Article
Multidisciplinary Sciences
Sana Aurangzeb, Muhammad Aleem
Summary: With the rise in popularity and usage of Android operating systems, innovative ways and techniques are being used to target malicious applications. This study proposes an approach to identify Android malware obfuscation variations and addresses the challenges related to classification and detection. The proposed approach uses static and dynamic analysis and demonstrates the importance of features in obfuscating benign and malware applications. The experiments show that the proposed model effectively detects malware and identifies obfuscated features.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Information Systems
Younghoon Ban, Sunjun Lee, Dokyung Song, Haehyun Cho, Jeong Hyun Yi
Summary: This study focuses on deep learning-based familial analysis of Android malware by examining different features and their effectiveness in representing malicious behaviors. The evaluation on a real-world malware dataset of 28,179 samples reveals the contribution of different features to the performance of familial analysis. With all features combined, the study achieves a high accuracy and micro F1-score.
Article
Chemistry, Multidisciplinary
Subhan Ullah, Tahir Ahmad, Attaullah Buriro, Nudrat Zara, Sudipan Saha
Summary: This paper proposes a multi-layer hybrid approach for Trojan detection, which analyzes the behavior of each downloaded application and extracts correlated features to determine whether it is a Trojan. The proposed approach combines static and dynamic analysis features for feature extraction. Evaluation on multiple datasets shows the effectiveness of this approach.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
He Peng, Defu Zhang
Summary: Mining frequent graph patterns and associated problems are of great importance. However, the exponential growth of frequent subgraphs makes it inefficient to mine large graph patterns. To overcome this issue, a novel closed frequent subgraph mining algorithm, CFGM, is proposed. The algorithm utilizes a stack-based architecture and a strict partial order to enumerate and prune unnecessary frequent subgraphs.
INFORMATION SCIENCES
(2023)
Article
Green & Sustainable Science & Technology
Altyeb Taha, Omar Barukab
Summary: This paper proposes an ensemble learning method based on genetic algorithms for Android malware classification, which showed higher accuracy and precision in experiments.
Review
Computer Science, Artificial Intelligence
Monika Sharma, Ajay Kaul
Summary: This article examines the detection methods of malware on Android mobile devices and provides an in-depth review of previous experiments using machine learning. It thoroughly analyzes the origins, evolution, and sustainability of Android malware detection, and suggests possible research paths.
Article
Computer Science, Information Systems
Sunder Ali Khowaja, Parus Khuwaja
Summary: This study integrates Q-learning characteristics into an active learning framework, allowing the network to request or predict labels during training. By using a handful of labeled examples, the network can classify malware applications more accurately.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Qian Li, Qingyuan Hu, Yong Qi, Saiyu Qi, Xinxing Liu, Pengfei Gao
Summary: The article introduces a system for analyzing the familial of Android malware, named GSFDroid. This system utilizes graph features and Graph Convolutional Networks to embed features, improving the efficiency of downstream analytics tasks. By using a simple graph feature normalization method to standardize embedded APK features, the system effectively clusters new malware samples from unknown families.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Hui-juan Zhu, Wei Gu, Liang-min Wang, Zhi-cheng Xu, Victor S. Sheng
Summary: The popularity and flexibility of the Android platform make it a prime target for malicious attackers. By extracting permissions, API calls, and hardware features, a new malware detection framework called MSerNetDroid is proposed. The framework utilizes a novel architectural unit, Multi-Head Squeeze-and-Excitation Residual block (MSer), to learn the correlation between features and recalibrate them from multiple perspectives. Experimental results show that MSerNetDroid successfully detects malware with an accuracy of 96.48%, outperforming state-of-the-art approaches.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Han Gao, Shaoyin Cheng, Weiming Zhang
Summary: The paper introduces a novel approach for Android malware detection and familial classification based on Graph Convolutional Network (GCN). Through experiments, GDroid system shows promising results in detecting Android malware and classifying malware families, outperforming existing methods.
COMPUTERS & SECURITY
(2021)
Article
Mathematics
Altyeb Taha, Omar Barukab, Sharaf Malebary
Summary: The open-source nature of Android OS and the inclusion of third-party apps have led to potential threats to user privacy. This study introduces a novel fuzzy integral-based multi-classifier ensemble for Android malware classification, achieving a high accuracy rate of 95.08% in experiments.
