survey

Deep Learning for Android Malware Defenses: A Systematic Literature Review

Authors:

Chakkrit Tantithamthavorn,

Yepang LiuAuthors Info & Claims

ACM Computing Surveys, Volume 55, Issue 8

Article No.: 153, Pages 1 - 36

https://doi.org/10.1145/3544968

Published: 23 December 2022 Publication History

Abstract

Malicious applications (particularly those targeting the Android platform) pose a serious threat to developers and end-users. Numerous research efforts have been devoted to developing effective approaches to defend against Android malware. 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 rules or traditional machine learning may not be effective. In recent years, a dominant research field called deep learning (DL), which provides a powerful feature abstraction ability, has demonstrated a compelling and promising performance in a variety of areas, like natural language processing and computer vision. To this end, employing DL techniques to thwart Android malware attacks has recently garnered considerable research attention. Yet, no systematic literature review focusing on DL approaches for Android malware defenses exists. In this article, we conducted a systematic literature review to search and analyze how DL approaches have been applied in the context of malware defenses in the Android environment. As a result, a total of 132 studies covering the period 2014–2021 were identified. Our investigation reveals that, while the majority of these sources mainly consider DL-based Android malware detection, 53 primary studies (40.1%) design defense approaches based on other scenarios. This review also discusses research trends, research focuses, challenges, and future research directions in DL-based Android malware defenses.

References

[1]

Abada Abderrahmane, Guettaf Adnane, Challal Yacine, and Garri Khireddine. 2019. Android malware detection based on system calls analysis and CNN classification. In 2019 IEEE Wireless Communications and Networking Conference Workshop (WCNCW’19). IEEE, 1–6.

[2]

Naveed Akhtar and Ajmal Mian. 2018. Threat of adversarial attacks on deep learning in computer vision: A survey. IEEE Access 6 (2018), 14410–14430.

[3]

Ebtesam J. Alqahtani, Rachid Zagrouba, and Abdullah Almuhaideb. 2019. A survey on Android malware detection techniques using machine learning algorithms. In 2019 6th International Conference on Software Defined Systems (SDS’19). IEEE, 110–117.

[4]

Hani Alshahrani, Harrison Mansourt, Seaver Thorn, Ali Alshehri, Abdulrahman Alzahrani, and Huirong Fu. 2018. DDefender: Android application threat detection using static and dynamic analysis. In 2018 IEEE International Conference on Consumer Electronics (ICCE’18). IEEE, 1–6.

[5]

Mohammed K. Alzaylaee, Suleiman Y. Yerima, and Sakir Sezer. 2017. Emulator vs real phone: Android malware detection using machine learning. In Proceedings of the 3rd ACM on International Workshop on Security and Privacy Analytics. 65–72.

Digital Library

[6]

Mohammed K. Alzaylaee, Suleiman Y. Yerima, and Sakir Sezer. 2020. DL-Droid: Deep learning based android malware detection using real devices. Computers & Security 89 (2020), 101663.

Digital Library

[7]

Muhammad Amin, Babar Shah, Aizaz Sharif, Tamleek Ali, Ki-lL Kim, and Sajid Anwar. 2019. Android malware detection through generative adversarial networks. Transactions on Emerging Telecommunications Technologies (2019), e3675.

[8]

Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Qiang Cheng, Guoliang Chen, et al. 2016. Deep speech 2: End-to-end speech recognition in English and Mandarin. In International Conference on Machine Learning. 173–182.

Digital Library

[9]

A. Ananya, A. Aswathy, T. R. Amal, P. G. Swathy, P. Vinod, and Shojafar Mohammad. 2020. SysDroid: A dynamic ML-based android malware analyzer using system call traces. Cluster Computing (2020), 1–20.

[10]

AndroZoo 2020. AndroZoo. Retrieved October 11, 2020 from https://androzoo.uni.lu/.

[11]

Apktool 2010. APKTOOL. Retrieved October 25, 2021 from https://ibotpeaches.github.io/Apktool/.

[12]

Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, and Konrad Rieck. 2022. Dos and Don’ts of Machine Learning in Computer Security. In 31st USENIX Security Symposium (USENIX Security’22). USENIX Association, Boston, MA.

[13]

Daniel Arp, Michael Spreitzenbarth, Malte Hubner, Hugo Gascon, Konrad Rieck, and CERT Siemens. 2014. DREBIN: Effective and explainable detection of android malware in your pocket. In NDSS, Vol. 14. 23–26.

[14]

Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath. 2017. Deep reinforcement learning: A brief survey. IEEE Signal Processing Magazine 34, 6 (2017), 26–38.

[15]

Alessandro Bacci, Alberto Bartoli, Fabio Martinelli, Eric Medvet, and Francesco Mercaldo. 2018. Detection of obfuscation techniques in Android applications. In Proceedings of the 13th International Conference on Availability, Reliability and Security. 1–9.

Digital Library

[16]

Yude Bai, Zhenchang Xing, Xiaohong Li, Zhiyong Feng, and Duoyuan Ma. 2020. Unsuccessful story about few shot malware family classification and siamese network to the rescue. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering. 1560–1571.

Digital Library

[17]

Yude Bai, Zhenchang Xing, Duoyuan Ma, Xiaohong Li, and Zhiyong Feng. 2021. Comparative analysis of feature representations and machine learning methods in Android family classification. Computer Networks 184 (2021), 107639.

[18]

Nazanin Bakhshinejad and Ali Hamzeh. 2019. Parallel-CNN network for malware detection. IET Information Security 14, 2 (2019), 210–219.

[19]

Khaled Bakour and Halil Murat Ünver. 2020. VisDroid: Android malware classification based on local and global image features, bag of visual words and machine learning techniques. Neural Computing and Applications (2020), 1–21.

[20]

Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 8 (2013), 1798–1828.

Digital Library

[21]

BlackHat 2011. Androguard. Retrieved October 25, 2021 from https://code.google.com/archive/p/androguard.

[22]

Marcus Botacin, Fabricio Ceschin, Ruimin Sun, Daniela Oliveira, and André Grégio. 2021. Challenges and pitfalls in malware research. Computers & Security 106 (2021), 102287.

