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Companion Proceedings of the ACM on Web Conference 2025

作者： Nishavi Ranaweera Jiarui Xu Suranga Seneviratne Aruna Seneviratne University of New South Wales Sydney Australia University of Sydney Sydney Australia

ISBN: (纸本)9798400713316

Web access today occurs predominantly through mobile devices, with android representing a significant share of the mobile device market. This widespread usage makes android a prime target for malicious attacks. Despite efforts to combat malicious attacks through tools like Google Play Protect and antivirus software, new and evolved malware continues to infiltrate android devices. Source code analysis is effective but limited, as attackers quickly abandon old malware for new variants to evade detection. Therefore, there is a need for alternative methods that complement source code analysis. Prior research investigated clustering applications based on their descriptions and identified outliers in these clusters by API usage as malware. However, these works often used traditional techniques such as Latent Dirichlet Allocation (LDA) and k-means clustering, that do not capture the nuanced semantic structures present in app descriptions. To this end, in this paper, we propose BERTDetect, which leverages the BERTopic neural topic modelling to effectively capture the latent topics in app descriptions. The resulting topic clusters are comparatively more coherent than previous methods and represent the app functionalities well. Our results demonstrate that BERTDetect outperforms other baselines, achieving ~10% relative improvement in F1 score.

关键词： android malware detection

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Impact of Code Deobfuscation and Feature Interaction in android malware detection

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IEEE ACCESS 2021年 9卷 123208-123219页

作者： Chen, Yun-Chung Chen, Hong-Yen Takahashi, Takeshi Sun, Bo Lin, Tsung-Nan Natl Taiwan Univ Grad Inst Elect Engn Taipei 10617 Taiwan Natl Taiwan Univ Grad Inst Commun Engn Taipei 10617 Taiwan Natl Inst Informat & Commun Technol Koganei Tokyo 1848795 Japan Natl Taiwan Univ Dept Elect Engn Taipei 10617 Taiwan Natl Taiwan Univ Grad Inst Commun Engn GICE Taipei 10617 Taiwan

With more than three million applications already in the android marketplace, various malware detection systems based on machine learning have been proposed to prevent attacks from cybercriminals;most of these systems use static analyses to extract application features. However, many features generated by static analyses can be easily thwarted by obfuscation techniques. Therefore, several researchers have addressed this obfuscation problem with obfuscation-invariant features. However, to the best of our knowledge, no researcher has utilized deobfuscation techniques. To this end, we adopt a code deobfuscation technique with an android malware detection system and investigate its effects. Experimental results indicate that code deobfuscation can successfully retrieve useful information concealed by obfuscation. Further, we propose interaction terms based on identified feature interactions. The proposed interaction terms aim to eliminate the interference caused by the size of the application and other features because many feature values are correlated to the size of the application. In addition, the experimental results indicate that these interaction terms have a high ranking in terms of feature importance values. Our proposed android malware detection model achieves 99.55% accuracy and a 94.61% F1-score with the well-known Drebin dataset, which is better than the performance of previous works.

关键词： Codes malware Feature extraction Static analysis Smart phones Layout Reverse engineering android malware detection classification code deobfuscation feature interaction machine learning static analysis structural feature

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TSDroid: A Novel android malware detection Framework Based on Temporal & Spatial Metrics in IoMT

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ACM TRANSACTIONS ON SENSOR NETWORKS 2023年第3期19卷 51-51页

作者： Zhang, Gaofeng Li, Yu Bao, Xudan Chakarborty, Chinmay Rodrigues, Joel J. P. C. Zheng, Liping Zhang, Xuyun Qi, Lianyong Khosravi, Mohammad R. Hefei Univ Technol Hefei Peoples R China Hefei Univ Technol Intelligent Interconnected Syst Lab Anhui Prov Hefei Peoples R China Birla Inst Technol Dept Elect Commun Engn Mesra 835215 Jharkhand India Univ Fed Piaui Teresina PI Brazil China Univ Petr East China Coll Comp Sci & Technol Qingdao Peoples R China Inst Telecomunicacoes Covilha Portugal Macquarie Univ Sydney Australia Qufu Normal Univ Qufu Peoples R China Persian Gulf Univ Bushehr Iran Shiraz Univ Technol Shiraz Iran

In the era of smart healthcare tremendous growth, plenty of smart devices facilitate cognitive computing for the purposes of lower cost, smarter diagnostic, etc. android system has been widely used in the field of IoMT, and as the main operating system. However, android malware is becoming one major security concern for healthcare, by the serious threat for our medical software assets, like the leakage of private information, the abusing of critical operations, etc. Unfortunately, the existing methods focus on building sustainable classification models, without fully considering system API which is the key to model aging. Compared to the traditional methods, we apply the lifeCycle of API as temporal metric. In addition to the temporal view, the "sizes" of the APPs are utilized as spatial metric in the spatial view. Based on this, we firstly discuss the temporal and spatial metrics together in terms of clustering, and then propose our novel framework-TSDroid. In this framework, we use TS-based clustering algorithm to obtain clustering subsets to enhance the detection

