Fetching the paper…
Reading the bibliography…
Deep learning has emerged as a promising technology for achieving Android malware detection.
K. W. Y. Au, Y. F. Zhou, Z. Huang, and D. Lie, “Pscout: analyzing the android permission specification,” in Proceedings of the 2012 ACM conference on Computer and communications security , 2012, pp. 217–228
2012
Earlier work this paper cites.
J. Hoffmann, M. Ussath, T. Holz, and M. Spreitzenbarth, “Slicing droids: program slicing for smali code,” in Proceedings of the 28th Annual ACM Symposium on Applied Computing , 2013, pp. 1844–1851
2013
Earlier work this paper cites.
D. Arp, M. Spreitzenbarth, M. Hubner, H. Gascon, K. Rieck, and C. Siemens, “Drebin: Effective and explainable detection of android malware in your pocket.” in Ndss , vol. 14, 2014, pp. 23–26
2014
Earlier work this paper cites.
S. Arzt, S. Rasthofer, C. Fritz, E. Bodden, A. Bartel, J. Klein, Y. Le Traon, D. Octeau, and P. McDaniel, “Flowdroid: Precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for android apps,” vol. 49, no. 6. ACM New York, NY, USA, 2014, pp. 259–269
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
D. Octeau, D. Luchaup, M. Dering, S. Jha, and P. McDaniel, “Composite constant propagation: Application to android inter-component communication analysis,” in ICSE’15 , vol. 1. IEEE, 2015, pp. 77–88
2015
Earlier work this paper cites.
K. Allix, T. F. Bissyandé, J. Klein, and Y. Le Traon, “Androzoo: Collecting millions of android apps for the research community,” in MSR’16 . IEEE, 2016, pp. 468–471
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
B. Kang, S. Y. Yerima, K. McLaughlin, and S. Sezer, “N-opcode analysis for android malware classification and categorization,” in 2016 International conference on cyber security and protection of digital services (cyber security) . IEEE, 2016, pp. 1–7
2016
Earlier work this paper cites.
M. Sebastián, R. Rivera, P. Kotzias, and J. Caballero, “Avclass: A tool for massive malware labeling,” in Research in Attacks, Intrusions, and Defenses: 19th International Symposium, RAID 2016, Paris, France, September 19-21, 2016, Proceedings 19 . Springer, 2016, pp. 230–253
2016
Earlier work this paper cites.
M. Backes, S. Bugiel, E. Derr, P. McDaniel, D. Octeau, and S. Weisgerber, “On demystifying the android application framework: { \{ Re-Visiting } \} android permission specification analysis,” in 25th USENIX security symposium (USENIX security 16) , 2016, pp. 1101–1118
2016
Earlier work this paper cites.
S. Hou, Y. Ye, Y. Song, and M. Abdulhayoglu, “Hindroid: An intelligent android malware detection system based on structured heterogeneous information network,” in Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining , 2017, pp. 1507–1515
2017
Earlier work this paper cites.
Z. Yu, J. Yu, J. Fan, and D. Tao, “Multi-modal factorized bilinear pooling with co-attention learning for visual question answering,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 1821–1830
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
S. Alam, Z. Qu, R. Riley, Y. Chen, and V. Rastogi, “Droidnative: Automating and optimizing detection of android native code malware variants,” Computers & Security , vol. 65, no. MAR., pp. 230–246, 2017
2017
Earlier work this paper cites.
K. Grosse, N. Papernot, P. Manoharan, M. Backes, and P. McDaniel, “Adversarial examples for malware detection,” in Computer Security–ESORICS 2017: 22nd European Symposium on Research in Computer Security, Oslo, Norway, September 11-15, 2017, Proceedings, Part II 22 . Springer, 2017, pp. 62–79
2017
Earlier work this paper cites.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 618–626
2017
Earlier work this paper cites.
T. Kim, B. Kang, M. Rho, S. Sezer, and E. G. Im, “A multimodal deep learning method for android malware detection using various features,” IEEE Transactions on Information Forensics and Security , vol. 14, no. 3, pp. 773–788, 2018
2018
Cited alongside, same era.
F. Pauck, E. Bodden, and H. Wehrheim, “Do android taint analysis tools keep their promises?” in Proceedings of the 2018 26th ACM joint meeting on european software engineering conference and symposium on the foundations of software engineering , 2018, pp. 331–341
2018
Cited alongside, same era.
A. Narayanan, M. Chandramohan, L. Chen, and Y. Liu, “A multi-view context-aware approach to android malware detection and malicious code localization,” Empirical Software Engineering , vol. 23, no. 3, pp. 1222–1274, 2018
2018
Cited alongside, same era.
A. Narayanan, C. Soh, L. Chen, Y. Liu, and L. Wang, “apk2vec: Semi-supervised multi-view representation learning for profiling android applications,” in 2018 IEEE International Conference on Data Mining (ICDM) . IEEE, 2018, pp. 357–366
S. Hou, Y. Fan, M. Ju, Y. Ye, W. Wan, K. Wang, Y. Mei, Q. Xiong, and F. Shao, “Disentangled representation learning in heterogeneous information network for large-scale android malware detection in the covid-19 era and beyond,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 9, 2021, pp. 7754–7761
2021
Later among the works it cites.
