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Why an Android App is Classified as Malware? Towards Malware Classification Interpretation [article]

Bozhi Wu, Sen Chen, Cuiyun Gao, Lingling Fan, Yang Liu, Weiping Wen, Michael R. Lyu
2020 arXiv   pre-print
Machine learning (ML) based approach is considered as one of the most promising techniques for Android malware detection and has achieved high accuracy by leveraging commonly-used features.  ...  Our study peeks into the interpretable ML through the research of Android malware detection and analysis.  ...  We conduct comprehensive experiments to demonstrate its interpretability of Android malware detection, and the results show that XMal can detect Android malware effectively, with 97.04% accuracy, and can  ... 
arXiv:2004.11516v2 fatcat:l6g6unutlrclbpjdoocgrvs6hu

FedHGCDroid: An Adaptive Multi-Dimensional Federated Learning for Privacy-Preserving Android Malware Classification

Changnan Jiang, Kanglong Yin, Chunhe Xia, Weidong Huang
2022 Entropy  
With the popularity of Android and its open source, the Android platform has become an attractive target for hackers, and the detection and classification of malware has become a research hotspot.  ...  Secondly, we introduce an FL framework to enable distributed Android clients to collaboratively train a comprehensive Android malware classification model in a privacy-preserving way.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/e24070919 pmid:35885142 pmcid:PMC9317647 fatcat:urlnwe2gsjg4xlnwenb5kecw3e

Deep Learning for Android Malware Defenses: a Systematic Literature Review [article]

Yue Liu, Chakkrit Tantithamthavorn, Li Li, Yepang Liu
2022 arXiv   pre-print
However, given the explosive growth of Android malware and the continuous advancement of malicious evasion technologies like obfuscation and reflection, Android malware defense approaches based on manual  ...  Yet, no systematic literature review focusing on deep learning approaches for Android Malware defenses exists.  ...  Specifically, they first maintained an effective Android malware detection model on the server side before migrating the pre-trained model to TensorFlow-lite 4 model.  ... 
arXiv:2103.05292v2 fatcat:qruddq4gknfq7jx5wyrk5qu2eu

Android Malware Detection via Graph Representation Learning

Pengbin Feng, Jianfeng Ma, Teng Li, Xindi Ma, Ning Xi, Di Lu, Raul Montoliu
2021 Mobile Information Systems  
The experimental results demonstrate that our approach implements high effective malware detection and outperforms state-of-the-art detection approaches.  ...  There is an urgent need for developing an automatic malware detection approach to prevent the spread of malware.  ...  can be effective in Android malware family detection.  ... 
doi:10.1155/2021/5538841 fatcat:o4beznwd4zadvcqfqubbwgalmy

Predicting the Impact of Android Malicious Samples via Machine Learning

Junyang Qiu, Wei Luo, Lei Pan, Yonghang Tai, Jun Zhang, Yang Xiang
2019 IEEE Access  
Their power of learning complex patterns and behaviors make Deep Neural Networks an appropriate technique for malware detection or classification.  ...  In the malware analysis area, it is expensive and difficult for security experts to manually generating and updating the malware detection patterns (or signatures) for Android malware.  ...  His translational research has made significant impact on the real-world applications, such as AI-driven cyber security applications, malware applications, cloud and the IoT security applications, and  ... 
doi:10.1109/access.2019.2914311 fatcat:hnmxjngmnfdyllzeqa2tctf4qm

HomDroid: Detecting Android Covert Malware by Social-Network Homophily Analysis [article]

Yueming Wu, Deqing Zou, Wei Yang, Xiang Li, Hai Jin
2021 arXiv   pre-print
In this paper, we call this type of malware as Android covert malware and generate the first dataset of covert malware. To detect them, we first conduct static analysis to extract the call graphs.  ...  Android has become the most popular mobile operating system. Correspondingly, an increasing number of Android malware has been developed and spread to steal users' private information.  ...  U1936211, 'the Fundamental Research Funds for the Central Universities', HUST: 2020JYCXJJ068, and UT Dallas startup funding #37030034.  ... 
arXiv:2107.04743v1 fatcat:oksf2hyn45gi7o74k6qxev4ilq

Automatic Detection of Android Malware via Hybrid Graph Neural Network

Chunyan Zhang, Qinglei Zhou, Yizhao Huang, Ke Tang, Hairen Gui, Fudong Liu
2022 Wireless Communications and Mobile Computing  
Android malware attacks have gained tremendous pace owing to the widespread use of mobile devices.  ...  Furthermore, the dynamic analysis is low code coverage and poor efficiency. Hence, we propose an automatic Android malware detection approach, named HyGNN-Mal.  ...  apps suitable for static analysis, for achieving a more accurate classification of families and even striving to effectively detect the newly generated malware. 2. 1 . 1 Deep_TNN (Deep Traversal Tree  ... 
doi:10.1155/2022/7245403 doaj:1c2e36354869485d8bc4c837590b80cf fatcat:bormdkslvbfbnlmieidp6cunxu

Android Malware Detection Based on a Hybrid Deep Learning Model

Tianliang Lu, Yanhui Du, Li Ouyang, Qiuyu Chen, Xirui Wang
2020 Security and Communication Networks  
and it also has a better detection effect on obfuscated malware.  ...  In recent years, the number of malware on the Android platform has been increasing, and with the widespread use of code obfuscation technology, the accuracy of antivirus software and traditional detection  ...  In traditional machine learning algorithms, the SVM algorithm is often used for Android malware detection, and it has a good classification effect in many cases. Li et al.  ... 
doi:10.1155/2020/8863617 fatcat:bdgc44k6kjggto6kmkakgd3r7u