Digital Library

[23]

Dewan Chaulagain, Prabesh Poudel, Prabesh Pathak, Sankardas Roy, Doina Caragea, Guojun Liu, and Xinming Ou. 2020. Hybrid analysis of android apps for security vetting using deep learning. In 2020 IEEE Conference on Communications and Network Security (CNS’20). IEEE, 1–9.

[24]

Simin Chen, Soroush Bateni, Sampath Grandhi, Xiaodi Li, Cong Liu, and Wei Yang. 2020. DENAS: Automated rule generation by knowledge extraction from neural networks. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 813–825.

Digital Library

[25]

Xiao Chen, Chaoran Li, Derui Wang, Sheng Wen, Jun Zhang, Surya Nepal, Yang Xiang, and Kui Ren. 2019. Android HIV: A study of repackaging malware for evading machine-learning detection. IEEE Transactions on Information Forensics and Security 15 (2019), 987–1001.

Digital Library

[26]

Gianni D’Angelo, Massimo Ficco, and Francesco Palmieri. 2020. Malware detection in mobile environments based on Autoencoders and API-images. Journal of Parallel and Distributed Computing 137 (2020), 26–33.

Digital Library

[27]

Nadia Daoudi, Kevin Allix, Tegawendé F. Bissyandé, and Jacques Klein. 2021. Lessons learnt on reproducibility in machine learning based android malware detection. Empirical Software Engineering 26, 4 (2021), 1–53.

Digital Library

[28]

Nadia Daoudi, Jordan Samhi, Abdoul Kader Kabore, Kevin Allix, Tegawendé F. Bissyandé, and Jacques Klein. 2021. DexRay: A simple, yet effective deep learning approach to android malware detection based on image representation of bytecode. In International Workshop on Deployable Machine Learning for Security Defense. Springer, 81–106.

[29]

Asim Darwaish, Farid Naït-Abdesselam, Chafiq Titouna, and Sumera Sattar. 2021. Robustness of image-based android malware detection under adversarial attacks. In IEEE International Conference on Communications (ICC’21). IEEE, 1–6.

[30]

Andrea De Lorenzo, Fabio Martinelli, Eric Medvet, Francesco Mercaldo, and Antonella Santone. 2020. Visualizing the outcome of dynamic analysis of Android malware with VizMal. Journal of Information Security and Applications 50 (2020), 102423.

Digital Library

[31]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805.

[32]

Yuxin Ding, Xiao Zhang, Jieke Hu, and Wenting Xu. 2020. Android malware detection method based on bytecode image. Journal of Ambient Intelligence and Humanized Computing (2020), 1–10.

[33]

Feng Dong, Haoyu Wang, Li Li, Yao Guo, Tegawendé F. Bissyandé, Tianming Liu, Guoai Xu, and Jacques Klein. 2018. FraudDroid: Automated Ad fraud detection for android apps. In The 26th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE’18).

Digital Library

[34]

Jeffrey L. Elman. 1990. Finding structure in time. Cognitive Science 14, 2 (1990), 179–211.

[35]

William Enck, Peter Gilbert, Seungyeop Han, Vasant Tendulkar, Byung-Gon Chun, Landon P. Cox, Jaeyeon Jung, Patrick McDaniel, and Anmol N. Sheth. 2014. Taintdroid: An information-flow tracking system for realtime privacy monitoring on smartphones. ACM Transactions on Computer Systems (TOCS) 32, 2 (2014), 1–29.

Digital Library

[36]

Ming Fan, Wenying Wei, Xiaofei Xie, Yang Liu, Xiaohong Guan, and Ting Liu. 2020. Can we trust your explanations? Sanity checks for interpreters in Android malware analysis. IEEE Transactions on Information Forensics and Security 16 (2020), 838–853.

[37]

Yujie Fan, Mingxuan Ju, Shifu Hou, Yanfang Ye, Wenqiang Wan, Kui Wang, Yinming Mei, and Qi Xiong. 2021. Heterogeneous temporal graph transformer: An intelligent system for evolving Android malware detection. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2831–2839.

Digital Library

[38]

Parvez Faruki, Ammar Bharmal, Vijay Laxmi, Vijay Ganmoor, Manoj Singh Gaur, Mauro Conti, and Muttukrishnan Rajarajan. 2014. Android security: A survey of issues, malware penetration, and defenses. IEEE Communications Surveys & Tutorials 17, 2 (2014), 998–1022.

Digital Library

[39]

Johannes Feichtner and Stefan Gruber. 2020. Understanding privacy awareness in android app descriptions using deep learning. In Proceedings of the 10th ACM Conference on Data and Application Security and Privacy. 203–214.

Digital Library

[40]

Ruitao Feng, Sen Chen, Xiaofei Xie, Lei Ma, Guozhu Meng, Yang Liu, and Shang-Wei Lin. 2019. Mobidroid: A performance-sensitive malware detection system on mobile platform. In 2019 24th International Conference on Engineering of Complex Computer Systems (ICECCS’19). IEEE, 61–70.

[41]

Ruitao Feng, Sen Chen, Xiaofei Xie, Guozhu Meng, Shang-Wei Lin, and Yang Liu. 2020. A performance-sensitive malware detection system using deep learning on mobile devices. IEEE Transactions on Information Forensics and Security 16 (2020), 1563–1578.

[42]

Yinglan Feng, Liang Chen, Angyu Zheng, Cuiyun Gao, and Zibin Zheng. 2019. AC-Net: Assessing the consistency of description and permission in Android apps. IEEE Access 7 (2019), 57829–57842.

[43]

Hossein Fereidooni, Mauro Conti, Danfeng Yao, and Alessandro Sperduti. 2016. ANASTASIA: Android malware detection using static analysis of applications. In 2016 8th IFIP International Conference on New tTechnologies, Mobility and Security (NTMS’16). IEEE, 1–5.

[44]

Massimo Ficco. 2021. Malware analysis by combining multiple detectors and observation windows. IEEE Trans. Comput. 71, 6 (2021), 1276–1290.