Y. Wu, D. Zou, W. Yang, X. Li, and H. Jin, “Homdroid: detecting android covert malware by social-network homophily analysis,” in Proceedings of the 30th acm sigsoft international symposium on software testing and analysis , 2021, pp. 216–229
2021
Later among the works it cites.
J. Samhi, L. Li, T. F. Bissyandé, and J. Klein, “Difuzer: uncovering suspicious hidden sensitive operations in android apps,” in 2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) . IEEE, 2022, pp. 723–735
2022
Later among the works it cites.
Y. He, Y. Liu, L. Wu, Z. Yang, K. Ren, and Z. Qin, “Msdroid: Identifying malicious snippets for android malware detection,” IEEE Transactions on Dependable and Secure Computing , 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
A. Arora, S. K. Peddoju, and M. Conti, “Permpair: Android malware detection using permission pairs,” IEEE Transactions on Information Forensics and Security , vol. 15, pp. 1968–1982, 2019
2019
Cited alongside, same era.
Y. Tsutano, S. Bachala, W. Srisa-an, G. Rothermel, and J. Dinh, “Jitana: A modern hybrid program analysis framework for android platforms,” Journal of Computer Languages , vol. 52, pp. 55–71, 2019
2019
Cited alongside, same era.
L. Onwuzurike, E. Mariconti, P. Andriotis, E. D. Cristofaro, G. Ross, and G. Stringhini, “Mamadroid: Detecting android malware by building markov chains of behavioral models (extended version),” ACM Transactions on Privacy and Security (TOPS) , vol. 22, no. 2, pp. 1–34, 2019
2019
Cited alongside, same era.
F. Pendlebury, F. Pierazzi, R. Jordaney, J. Kinder, and L. Cavallaro, “ { \{ TESSERACT } \} : Eliminating experimental bias in malware classification across space and time,” in 28th USENIX security symposium (USENIX Security 19) , 2019, pp. 729–746
2019
Cited alongside, same era.
Z. Ying, D. Bourgeois, J. You, M. Zitnik, and J. Leskovec, “Gnnexplainer: Generating explanations for graph neural networks,” Advances in neural information processing systems , vol. 32, 2019
2019
Cited alongside, same era.
L. Shi, J. Ming, J. Fu, G. Peng, D. Xu, K. Gao, and X. Pan, “Vahunt: Warding off new repackaged android malware in app-virtualization’s clothing,” in Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security , 2020, pp. 535–549
2020
Cited alongside, same era.
M. Alhanahnah, Q. Yan, H. Bagheri, H. Zhou, Y. Tsutano, W. Srisa-An, and X. Luo, “Dina: Detecting hidden android inter-app communication in dynamic loaded code,” IEEE Transactions on Information Forensics and Security , vol. 15, pp. 2782–2797, 2020
2020
Cited alongside, same era.
X. Zhang, Y. Zhang, M. Zhong, D. Ding, Y. Cao, Y. Zhang, M. Zhang, and M. Yang, “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 , 2020, pp. 757–770
2020
Cited alongside, same era.
2022
Later among the works it cites.
J. Qiu, Q.-L. Han, W. Luo, L. Pan, S. Nepal, J. Zhang, and Y. Xiang, “Cyber code intelligence for android malware detection,” IEEE Transactions on Cybernetics , vol. 53, no. 1, pp. 617–627, 2022
2022
Later among the works it cites.
Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, “A convnet for the 2020s,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 11 976–11 986
2022
Later among the works it cites.
Y. Wu, D. Zou, S. Dou, W. Yang, D. Xu, and H. Jin, “Vulcnn: An image-inspired scalable vulnerability detection system,” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 2365–2376
2022
Later among the works it cites.
P. P. Fyodor Yarochkin, Zhengyu Dong, “Lemon Group’s Cybercriminal Businesses Built on Preinfected Devices,” https://www.trendmicro.com/en_us/research/23/e/lemon-group-cybercriminal-businesses-built-on-preinfected-devices.html
2023
Later among the works it cites.
Y. He, X. Kang, Q. Yan, and E. Li, “Resnext+: Attention mechanisms based on resnext for malware detection and classification,” IEEE Transactions on Information Forensics and Security , 2023
2023
Later among the works it cites.
Z. Liu, L. F. Zhang, and Y. Tang, “Enhancing malware detection for android apps: Detecting fine-granularity malicious components,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2023, pp. 1212–1224
2023
Later among the works it cites.
C. Gao, M. Cai, S. Yin, G. Huang, H. Li, W. Yuan, and X. Luo, “Obfuscation-resilient android malware analysis based on complementary features,” IEEE Transactions on Information Forensics and Security , 2023
2023
Later among the works it cites.
W. Niu, Y. Wang, X. Liu, R. Yan, X. Li, and X. Zhang, “Gcdroid: Android malware detection based on graph compression with reachability relationship extraction for iot devices,” IEEE Internet of Things Journal , 2023
2023
Later among the works it cites.
“Malware,” https://www.av-test.org/en/statistics/malware/
2024
Closest in time.
“Androguard,” https://github.com/androguard/androguard
2024
Closest in time.
“Apktool,” https://apktool.org/
2024
Closest in time.
C. Gao, G. Huang, H. Li, B. Wu, Y. Wu, and W. Yuan, “A comprehensive study of learning-based android malware detectors under challenging environments,” in Proceedings of the 46th IEEE/ACM International Conference on Software Engineering , 2024, pp. 1–13
2024
Closest in time.