient Deep Artificial Neural Network

Future generations computer systems, 2021
Android smartphones are being utilized by a vast majority of users for everyday planning, data exchanges, correspondences, social interaction, business execution, bank transactions, and almost in each walk of everyday lives.
Syed Ibrahim Imtiaz, Saif ur Rehman, A. R. Javed, Z. Jalil, Xuan Liu, Waleed S. Alnumay   +5 more
semanticscholar   +1 more source

Dynamic Android Malware Category Classification using Semi-Supervised Deep Learning

2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), 2020
Due to the significant threat of Android mobile malware, its detection has become increasingly important. Despite the academic and industrial attempts, devising a robust and efficient solution for Android malware detection and category classification is ...
Samaneh Mahdavifar, Andi Fitriah Abdul Kadir, Rasool Fatemi, D. Alhadidi, A. Ghorbani   +4 more
semanticscholar   +1 more source

Enhancing State-of-the-art Classifiers with API Semantics to Detect Evolved Android Malware

Conference on Computer and Communications Security, 2020
Machine learning (ML) classifiers have been widely deployed to detect Android malware, but at the same time the application of ML classifiers also faces an emerging problem.
Xiaohan Zhang, Yuan Zhang, Ming Zhong, Dai-Zong Ding, Yinzhi Cao, Yukun Zhang, Mi Zhang, Min Yang   +7 more
semanticscholar   +1 more source

Android in the Zoo: Chain-of-Action-Thought for GUI Agents

Conference on Empirical Methods in Natural Language Processing
Large language model (LLM) leads to a surge of autonomous GUI agents for smartphone, which completes a task triggered by natural language through predicting a sequence of actions of API.
Ji-Wen Zhang, Jihao Wu, Yihua Teng, Minghui Liao, Nuo Xu, Xiaojun Xiao, Zhongyu Wei, Duyu Tang   +7 more
semanticscholar   +1 more source

AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents

arXiv.org
Autonomous agents have become increasingly important for interacting with the real world. Android agents, in particular, have been recently a frequently-mentioned interaction method.
Yi-Fan Xu, Xiao Liu, Xueqiao Sun, Siyi Cheng, Hao Yu, Hanyu Lai, Shudan Zhang, Dan Zhang, Jie Tang, Yu-Xiao Dong   +9 more
semanticscholar   +1 more source

AppPoet: Large Language Model based Android malware detection via multi-view prompt engineering

Expert systems with applications
Due to the vast array of Android applications, their multifarious functions and intricate behavioral semantics, attackers can adopt various tactics to conceal their genuine attack intentions within legitimate functions.
Wenxiang Zhao, Juntao Wu, Zhaoyi Meng
semanticscholar   +1 more source

Reinforcement learning based curiosity-driven testing of Android applications

International Symposium on Software Testing and Analysis, 2020
Mobile applications play an important role in our daily life, while it still remains a challenge to guarantee their correctness. Model-based and systematic approaches have been applied to Android GUI testing.
Minxue Pan, An Huang, Guo-Xin Wang, Tian Zhang, Xuandong Li   +4 more
semanticscholar   +1 more source

EntropLyzer: Android Malware Classification and Characterization Using Entropy Analysis of Dynamic Characteristics

2021 Reconciling Data Analytics, Automation, Privacy, and Security: A Big Data Challenge (RDAAPS), 2021
The unmatched threat of Android malware has tremendously increased the need for analyzing prominent malware samples. There are remarkable efforts in static and dynamic malware analysis using static features and API calls respectively.
David S. Keyes, Beiqi Li, Gurdip Kaur, Arash Habibi Lashkari, F. Gagnon, Frédéric Massicotte   +5 more
semanticscholar   +1 more source

PermPair: Android Malware Detection Using Permission Pairs

IEEE Transactions on Information Forensics and Security, 2020
The Android smartphones are highly prone to spreading the malware due to intrinsic feebleness that permits an application to access the internal resources when the user grants the permissions knowingly or unknowingly.
Anshul Arora, S. K. Peddoju, Mauro Conti
semanticscholar   +1 more source

Practical GUI Testing of Android Applications Via Model Abstraction and Refinement

International Conference on Software Engineering, 2019
This paper introduces a new, fully automated modelbased approach for effective testing of Android apps. Different from existing model-based approaches that guide testing with a static GUI model (i.e., the model does not evolve its abstraction during ...
Tianxiao Gu, Chengnian Sun, Xiaoxing Ma, Chun Cao, Chang Xu, Yuan Yao, Qirun Zhang, Jian Lu, Zhendong Su   +8 more
semanticscholar   +1 more source