[45]

Vincent François-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare, and Joelle Pineau. 2018. An introduction to deep reinforcement learning. arXiv:1811.12560.

[46]

Rafa Gálvez, Veelasha Moonsamy, and Claudia Diaz. 2021. Less is more: A privacy-respecting Android malware classifier using federated learning. Proceedings on Privacy Enhancing Technologies 1 (2021), 20.

[47]

Ekta Gandotra, Divya Bansal, and Sanjeev Sofat. 2014. Malware analysis and classification: A survey. Journal of Information Security 5 (2014), 56–64.

[48]

Han Gao, Shaoyin Cheng, and Weiming Zhang. 2021. GDroid: Android malware detection and classification with graph convolutional network. Computers & Security 106 (2021), 102264.

Digital Library

[49]

Amirhossein Gharib and Ali Ghorbani. 2017. DNA-Droid: A real-time android ransomware detection framework. In International Conference on Network and System Security. Springer, 184–198.

[50]

Liangyi Gong, Zhenhua Li, Feng Qian, Zifan Zhang, Qi Alfred Chen, Zhiyun Qian, Hao Lin, and Yunhao Liu. 2020. Experiences of landing machine learning onto market-scale mobile malware detection. In Proceedings of the 15th European Conference on Computer Systems. 1–14.

Digital Library

[51]

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press.

Digital Library

[52]

Google Play Protect 2020. Google Play Protect. Retrieved September 9, 2020 from https://www.android.com/play-protect/.

[53]

Petr Gronát, Javier Alejandro Aldana-Iuit, and Martin Bálek. 2019. MaxNet: Neural network architecture for continuous detection of malicious activity. In 2019 IEEE Security and Privacy Workshops (SPW’19). IEEE, 28–35.

[54]

Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, and Patrick McDaniel. 2017. Adversarial examples for malware detection. In European Symposium on Research in Computer Security. Springer, 62–79.

[55]

Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 855–864.

Digital Library

[56]

Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Dino Pedreschi, Franco Turini, and Fosca Giannotti. 2018. Local rule-based explanations of black box decision systems. arXiv:1805.10820.

[57]

Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. 2018. A survey of methods for explaining black box models. ACM Computing Surveys (CSUR) 51, 5 (2018), 1–42.

Digital Library

[58]

Wenbo Guo, Dongliang Mu, Jun Xu, Purui Su, Gang Wang, and Xinyu Xing. 2018. Lemna: Explaining deep learning based security applications. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. 364–379.

Digital Library

[59]

Ke He and Dong-Seong Kim. 2019. Malware detection with malware images using deep learning techniques. In 2019 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE’19). IEEE, 95–102.

[60]

Geoffrey Hinton, Li Deng, Dong Yu, George E. Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N. Sainath, et al. 2012. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine 29, 6 (2012), 82–97.

[61]

Geoffrey E. Hinton. 2009. Deep belief networks. Scholarpedia 4, 5 (2009), 5947.

[62]

Kurt Hornik, Maxwell Stinchcombe, and Halbert White. 1989. Multilayer feedforward networks are universal approximators. Neural Networks 2, 5 (1989), 359–366.

[63]

Shifu Hou, Aaron Saas, Lifei Chen, and Yanfang Ye. 2016. Deep4maldroid: A deep learning framework for android malware detection based on Linux kernel system call graphs. In 2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops (WIW’16). IEEE, 104–111.

[64]

Shifu Hou, Aaron Saas, Lingwei Chen, Yanfang Ye, and Thirimachos Bourlai. 2017. Deep neural networks for automatic android malware detection. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. 803–810.

Digital Library

[65]

Shifu Hou, Aaron Saas, Yanfang Ye, and Lifei Chen. 2016. Droiddelver: An Android malware detection system using deep belief network based on API call blocks. In International Conference on Web-Age Information Management. Springer, 54–66.

[66]

TonTon Hsien-De Huang and Hung-Yu Kao. 2018. R2-D2: ColoR-inspired convolutional neuRal network (CNN)-based android malware detections. In 2018 IEEE International Conference on Big Data (Big Data’18). IEEE, 2633–2642.

[67]

Na Huang, Ming Xu, Ning Zheng, Tong Qiao, and Kim-Kwang Raymond Choo. 2019. Deep Android malware classification with API-based feature graph. In 2019 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE’19). IEEE, 296–303.

[68]

Shinelle Hutchinson, Bing Zhou, and Umit Karabiyik. 2019. Are we really protected? An investigation into the play protect service. In 2019 IEEE International Conference on Big Data (Big Data’19). IEEE, 4997–5004.

[69]

Giacomo Iadarola, Fabio Martinelli, Francesco Mercaldo, and Antonella Santone. 2021. Towards an interpretable deep learning model for mobile malware detection and family identification. Computers & Security 105 (2021), 102198.

[70]

Amir Namavar Jahromi, Sattar Hashemi, Ali Dehghantanha, Reza M. Parizi, and Kim-Kwang Raymond Choo. 2020. An enhanced stacked LSTM method with no random initialization for malware threat hunting in safety and time-critical systems. IEEE Transactions on Emerging Topics in Computational Intelligence 4, 5 (2020), 630–640.

[71]

Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Hoa Khanh Dam, and John Grundy. 2022. An Empirical Study of Model-Agnostics Techniques for Defect Prediction Models. IEEE Transactions on Software Engineering (TSE) 48, 1 (2022), 166–185.

Digital Library

[72]

ElMouatez Billah Karbab and Mourad Debbabi. 2021. PetaDroid: Adaptive android malware detection using deep learning. In International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment. Springer, 319–340.

Digital Library

[73]

ElMouatez Billah Karbab, Mourad Debbabi, Abdelouahid Derhab, and Djedjiga Mouheb. 2018. MalDozer: Automatic framework for android malware detection using deep learning. Digital Investigation 24 (2018), S48–S59.