关键词： Internet of Medical Things android malware detection Temporal & Spatial metrics clustering

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android malware analysis and detection: A systematic review

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EXPERT SYSTEMS 2025年第1期42卷

作者： Dahiya, Anuradha Singh, Sukhdip Shrivastava, Gulshan Deenbandhu Chhotu Ram Univ Sci & Technol Dept CSE Murthal India Galgotias Univ Dept CSE Greater Noida India

android malware has been emerged as a significant threat, which includes exposure of confidential information, misrepresentation of facts and execution of applications without the knowledge of the users. malware analysis plays an essential role in dealing with the unlawful behaviour of such malicious applications. android malware analysis involves examining and understanding malware behaviour and its characteristics. It also includes potential adversarial impacts on android devices. This paper presents a quick understanding and a holistic view of malware detection and analysis. The current investigation conducted a systematic literature review (SLR) to recognize the salient shifts in malware detection by examining a range of scholarly journals and conference papers. The SLR investigated 99 articles published between the years 2018 and 2023. The key observation of this SLR is that static analysis is the most implemented approach for detecting android malware;Apktool and Androguard are the most frequently used tools. This study also conceded that deep learning and machine learning models have more potential to analyse the malicious behaviour of malware. Certain challenges are faced in android malware analysis, that is, obfuscation techniques, dynamic code loading, and issues related to experimented datasets. Further, this study focuses on the following areas: the definition of the sample set, data optimisation and processing, feature extraction, machine learning application, and classifier validation. This investigation differs from previous analyses of android malware detection by emphasizing additional methods based on machine learning.

关键词： android malware analysis android malware detection application risk and trust artificial intelligence malicious behaviour mobile device security user privacy

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android malware detection using Convolutional Deep Neural Networks 4

Android Malware Detection using Convolutional Deep Neural Ne...

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4th International Conference on Advanced Aspects of Software Engineering (ICAASE)

作者： Bourebaa, Fatima Benmohammed, Mohamed Univ Constantine 2 Abdelhamid Mehri Lire Lab Constantine Algeria

ISBN: (纸本)9781665422314

Deep learning in general and convolutional architectures, in particular, have pushed the limits of the current state of the art in the field of computer vision and the processing of natural languages and speech. Recently, these techniques have been applied to detect mobile malware and have once again shown their ability to remedy this type of problem. However, the most suitable deep network architecture for malware detection remains an open issue. In this paper, we investigate the possibilities of convolutional neural networks for efficient detection of mobile malware. Specifically, we address the impact of using inception based and multichannel architectures on network performance. We achieve an accuracy of 92% using a multichannel model on a set of 50000 malware and 50000 benign applications.

关键词： Deep learning Convolutional neural networks Mobile security android malware detection

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android malware detection based on multi-category and tag level ratio 5

Android malware detection based on multi-category and tag le...

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IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Yang, Zongjing Long, Zhaohua Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing 400000 Peoples R China

ISBN: (纸本)9781728143231;9781728143224

The arrival of the mobile platforms has brought much convenience to our life, people start to get used to social events, online shopping, and business activities with mobile phones, especially those with the android system. Since the open source of android system, it has rapidly penetrated into various internet ecosystems, a large number of android applications are released to the market every day. However, more and more malicious applications are being developed to steal user privacy and property. In this paper, firstly, based on the androidManifest file structure, a tag level ratio feature is proposed for detecting android malicious applications. Secondly, a new hybrid feature selection strategy is used to preprocessing permission and API features. Finally, through single model and ensemble model, 1100 malicious applications and 1567 nonmalicious applications were detected. The result show that our method has a good accuracy.

关键词： Tag level ratio Multi-category Stacking model android malware detection

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Structural Attack against Graph Based android malware detection 21

Structural Attack against Graph Based Android Malware Detect...

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ACM SIGSAC Conference on Computer and Communications Security (ACM CCS)

作者： Zhao, Kaifa Zhou, Hao Zhu, Yulin Zhan, Xian Zhou, Kai Li, Jianfeng Yu, Le Yuan, Wei Luo, Xiapu Hong Kong Polytech Univ Hong Kong Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9781450384544

malware detection techniques achieve great success with deeper insight into the semantics of malware. Among existing detection techniques, function call graph (FCG) based methods achieve promising performance due to their prominent representations of malware's functionalities. Meanwhile, recent adversarial attacks not only perturb feature vectors to deceive classifiers (i.e., feature-space attacks) but also investigate how to generate real evasive malware (i.e., problem-space attacks). However, existing problem-space attacks are limited due to their inconsistent transformations between feature space and problem space. In this paper, we propose the first structural attack against graph-based android malware detection techniques, which addresses the inverse-transformation problem [1] between feature-space attacks and problem-space attacks. We design a Heuristic optimization model integrated with Reinforcement learning framework to optimize our structural ATtack (HRAT). HRAT includes four types of graph modifications (i.e., inserting and deleting nodes, adding edges and rewiring) that correspond to four manipulations on apps (i.e., inserting and deleting methods, adding call relation, rewiring). Through extensive experiments on over 30k android apps, HRAT demonstrates outstanding attack performance on both feature space (over 90% attack success rate) and problem space (up to 100% attack success rate in most cases). Besides, the experiment results show that combing multiple attack behaviors strategically makes the attack more effective and efficient.