Article
Multidisciplinary Sciences
TianYue Liu, HongQi Zhang, HaiXia Long, Jinmei Shi, YuHua Yao
Summary: This study proposes a new method for Android malware classification, called BIR-CNN, which combines CNN, batch normalization, and inception-residual network modules, achieving high accuracy rates. Experimental results demonstrate the effectiveness of this model in classification of Android malware, especially in malware category and family classification.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Information Systems
Akshay Mathur, Laxmi Mounika Podila, Keyur Kulkarni, Quamar Niyaz, Ahmad Y. Javaid
Summary: The rapid growth and popularity of Android apps have made it a vulnerable target for malware, prompting researchers to propose a new malware detection framework called NATICUSdroid. By using statistically selected Android permissions for classification, the experimental results show that the Random Forest classifier-based model performed best with an accuracy of 97%.
JOURNAL OF INFORMATION SECURITY AND APPLICATIONS
(2021)
Article
Chemistry, Analytical
Mohammed Rashed, Guillermo Suarez-Tangil
Summary: The study conducted extensive research on the Android malware ecosystem, limitations of using existing malware classification services, and inconsistencies between antivirus engines, offering insights into the challenges with Android malware family labels provided by common AV engines.
Article
Computer Science, Theory & Methods
Junwei Tang, Ruixuan Li, Yu Jiang, Xiwu Gu, Yuhua Li
Summary: Android malware poses a serious security threat, and obfuscation technology can generate variants that bypass existing detection methods. The proposed MGOPDroid system combines opcode feature extraction, TFIDF algorithm, and deep learning detection model for efficient anti-obfuscation Android malware detection. Experimental results show that the detection accuracy for both unobfuscated and obfuscated malware samples is over 90% with MGOPDroid.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
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Computer Science, Theory & Methods
Le Yu, Tao Zhang, Xiapu Luo, Lei Xue, Henry Chang
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2017)
Article
Computer Science, Software Engineering
Le Yu, Xiapu Luo, Chenxiong Qian, Shuai Wang, Hareton K. N. Leung
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2018)
Article
Computer Science, Software Engineering
Ting Chen, Xiaoqi Li, Xiapu Luo, Xiaosong Zhang
SOFTWARE QUALITY JOURNAL
(2018)
Article
Engineering, Electrical & Electronic
Peng Zhang, Joseph K. Liu, F. Richard Yu, Mehdi Sookhak, Man Ho Au, Xiapu Luo
IEEE COMMUNICATIONS MAGAZINE
(2018)
Article
Computer Science, Artificial Intelligence
Xiaobo Ma, Yihui He, Xiapu Luo, Jianfeng Li, Mengchen Zhao, Bo An, Xiaohong Guan
IEEE INTELLIGENT SYSTEMS
(2018)
Article
Computer Science, Theory & Methods
Lei Xue, Xiaobo Ma, Xiapu Luo, Edmond W. W. Chan, Tony T. N. Miu, Guofei Gu
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2018)
Article
Computer Science, Theory & Methods
Chenxu Wang, Tony T. N. Miu, Xiapu Luo, Jinhe Wang
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2018)
Proceedings Paper
Computer Science, Software Engineering
Lei Xue, Yajin Zhou, Ting Chen, Xiapu Luo, Guofei Gu
PROCEEDINGS OF THE 26TH USENIX SECURITY SYMPOSIUM (USENIX SECURITY '17)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Muhui Jiang, Chenxu Wang, Xiapu Luo, MiuTung Miu, Ting Chen
2017 IEEE 24TH INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS 2017)
(2017)
Proceedings Paper
Computer Science, Software Engineering
Lei Xue, Xiapu Luo, Le Yu, Shuai Wang, Dinghao Wu
2017 IEEE/ACM 39TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Shengtuo Hu, Xiaobo Ma, Muhui Jiang, Xiapu Luo, Man Ho Au
2017 IEEE 36TH INTERNATIONAL SYMPOSIUM ON RELIABLE DISTRIBUTED SYSTEMS (SRDS)
(2017)
Proceedings Paper
Computer Science, Software Engineering
Tao Zhang, Jiachi Chen, He Jiang, Xiapu Luo, Xin Xia
2017 IEEE/ACM 25TH INTERNATIONAL CONFERENCE ON PROGRAM COMPREHENSION (ICPC)
(2017)
Proceedings Paper
Computer Science, Software Engineering
Ting Chen, Youzheng Feng, Xiapu Luo, Xiaodong Lin, Xiaosong Zhang
2017 IEEE 24TH INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION, AND REENGINEERING (SANER)
(2017)
Proceedings Paper
Computer Science, Software Engineering
Ting Chen, Xiaoqi Li, Xiapu Luo, Xiaosong Zhang
2017 IEEE 24TH INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION, AND REENGINEERING (SANER)
(2017)