[74]

Naveen Karunanayake, Jathushan Rajasegaran, Ashanie Gunathillake, Suranga Seneviratne, and Guillaume Jourjon. 2020. A multi-modal neural embeddings approach for detecting mobile counterfeit Apps: A case study on Google Play store. IEEE Transactions on Mobile Computing (2020), 1–1.

[75]

Mahbub Khoda, Tasadduq Imam, Joarder Kamruzzaman, Iqbal Gondal, and Ashfaqur Rahman. 2019. Selective adversarial learning for mobile malware. In 2019 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE’19). IEEE, 272–279.

[76]

M. E. Khoda, J. Kamruzzaman, I. Gondal, T. Imam, and A. Rahman. 2019. Mobile malware detection: An analysis of deep learning model. In 2019 IEEE International Conference on Industrial Technology (ICIT’19). 1161–1166.

[77]

TaeGuen Kim, BooJoong Kang, Mina Rho, Sakir Sezer, and Eul Gyu Im. 2018. A multimodal deep learning method for android malware detection using various features. IEEE Transactions on Information Forensics and Security 14, 3 (2018), 773–788.

[78]

Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv:1609.02907.

[79]

Barbara Kitchenham. 2004. Procedures for performing systematic reviews. Keele, UK, Keele University 33, 2004 (2004), 1–26.

[80]

Barbara Kitchenham and Stuart Charters. 2007. Guidelines for Performing Systematic Literature Reviews in Software Engineering. EBSE Technical Report EBSE-2007-01.

[81]

Rahul Krishna and Tim Menzies. 2020. Learning actionable analytics from multiple software projects. Empirical Software Engineering (EMSE) 25, 5 (2020), 3468–3500.

Digital Library

[82]

Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2016. Adversarial machine learning at scale. arXiv:1611.01236.

[83]

Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436–444.

[84]

William Younghoo Lee, Joshua Saxe, and Richard Harang. 2019. SeqDroid: Obfuscated Android malware detection using stacked convolutional and recurrent neural networks. In Deep Learning Applications for Cyber Security. Springer, 197–210.

[85]

Tao Lei, Zhan Qin, Zhibo Wang, Qi Li, and Dengpan Ye. 2019. EveDroid: Event-aware Android malware detection against model degrading for IoT devices. IEEE Internet of Things Journal 6, 4 (2019), 6668–6680.

[86]

Chaoran Li, Xiao Chen, Derui Wang, Sheng Wen, Muhammad Ejaz Ahmed, Seyit Camtepe, and Yang Xiang. 2021. Backdoor Attack on Machine Learning Based Android Malware Detectors. IEEE Transactions on Dependable and Secure Computing 19 (2021), 1–1.

[87]

Deqiang Li and Qianmu Li. 2020. Adversarial deep ensemble: Evasion attacks and defenses for malware detection. IEEE Transactions on Information Forensics and Security 15 (2020), 3886–3900.

Digital Library

[88]

Deqiang Li, Qianmu Li, Yanfang Ye, and Shouhuai Xu. 2021. A framework for enhancing deep neural networks against adversarial malware. IEEE Transactions on Network Science and Engineering 8, 1 (2021), 736–750.

[89]

Deqiang Li, Tian Qiu, Shuo Chen, Qianmu Li, and Shouhuai Xu. 2021. Can we leverage predictive uncertainty to detect dataset shift and adversarial examples in Android malware detection? In Annual Computer Security Applications Conference.

Digital Library

[90]

Dan Li, Lichao Zhao, Qingfeng Cheng, Ning Lu, and Wenbo Shi. 2020. Opcode sequence analysis of Android malware by a convolutional neural network. Concurrency and Computation: Practice and Experience 32, 18 (2020), e5308.

[91]

Heng Li, ShiYao Zhou, Wei Yuan, Jiahuan Li, and Henry Leung. 2019. Adversarial-example attacks toward android malware detection system. IEEE Systems Journal 14, 1 (2019), 653–656.

[92]

Heng Li, Shiyao Zhou, Wei Yuan, Xiapu Luo, Cuiying Gao, and Shuiyan Chen. 2021. Robust Android malware detection against adversarial example attacks. In Proceedings of the Web Conference 2021. 3603–3612.

Digital Library

[93]

Li Li, Alexandre Bartel, Tegawendé F. Bissyandé, Jacques Klein, Yves Le Traon, Steven Arzt, Siegfried Rasthofer, Eric Bodden, Damien Octeau, and Patrick Mcdaniel. 2015. IccTA: Detecting inter-component privacy leaks in Android Apps. In Proceedings of the 37th International Conference on Software Engineering (ICSE’15).

[94]

Li Li, Tegawendé F. Bissyandé, and Jacques Klein. 2019. Rebooting research on detecting repackaged android apps: Literature review and benchmark. IEEE Transactions on Software Engineering 47, 4 (2019), 676–693.

[95]

Li Li, Tegawendé F. Bissyandé, Mike Papadakis, Siegfried Rasthofer, Alexandre Bartel, Damien Octeau, Jacques Klein, and Le Traon. 2017. Static analysis of android apps: A systematic literature review. Information and Software Technology 88 (2017), 67–95.

Digital Library

[96]

Li Li, Daoyuan Li, Tegawendé F. Bissyandé, Jacques Klein, Yves Le Traon, David Lo, and Lorenzo Cavallaro. 2017. Understanding android app piggybacking: A systematic study of malicious code grafting. IEEE Transactions on Information Forensics and Security 12, 6 (2017), 1269–1284.

Digital Library

[97]

Li Li, Timothée Riom, Tegawendé F. Bissyandé, Haoyu Wang, Jacques Klein, et al. 2019. Revisiting the impact of common libraries for android-related investigations. Journal of Systems and Software 154 (2019), 157–175.

Digital Library

[98]

Yang Li and Tao Yang. 2018. Word embedding for understanding natural language: A survey. In Guide to Big Data Applications. Springer, 83–104.

[99]

Kaijun Liu, Shengwei Xu, Guoai Xu, Miao Zhang, Dawei Sun, and Haifeng Liu. 2020. A review of Android malware detection approaches based on machine learning. IEEE Access 8 (2020), 124579–124607.