关键词： Structural attack android malware detection Function call graph

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Robust android malware detection against Adversarial Example Attacks 21

Robust Android Malware Detection against Adversarial Example...

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30th World Wide Web Conference (WWW)

作者： Li, Heng Zhou, Shiyao Yuan, Wei Luo, Xiapu Gao, Cuiying Chen, Shuiyan Huazhong Univ Sci & Technol Wuhan Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9781450383127

Adversarial examples pose severe threats to android malware detection because they can render the machine learning based detection systems useless. How to effectively detect android malware under various adversarial example attacks becomes an essential but very challenging issue. Existing adversarial example defense mechanisms usually rely heavily on the instances or the knowledge of adversarial examples, and thus their usability and effectiveness are significantly limited because they often cannot resist the unseen-type adversarial examples. In this paper, we propose a novel robust android malware detection approach that can resist adversarial examples without requiring their instances or knowledge by jointly investigating malware detection and adversarial example defenses. More precisely, our approach employs a new VAE (variational autoencoder) and an MLP (multi-layer perceptron) to detect malware, and combines their detection outcomes to make the final decision. In particular, we share a feature extraction network between the VAE and the MLP to reduce model complexity and design a new loss function to disentangle the features of different classes, hence improving detection performance. Extensive experiments confirm our model's advantage in accuracy and robustness. Our method outperforms 11 state-of-the-art robust android malware detection models when resisting 7 kinds of adversarial example attacks.

关键词： android malware detection Adversarial Example Mobile Security

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TL-GNN: android malware detection Using Transfer Learning

Applied AI Letters

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Applied AI Letters 2024年第3期5卷

作者： Raza, Ali Qaisar, Zahid Hussain Aslam, Naeem Faheem, Muhammad Ashraf, Muhammad Waqar Chaudhry, Muhammad Naman Department of Computer Science NFC Institute of Engineering and Technology Multan Pakistan Department of Computing and Emerging Technologies Emerson University Multan Pakistan Department of Computing Science School of Technology and Innovations University of Vaasa Vaasa Finland Department of Computer Engineering Bahauddin Zakariya University Multan Pakistan

malware growth has accelerated due to the widespread use of android applications. android smartphone attacks have increased due to the widespread use of these devices. While deep learning models offer high efficiency and accuracy, training them on large and complex datasets is computationally expensive. Hence, a method that effectively detects new malware variants at a low computational cost is required. A transfer learning method to detect android malware is proposed in this research. Because of transferring known features from a source model that has been trained to a target model, the transfer learning approach reduces the need for new training data and minimizes the need for huge amounts of computational power. We performed many experiments on 1.2 million android application samples for performance evaluation. In addition, we evaluated how well our framework performed in comparison with traditional deep learning and standard machine learning models. In comparison with state-of-the-art android malware detection methods, the proposed framework offers improved classification accuracy of 98.87%, a precision of 99.55%, recall of 97.30%, F1-measure of 99.42%, and a quicker detection rate of 5.14 ms using the transfer learning strategy. © 2024 The Authors. Applied AI Letters published by John Wiley & Sons Ltd.

关键词： android malware detection deep learning graph neural network malware classifier transfer learning

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You are what the permissions told me! android malware detection based onhybrid tactics

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JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2022年 66卷

作者： Wang, Huanran Zhang, Weizhe He, Hui Harbin Inst Technol Sch Cyberspace Sci Harbin Peoples R China Peng Cheng Lab Cyberspace Secur Res Ctr Shenzhen Peoples R China

Recent years have witnessed a significant increase in the use of android devices in many aspects of our ***, users can download android apps from third-party channels, which provides numerous opportunitiesfor malware. Attackers utilize unsolicited permissions to gain access to the sensitive private intelligenceof users. Since signature-based antivirus solutions no longer meet practical needs, efficient and adaptablesolutions are desperately needed, especially in new variants. As a remedy, we propose a hybrid androidmalware detection approach that combines dynamic and static tactics. We firstly adopt static analysis inferringdifferent permission usage patterns between malware and benign apps based on the machine-learning-basedmethod. To classify the suspicious apps further, we extract the object reference relationships from the memoryheap to construct a dynamic feature base. We then present an improved state-based algorithm based onDAMBA. Experimental results on a real-world dataset of 21,708 apps show that our approach outperformsthe well-known detector with 97.5% F1-measure. Besides, our system is demonstrated to resist permissionabuse behaviors and obfuscation techniques

关键词： android malware detection Deep learning Permission sequence

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