HEFESTDROID: Highly Effective Features for Android Malware Detection and Analysis

Shafiu Musa Et.al
2021 Turkish Journal of Computer and Mathematics Education  
The Highly Effective Features for Android Malware Detection and Analysis (HEFEST) summarises four effective android permission features to be considered in conducting malware detection analysis and classifications  ...  accuracy ratio of malware detection, the classifier has a strong accuracy decision of classification and exceptional computational efficiency.  ...  adopts support vector machine (SVM) classification model for Android malware detection based on a using IG and PSO feature weights.  ... 
doi:10.17762/turcomat.v12i3.1884 fatcat:j76msctpwzeh5lwpysxaj7ckvq

Android-SEM: Generative Adversarial Network for Android Malware Semantic Enhancement Model Based on Transfer Learning

Yizhao Huang, Xingwei Li, Meng Qiao, Ke Tang, Chunyan Zhang, Hairen Gui, Panjie Wang, Fudong Liu
2022 Electronics  
The results proved that Android-SEM achieves accuracy levels of 99.55% and 99.01% for malware detection and malware categorization, respectively.  ...  Therefore, it is imperative to develop approaches for detecting Android malware.  ...  The aforementioned results show that the semantic enhancement model based on transfer learning is effective for Android malware detection and classification tasks. For comparison, Sihag et al.  ... 
doi:10.3390/electronics11050672 fatcat:2hpovbmmg5h4liznlu36klhlpe

Droidetec: Android Malware Detection and Malicious Code Localization through Deep Learning [article]

Zhuo Ma, Haoran Ge, Zhuzhu Wang, Yang Liu, Ximeng Liu
2020 arXiv   pre-print
In this paper, we propose Droidetec, a deep learning based method for android malware detection and malicious code localization, to model an application program as a natural language sequence.  ...  Android malware detection is a critical step towards building a security credible system. Especially, manual search for the potential malicious code has plagued program analysts for a long time.  ...  For instance, Drebin [24] implements an effective and explainable detection for Android malware that extracts 8 feature sets from the manifest and disassembled code.  ... 
arXiv:2002.03594v1 fatcat:2pflabo2z5ebnf2kli4ozxdeai

The Android malware detection systems between hope and reality

Khaled Bakour, Halil Murat Ünver, Razan Ghanem
2019 SN Applied Sciences  
Moreover, we have proposed a detailed schematic model (called Schematic Review Model) illustrates the process of detecting the malignant applications of an Android in the light of the studied works and  ...  Also, there is no comprehensive taxonomy for all research trends in the field of analysing malicious applications targeting the Android system.  ...  RQ3 Is it possible to express the techniques and methods that used in Android malware detection frameworks using a comprehensive schematic model inspired from the studied works?  ... 
doi:10.1007/s42452-019-1124-x fatcat:jzbb6ruykrcw3nuwps4qb4fuze

An efficient Android malware detection system based on method-level behavioral semantic analysis

Hanqing Zhang, Senlin Luo, Yifei Zhang, Limin Pan
2019 IEEE Access  
The results of our empirical evaluation show our system is competitive in terms of classification accuracy and detection efficiency.  ...  INDEX TERMS Android malware detection, abstracted API call, association analysis, behavioral semantics, machine learning. 69246 2169-3536  ...  Therefore, the effective and efficient detection techniques are urgently needed to cope with the increasing sophistication of Android malware.  ... 
doi:10.1109/access.2019.2919796 fatcat:dqsbi4nlfzf3lfo2rqjbfm77ue

AiDroid: When Heterogeneous Information Network Marries Deep Neural Network for Real-time Android Malware Detection [article]

Yanfang Ye, Shifu Hou, Lingwei Chen, Jingwei Lei, Wenqiang Wan, Jiabin Wang, Qi Xiong, Fudong Shao
2019 arXiv   pre-print
The explosive growth and increasing sophistication of Android malware call for new defensive techniques that are capable of protecting mobile users against novel threats.  ...  Afterwards, we design a deep neural network (DNN) classifier taking the learned HIN representations as inputs for Android malware detection.  ...  Acknowledgement The authors would also like to thank the anti-malware experts of Tencent Security Lab (Yinming Mei, Yuanhai Luo, Hong Yi, and Kui Wang) for helpful discussion and implementation. Y.  ... 
arXiv:1811.01027v2 fatcat:v7bea5oswbe6vpzxs23ab4k2ka

A Systematic Literature Review of Android Malware Detection Using Static Analysis

Ya Pan, Xiuting Ge, Chunrong Fang, Yong Fan
2020 IEEE Access  
Then we assess the malware detection capability of static analysis, and we compare the performance of different models in Android malware detection by analyzing the results of empirical evidence.  ...  Finally, it is concluded that static analysis is effective to detect Android malware.  ...  CONCLUSION AND FUTURE WORK This paper summarizes the state-of-the-art techniques and provides a comprehensive overview of Android malware detection using static analysis.  ... 
doi:10.1109/access.2020.3002842 fatcat:uzforojdpndljf6qgscjpgsns4
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