[100]

Tianming Liu, Haoyu Wang, Li Li, Xiapu Luo, Feng Dong, Yao Guo, Liu Wang, Tegawendé F. Bissyandé, and Jacques Klein. 2020. MadDroid: Characterising and detecting devious Ad content for Android Apps. In The Web Conference 2020 (WWW’20).

Digital Library

[101]

Tianliang Lu, Yanhui Du, Li Ouyang, Qiuyu Chen, and Xirui Wang. 2020. Android Malware Detection Based on a Hybrid Deep Learning Model. Security and Communication Networks 2020 (2020), 8863617.

Digital Library

[102]

Scott M. Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems. 4765–4774.

[103]

Haoyu Ma, Jianwen Tian, Kefan Qiu, David Lo, Debin Gao, Daoyuan Wu, Chunfu Jia, and Thar Baker. 2020. Deep-learning–based app sensitive behavior surveillance for Android powered cyber–physical systems. IEEE Transactions on Industrial Informatics 17, 8 (2020), 5840–5850.

[104]

E. Mariconti, L. Onwuzurike, P. Andriotis, E. De Cristofaro, G. Ross, and G. Stringhini. 2017. MamaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models. In 24th Annual Network and Distributed System Security Symposium, NDSS 2017, San Diego, California, USA, February 26 - March 1, 2017. The Internet Society.

[105]

Alejandro Martín, Félix Fuentes-Hurtado, Valery Naranjo, and David Camacho. 2017. Evolving deep neural networks architectures for android malware classification. In 2017 IEEE Congress on Evolutionary Computation (CEC’17). IEEE, 1659–1666.

[106]

Niall McLaughlin, Jesus Martinez del Rincon, BooJoong Kang, Suleiman Yerima, Paul Miller, Sakir Sezer, Yeganeh Safaei, Erik Trickel, Ziming Zhao, Adam Doupé, et al. 2017. Deep android malware detection. In Proceedings of the 7th ACM Conference on Data and Application Security and Privacy. 301–308.

Digital Library

[107]

Francesco Mercaldo and Antonella Santone. 2020. Deep learning for image-based mobile malware detection. Journal of Computer Virology and Hacking Techniques (2020), 1–15.

[108]

Stuart Millar, Niall McLaughlin, Jesus Martinez del Rincon, Paul Miller, and Ziming Zhao. 2020. DANdroid: A multi-view discriminative adversarial network for obfuscated Android malware detection. In Proceedings of the 10th ACM Conference on Data and Application Security and Privacy. 353–364.

Digital Library

[109]

Christoph Molnar. 2020. Interpretable Machine Learning. Lulu.com.

[110]

Abdelmonim Naway and Yuancheng Li. 2018. A review on the use of deep learning in android malware detection. arXiv:1812.10360.

[111]

Robin Nix and Jian Zhang. 2017. Classification of android apps and malware using deep neural networks. In 2017 International Joint Conference on Neural Networks (IJCNN’17). IEEE, 1871–1878.

[112]

Rajvardhan Oak, Min Du, David Yan, Harshvardhan Takawale, and Idan Amit. 2019. Malware detection on highly imbalanced data through sequence modeling. In Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security. 37–48.

Digital Library

[113]

Ori Or-Meir, Nir Nissim, Yuval Elovici, and Lior Rokach. 2019. Dynamic malware analysis in the modern era—A state of the art survey. ACM Computing Surveys (CSUR) 52, 5 (2019), 1–48.

Digital Library

[114]

Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, and Wenwu Zhu. 2016. Asymmetric transitivity preserving graph embedding. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1105–1114.

Digital Library

[115]

Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami. 2016. The limitations of deep learning in adversarial settings. In 2016 IEEE European Symposium on Security and Privacy (EuroS&P’16). IEEE, 372–387.

[116]

Xinjun Pei, Long Yu, Shengwei Tian, Huanhuan Wang, and Yongfang Peng. 2020. Combining multi-features with a neural joint model for Android malware detection 1. Journal of Intelligent & Fuzzy Systems (2020), 1–11 (preprint).

[117]

Abdurrahman Pektaş and Tankut Acarman. 2020. Deep learning for effective Android malware detection using API call graph embeddings. Soft Computing 24, 2 (2020), 1027–1043.

Digital Library

[118]

Abdurrahman Pektaş and Tankut Acarman. 2020. Learning to detect Android malware via opcode sequences. Neurocomputing 396 (2020), 599–608.

[119]

Feargus Pendlebury, Fabio Pierazzi, Roberto Jordaney, Johannes Kinder, and Lorenzo Cavallaro. 2019. \(\lbrace TESSERACT\rbrace\) : Eliminating experimental bias in malware classification across space and time. In 28th USENIX Security Symposium (USENIX Security’19). 729–746.

[120]

Kewen Peng and Tim Menzies. 2020. Defect reduction planning (using TimeLIME). arXiv:2006.07416.

[121]

Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 701–710.

Digital Library

[122]

Fabio Pierazzi, Ghita Mezzour, Qian Han, Michele Colajanni, and V. S. Subrahmanian. 2020. A data-driven characterization of modern Android spyware. ACM Transactions on Management Information Systems (TMIS) 11, 1 (2020), 1–38.

Digital Library

[123]

Robert Podschwadt and Hassan Takabi. 2019. On effectiveness of adversarial examples and defenses for malware classification. In International Conference on Security and Privacy in Communication Systems. Springer, 380–393.

[124]

Chanathip Pornprasit and Chakkrit Tantithamthavorn. 2021. JITLine: A simpler, better, faster, finer-grained just-in-time defect prediction. In Proceedings of the International Conference on Mining Software Repositories (MSR’21). To appear.

[125]

Samira Pouyanfar, Saad Sadiq, Yilin Yan, Haiman Tian, Yudong Tao, Maria Presa Reyes, Mei-Ling Shyu, Shu-Ching Chen, and S. S. Iyengar. 2018. A survey on deep learning: Algorithms, techniques, and applications. ACM Computing Surveys (CSUR) 51, 5 (2018), 1–36.

