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Currently, Android malware detection is mostly performed on server side against the increasing number of malware.
A.-D. Schmidt, F. Peters, F. Lamour, C. Scheel, S. A. Çamtepe, and S. Albayrak, “Monitoring smartphones for anomaly detection,” Mobile Networks and Applications , 2009
2009
Earlier work this paper cites.
T. Bläsing, L. Batyuk, A.-D. Schmidt, S. A. Camtepe, and S. Albayrak, “An Android application sandbox system for suspicious software detection,” in MALWARE , 2010
2010
Earlier work this paper cites.
E. Chin, A. P. Felt, K. Greenwood, and D. Wagner, “Analyzing inter-application communication in Android,” in MobiSys , 2011
2011
Earlier work this paper cites.
Y. Zhou and X. Jiang, “Dissecting Android malware: Characterization and evolution,” in S&P , 2012
2012
Earlier work this paper cites.
Y. Zhou, Z. Wang, W. Zhou, and X. Jiang, “Hey, you, get off of my market: detecting malicious apps in official and alternative Android markets.” in NDSS , 2012
2012
Earlier work this paper cites.
L. K. Yan and H. Yin, “Droidscope: Seamlessly reconstructing the OS and dalvik semantic views for dynamic Android malware analysis,” in USENIX Security , 2012
2012
Earlier work this paper cites.
D.-J. Wu, C.-H. Mao, T.-E. Wei, H.-M. Lee, and K.-P. Wu, “Droidmat: Android malware detection through manifest and api calls tracing,” in ASIAJCIS , 2012
2012
Earlier work this paper cites.
A. Shabtai, U. Kanonov, Y. Elovici, C. Glezer, and Y. Weiss, ““andromaly”: a behavioral malware detection framework for Android devices,” Journal of Intelligent Information Systems , 2012
2012
Earlier work this paper cites.
M. Grace, Y. Zhou, Q. Zhang, S. Zou, and X. Jiang, “Riskranker: scalable and accurate zero-day Android malware detection,” in MobiSys , 2012
2012
Earlier work this paper cites.
L. Lu, Z. Li, Z. Wu, W. Lee, and G. Jiang, “Chex: statically vetting Android apps for component hijacking vulnerabilities,” in CCS , 2012
2012
Earlier work this paper cites.
P. P. Chan, L. C. Hui, and S.-M. Yiu, “Droidchecker: analyzing Android applications for capability leak,” in WISEC , 2012
2012
Earlier work this paper cites.
W. Zhou, Y. Zhou, M. Grace, X. Jiang, and S. Zou, “Fast, scalable detection of piggybacked mobile applications,” in CODASPY , 2013
2013
Earlier work this paper cites.
Y. Zhongyang, Z. Xin, B. Mao, and L. Xie, “Droidalarm: an all-sided static analysis tool for Android privilege-escalation malware,” in AsiaCCS , 2013
2013
Earlier work this paper cites.
C.-Y. Huang, Y.-T. Tsai, and C.-H. Hsu, “Performance evaluation on permission-based detection for Android malware,” in Advances in Intelligent Systems and Applications-Volume 2 , 2013
2013
Earlier work this paper cites.
Z. Aung and W. Zaw, “Permission-based Android malware detection,” International Journal of Scientific & Technology Research , 2013
2013
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,” in PLDI , 2014
2014
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 , 2014
2014
Earlier work this paper cites.
C. Yang, Z. Xu, G. Gu, V. Yegneswaran, and P. Porras, “Droidminer: Automated mining and characterization of fine-grained malicious behaviors in Android applications,” in ESORICS , 2014
2014
Earlier work this paper cites.
W. Yu, L. Ge, G. Xu, and X. Fu, “Towards neural network based malware detection on Android mobile devices,” in Cybersecurity Systems for Human Cognition Augmentation , 2014
2014
Earlier work this paper cites.
L. Deshotels, V. Notani, and A. Lakhotia, “Droidlegacy: Automated familial classification of Android malware,” in ACM SIGPLAN on program protection and reverse engineering workshop , 2014
2014
Cited alongside, same era.
M. Zhang, Y. Duan, H. Yin, and Z. Zhao, “Semantics-aware Android malware classification using weighted contextual api dependency graphs,” in CCS , 2014
2014
Cited alongside, same era.
W. Enck, P. Gilbert, S. Han, V. Tendulkar, B.-G. Chun, L. P. Cox, J. Jung, P. McDaniel, and A. N. Sheth, “Taintdroid: an information-flow tracking system for realtime privacy monitoring on smartphones,” TOCS , 2014
2014
Cited alongside, same era.