Digital Library

[126]

Junyang Qiu, Jun Zhang, Wei Luo, Lei Pan, Surya Nepal, Yu Wang, and Yang Xiang. 2019. A3CM: Automatic capability annotation for android malware. IEEE Access 7 (2019), 147156–147168.

[127]

Junyang Qiu, Jun Zhang, Wei Luo, Lei Pan, Surya Nepal, and Yang Xiang. 2020. A survey of Android malware detection with deep neural models. ACM Computing Surveys (CSUR) 53, 6 (2020), 1–36.

Digital Library

[128]

Dilini Rajapaksha, Chakkrit Tantithamthavorn, Christoph Bergmeir, Wray Buntine, Jirayus Jiarpakdee, and John Grundy. 2021. SQAPlanner: Generating data-informed software quality improvement plans.

[129]

Hemant Rathore, Sanjay K. Sahay, Piyush Nikam, and Mohit Sewak. 2021. Robust android malware detection system against adversarial attacks using q-learning. Information Systems Frontiers 23, 4 (2021), 867–882.

Digital Library

[130]

Zhongru Ren, Haomin Wu, Qian Ning, Iftikhar Hussain, and Bingcai Chen. 2020. End-to-end malware detection for android IoT devices using deep learning. Ad Hoc Networks 101 (2020), 102098.

Digital Library

[131]

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. “Why should I trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1135–1144.

Digital Library

[132]

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018. Anchors: High-precision model-agnostic explanations. In AAAI, Vol. 18. 1527–1535.

[133]

Justin Sahs and Latifur Khan. 2012. A machine learning approach to android malware detection. In 2012 European Intelligence and Security Informatics Conference. IEEE, 141–147.

Digital Library

[134]

Jürgen Schmidhuber. 2015. Deep learning in neural networks: An overview. Neural Networks 61 (2015), 85–117. DOI:

Digital Library

[135]

Giorgio Severi, Jim Meyer, Scott Coull, and Alina Oprea. 2021. Explanation-guided backdoor poisoning attacks against malware classifiers. In 30th USENIX Security Symposium (USENIX Security 21).

[136]

Lwin Khin Shar, Biniam Fisseha Demissie, Mariano Ceccato, and Wei Minn. 2020. Experimental comparison of features and classifiers for Android malware detection. (2020).

[137]

Alireza Souri and Rahil Hosseini. 2018. A state-of-the-art survey of malware detection approaches using data mining techniques. Human-Centric Computing and Information Sciences 8, 1 (2018), 3.

Digital Library

[138]

Statista 2020. Mobile Operating Systems’ Market Share Worldwide from January 2012 to July 2020. Retrieved September 9, 2020 from https://www.statista.com/statistics/272698/global-market-share-held-by-mobile-operating-systems-since-2009/.

[139]

Xin Su, Weiqi Shi, Xilong Qu, Yi Zheng, and Xuchong Liu. 2020. DroidDeep: Using deep belief network to characterize and detect Android malware. Soft Computing (2020), 1–14.

[140]

Xin Su, Dafang Zhang, Wenjia Li, and Kai Zhao. 2016. A deep learning approach to Android malware feature learning and detection. In 2016 IEEE Trustcom/BigDataSE/ISPA. IEEE, 244–251.

[141]

Yuxia Sun, Yanjia Chen, Yuchang Pan, and Lingyu Wu. 2019. Android malware family classification based on deep learning of code images. IAENG International Journal of Computer Science 46, 4 (2019).

[142]

Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1–9.

[143]

Rahim Taheri, Reza Javidan, and Zahra Pooranian. 2020. Adversarial Android malware detection for mobile multimedia applications in IoT environments. Multimedia Tools and Applications (2020), 1–17.

[144]

Rahim Taheri, Reza Javidan, Mohammad Shojafar, Zahra Pooranian, Ali Miri, and Mauro Conti. 2020. On defending against label flipping attacks on malware detection systems. Neural Computing and Applications (2020), 1–20.

[145]

Kimberly Tam, Ali Feizollah, Nor Badrul Anuar, Rosli Salleh, and Lorenzo Cavallaro. 2017. The evolution of android malware and Android analysis techniques. ACM Computing Surveys (CSUR) 49, 4 (2017), 1–41.

Digital Library

[146]

Kimberly Tam, Salahuddin J. Khan, Aristide Fattori, and Lorenzo Cavallaro. 2015. Copperdroid: Automatic reconstruction of Android malware behaviors. In NDSS.

[147]

Xinrui Tan, Hongjia Li, Liming Wang, and Zhen Xu. 2020. End-edge coordinated inference for real-time BYOD malware detection using deep learning. In 2020 IEEE Wireless Communications and Networking Conference (WCNC’20). IEEE, 1–6.

Digital Library

[148]

Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, and John Grundy. 2020. Explainable AI for software engineering. arXiv:2012.01614.

[149]

Daniele Ucci, Leonardo Aniello, and Roberto Baldoni. 2019. Survey of machine learning techniques for malware analysis. Computers & Security 81 (2019), 123–147.

[150]

Farhan Ullah, Hamad Naeem, Muhammad Rashid Naeem, Sohail Jabbar, Shehazad Khalid, Fadi Al-Turjman, and Abdelrahman Abuarqoub. 2019. Detection of clone scammers in Android markets using IoT-based edge computing. Transactions on Emerging Telecommunications Technologies (2019), e3791.

[151]

Danish Vasan, Mamoun Alazab, Sobia Wassan, Hamad Naeem, Babak Safaei, and Qin Zheng. 2020. IMCFN: Image-based malware classification using fine-tuned convolutional neural network architecture. Computer Networks 171 (2020), 107138.

Digital Library

[152]

Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv:1710.10903.

[153]

R. Vinayakumar, K. P. Soman, and Prabaharan Poornachandran. 2017. Deep android malware detection and classification. In 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI’17). IEEE, 1677–1683.

[154]

R. Vinayakumar, K. P. Soman, Prabaharan Poornachandran, and S. Sachin Kumar. 2018. Detecting Android malware using long short-term memory (LSTM). Journal of Intelligent & Fuzzy Systems 34, 3 (2018), 1277–1288.

Digital Library

[155]

VirusShare.com 2020. Because Sharing is Caring. Retrieved October 11, 2020 from https://virusshare.com/.

[156]

Xiaoyue Wan, Geyi Sheng, Yanda Li, Liang Xiao, and Xiaojiang Du. 2017. Reinforcement learning based mobile offloading for cloud-based malware detection. In GLOBECOM 2017-2017 IEEE Global Communications Conference. IEEE, 1–6.

Digital Library

[157]

Ji Wang, Qi Jing, Jianbo Gao, and Xuanwei Qiu. 2020. SEdroid: A robust Android malware detector using selective ensemble learning. In 2020 IEEE Wireless Communications and Networking Conference (WCNC’20). IEEE, 1–5.

[158]

Shanshan Wang, Zhenxiang Chen, Qiben Yan, Ke Ji, Lin Wang, Bo Yang, and Mauro Conti. 2018. Deep and broad learning based detection of Android malware via network traffic. In 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS’18). IEEE, 1–6.

[159]

Wei Wang, Mengxue Zhao, and Jigang Wang. 2019. Effective Android malware detection with a hybrid model based on deep autoencoder and convolutional neural network. Journal of Ambient Intelligence and Humanized Computing 10, 8 (2019), 3035–3043.

[160]

Yuxuan Wang, Yutai Hou, Wanxiang Che, and Ting Liu. 2020. From static to dynamic word representations: A survey. International Journal of Machine Learning and Cybernetics (2020), 1–20.

[161]

Zi Wang, Juecong Cai, Sihua Cheng, and Wenjia Li. 2016. DroidDeepLearner: Identifying Android malware using deep learning. In 2016 IEEE 37th Sarnoff Symposium. IEEE, 160–165.

[162]

Zhiqiang Wang, Qian Liu, and Yaping Chi. 2020. Review of Android malware detection based on deep learning. IEEE Access (2020).

[163]

Alexander Warnecke, Daniel Arp, Christian Wressnegger, and Konrad Rieck. 2020. Evaluating explanation methods for deep learning in security. In 2020 IEEE European Symposium on Security and Privacy (EuroS&P’20). IEEE, 158–174.

[164]

Supatsara Wattanakriengkrai, Patanamon Thongtanunam, Chakkrit Tantithamthavorn, Hideaki Hata, and Kenichi Matsumoto. 2022. Predicting Defective Lines Using a Model-Agnostic Technique. IEEE Transactions on Software Engineering 48, 5 (2022), 1480–1496.

[165]

Fengguo Wei, Yuping Li, Sankardas Roy, Xinming Ou, and Wu Zhou. 2017. Deep ground truth analysis of current android malware. In International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment. Springer, 252–276.

[166]

Lilian Weng. 2018. Attention? Attention. Lil’Log, June 24 (2018).

[167]

Bozhi Wu, Sen Chen, Cuiyun Gao, Lingling Fan, Yang Liu, Weiping Wen, and Michael R. Lyu. 2021. Why an Android App is classified as malware: Toward malware classification interpretation. ACM Transactions on Software Engineering and Methodology (TOSEM) 30, 2 (2021), 1–29.

Digital Library

[168]

Dong-Jie Wu, Ching-Hao Mao, Te-En Wei, Hahn-Ming Lee, and Kuo-Ping Wu. 2012. Droidmat: Android malware detection through manifest and API calls tracing. In 2012 Seventh Asia Joint Conference on Information Security. IEEE, 62–69.

Digital Library

[169]

Huanyu Wu. 2020. A systematical study for deep learning based Android malware detection. In Proceedings of the 2020 9th International Conference on Software and Computer Applications. 177–182.

Digital Library

[170]

Shengqu Xi, Shao Yang, Xusheng Xiao, Yuan Yao, Yayuan Xiong, Fengyuan Xu, Haoyu Wang, Peng Gao, Zhuotao Liu, Feng Xu, et al. 2019. DeepIntent: Deep icon-behavior learning for detecting intention-behavior discrepancy in mobile apps. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security. 2421–2436.

Digital Library

[171]

Xusheng Xiao. 2019. An image-inspired and CNN-based Android malware detection approach. In 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE’19). IEEE, 1259–1261.

Digital Library

[172]

Xi Xiao, Shaofeng Zhang, Francesco Mercaldo, Guangwu Hu, and Arun Kumar Sangaiah. 2019. Android malware detection based on system call sequences and LSTM. Multimedia Tools and Applications 78, 4 (2019), 3979–3999.

Digital Library

[173]

Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He. 2017. Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1492–1500.

[174]

Jiayun Xu, Yingjiu Li, Robert H. Deng, and Ke Xu. 2022. SDAC: A Slow-Aging Solution for Android Malware Detection Using Semantic Distance Based API Clustering. IEEE Transactions on Dependable and Secure Computing 19, 2 (2022), 1149–1163.

[175]

Ke Xu, Yingjiu Li, Robert H. Deng, and Kai Chen. 2018. DeepRefiner: Multi-layer Android malware detection system applying deep neural networks. In 2018 IEEE European Symposium on Security and Privacy (EuroS&P’18). IEEE, 473–487.

[176]

Lifan Xu, Dongping Zhang, Nuwan Jayasena, and John Cavazos. 2016. HADM: Hybrid analysis for detection of malware. In Proceedings of SAI Intelligent Systems Conference. Springer, 702–724.

[177]

Jinpei Yan, Yong Qi, and Qifan Rao. 2018. LSTM-based hierarchical denoising network for Android malware detection. Security and Communication Networks 2018 (2018), 5249190.

[178]

Limin Yang, Wenbo Guo, Qingying Hao, Arridhana Ciptadi, Ali Ahmadzadeh, Xinyu Xing, and Gang Wang. 2021. CADE: Detecting and explaining concept drift samples for security applications. In 30th USENIX Security Symposium (USENIX Security’21).

[179]

Yanfang Ye, Shifu Hou, Lingwei Chen, Jingwei Lei, Wenqiang Wan, Jiabin Wang, Qi Xiong, and Fudong Shao. 2019. Out-of-sample node representation learning for heterogeneous graph in real-time Android malware detection. In IJCAI. 4150–4156.

[180]

Yanfang Ye, Tao Li, Donald Adjeroh, and S. Sitharama Iyengar. 2017. A survey on malware detection using data mining techniques. ACM Computing Surveys (CSUR) 50, 3 (2017), 1–40.

Digital Library

[181]

Yao-Saint Yen and Hung-Min Sun. 2019. An Android mutation malware detection based on deep learning using visualization of importance from codes. Microelectronics Reliability 93 (2019), 109–114.

[182]

Baoguo Yuan, Junfeng Wang, Dong Liu, Wen Guo, Peng Wu, and Xuhua Bao. 2020. Byte-level malware classification based on Markov images and deep learning. Computers & Security 92 (2020), 101740.

[183]

Xiaoyong Yuan, Pan He, Qile Zhu, and Xiaolin Li. 2019. Adversarial examples: Attacks and defenses for deep learning. IEEE Transactions on Neural Networks and Learning Systems 30, 9 (2019), 2805–2824.

[184]

Zhenlong Yuan, Yongqiang Lu, Zhaoguo Wang, and Yibo Xue. 2014. Droid-Sec: Deep learning in android malware detection. In Proceedings of the 2014 ACM Conference on SIGCOMM. 371–372.

Digital Library

[185]

Zhenlong Yuan, Yongqiang Lu, and Yibo Xue. 2016. Droiddetector: Android malware characterization and detection using deep learning. Tsinghua Science and Technology 21, 1 (2016), 114–123.

[186]

Xian Zhan, Lingling Fan, Tianming Liu, Sen Chen, Li Li, Haoyu Wang, Yifei Xu, Xiapu Luo, and Yang Liu. 2020. Automated third-party library detection for Android applications: Are we there yet? In The 35th IEEE/ACM International Conference on Automated Software Engineering (ASE’20).

Digital Library

[187]

Xiaohan Zhang, Yuan Zhang, Ming Zhong, Daizong Ding, Yinzhi Cao, Yukun Zhang, Mi Zhang, and Min Yang. 2020. Enhancing state-of-the-art classifiers with API semantics to detect evolved Android malware. In Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security. 757–770.

Digital Library

[188]

Yanxin Zhang, Yulei Sui, Shirui Pan, Zheng Zheng, Baodi Ning, Ivor Tsang, and Wanlei Zhou. 2019. Familial clustering for weakly-labeled Android malware using hybrid representation learning. IEEE Transactions on Information Forensics and Security 15 (2019), 3401–3414.

[189]

Zicheng Zhang, Wenrui Diao, Chengyu Hu, Shanqing Guo, Chaoshun Zuo, and Li Li. 2020. An empirical study of potentially malicious third-party libraries in Android apps. In The 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec’20).

Digital Library

[190]

Kaifa Zhao, Hao Zhou, Yulin Zhu, Xian Zhan, Kai Zhou, Jianfeng Li, Le Yu, Wei Yuan, and Xiapu Luo. 2021. Structural attack against graph based Android malware detection. In Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security. 3218–3235.

Digital Library

[191]

Yanjie Zhao, Li Li, Haoyu Wang, Haipeng Cai, Tegawendé F. Bissyandé, Jacques Klein, and John Grundy. 2021. On the impact of sample duplication in machine-learning-based android malware detection. ACM Transactions on Software Engineering and Methodology (TOSEM) 30, 3 (2021), 1–38.

[192]

Xu Zhiwu, Kerong Ren, and Fu Song. 2019. Android malware family classification and characterization using CFG and DFG. In 2019 International Symposium on Theoretical Aspects of Software Engineering (TASE’19). IEEE, 49–56.

[193]

Hanxun Zhou, Xinlin Yang, Hong Pan, and Wei Guo. 2020. An Android malware detection approach based on SIMGRU. IEEE Access 8 (2020), 148404–148410.

[194]

Yajin Zhou and Xuxian Jiang. 2012. Dissecting Android malware: Characterization and evolution. In 2012 IEEE Symposium on Security and Privacy. IEEE, 95–109.

Digital Library

[195]

Dali Zhu, Hao Jin, Ying Yang, Di Wu, and Weiyi Chen. 2017. DeepFlow: Deep learning-based malware detection by mining Android application for abnormal usage of sensitive data. In 2017 IEEE Symposium on Computers and Communications (ISCC’17). IEEE, 438–443.

[196]

Dali Zhu, Yuchen Ma, Tong Xi, and Yiming Zhang. 2019. FSNet: Android malware detection with only one feature. In 2019 IEEE Symposium on Computers and Communications (ISCC’19). IEEE, 1–6.

[197]

Dali Zhu, Tong Xi, Pengfei Jing, Di Wu, Qing Xia, and Yiming Zhang. 2019. A transparent and multimodal malware detection method for Android apps. In Proceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems. 51–60.

Digital Library

[198]

Dali Zhu, Tong Xi, Pengfei Jing, Qing Xia, Di Wu, and Yiming Zhang. 2020. Sadroid: A deep classification model for Android malware detection based on semantic analysis. In 2020 IEEE Wireless Communications and Networking Conference (WCNC’20). IEEE, 1–7.

[199]

Huijuan Zhu, Liangmin Wang, Sheng Zhong, Yang Li, and Victor S. Sheng. 2021. A Hybrid Deep Network Framework for Android Malware Detection. IEEE Transactions on Knowledge and Data Engineering 19 (2021), 1-1.

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Index Terms

Deep Learning for Android Malware Defenses: A Systematic Literature Review
1. General and reference
  1. Document types
    1. Surveys and overviews
2. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Malware and its mitigation
  2. Software and application security
    1. Software security engineering

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 8

August 2023

789 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3567473

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Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 23 December 2022

Online AM: 22 June 2022

Accepted: 14 June 2022

Revised: 26 April 2022

Received: 11 March 2021

Published in CSUR Volume 55, Issue 8

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