F. Wei, S. Roy, X. Ou et al. , “Amandroid: A precise and general inter-component data flow analysis framework for security vetting of Android apps,” in CCS , 2014
2014
Cited alongside, same era.
J. Sun, K. Yan, X. Liu, C. Yang, and Y. Fu, “Malware detection on Android smartphones using keywords vector and svm,” in ICIS , 2017
2017
Later among the works it cites.
S. Chen, T. Su, L. Fan, G. Meng, M. Xue, Y. Liu, and L. Xu, “Are mobile banking apps secure? What can be improved?” in FSE , 2018
2018
Later among the works it cites.
S. Chen, M. Xue, L. Fan, S. Hao, L. Xu, H. Zhu, and B. Li, “Automated poisoning attacks and defenses in malware detection systems: An adversarial machine learning approach,” Computers & Security , 2018
2018
Later among the works it cites.
E. B. Karbab, M. Debbabi, A. Derhab, and D. Mouheb, “MalDozer: Automatic framework for Android malware detection using deep learning,” Digital Investigation , 2018
2018
Later among the works it cites.
A. Narayanan, C. Soh, L. Chen, Y. Liu, and L. Wang, “Apk2vec: Semi-supervised multi-view representation learning for profiling Android applications,” in ICDM , 2018
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S. Hao, B. Liu, S. Nath, W. G. Halfond, and R. Govindan, “Puma: programmable ui-automation for large-scale dynamic analysis of mobile apps,” in MobiSys , 2014
2014
Cited alongside, same era.
K. Tam, S. J. Khan, A. Fattori, and L. Cavallaro, “Copperdroid: Automatic reconstruction of Android malware behaviors.” in NDSS , 2015
2015
Cited alongside, same era.
L. Li, A. Bartel, T. F. Bissyandé, J. Klein, Y. Le Traon, S. Arzt, S. Rasthofer, E. Bodden, D. Octeau, and P. McDaniel, “Iccta: Detecting inter-component privacy leaks in Android apps,” in ICSE , 2015
2015
Cited alongside, same era.
K. Lu, Z. Li, V. P. Kemerlis, Z. Wu, L. Lu, C. Zheng, Z. Qian, and W. Lee, “Checking more and alerting less: detecting privacy leakages via enhanced data-flow analysis and peer voting.” in NDSS , 2015
2015
Cited alongside, same era.
S. Chen, M. Xue, Z. Tang, L. Xu, and H. Zhu, “Stormdroid: A streaminglized machine learning-based system for detecting Android malware,” in AsiaCCS , 2016
2016
Cited alongside, same era.
E. Mariconti, L. Onwuzurike, P. Andriotis, E. D. Cristofaro, G. Ross, and G. Stringhini, “Mamadroid: Detecting Android malware by building markov chains of behavioral models,” 2016
2016
Cited alongside, same era.
S. Chen, M. Xue, and L. Xu, “Towards adversarial detection of mobile malware: poster,” in MobiCom , 2016
2016
Cited alongside, same era.
L. Fan, M. Xue, S. Chen, L. Xu, and H. Zhu, “Poster: Accuracy vs. time cost: Detecting android malware through pareto ensemble pruning,” in CCS , 2016
2016
Cited alongside, same era.
2018
Later among the works it cites.
L. Li, J. Gao, T. F. Bissyandé, L. Ma, X. Xia, and J. Klein, “Characterising deprecated Android apis,” in MSR , 2018
2018
Later among the works it 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,” TIFS , 2018
2018
Later among the works it cites.
K. Xu, Y. Li, R. H. Deng, and K. Chen, “Deeprefiner: Multi-layer Android malware detection system applying deep neural networks,” in EuroS&P , 2018
2018
Later among the works it cites.
C. Tang, S. Chen, L. Fan, L. Xu, Y. Liu, Z. Tang, and L. Dou, “A large-scale empirical study on industrial fake apps,” in ICSE , 2019
2019
Later among the works it cites.
R. Feng, S. Chen, X. Xie, L. Ma, G. Meng, Y. Liu, and S.-W. Lin, “Mobidroid: A performance-sensitive malware detection system on mobile platform,” in ICECCS , 2019
2019
Later among the works it cites.
Q. Guo, S. Chen, X. Xie, L. Ma, Q. Hu, H. Liu, Y. Liu, J. Zhao, and X. Li, “An empirical study towards characterizing deep learning development and deployment across different frameworks and platforms,” in ASE , 2019
2019
Later among the works it cites.
S. Chen, M. Xue, L. Fan, L. Ma, Y. Liu, and L. Xu, “How can we craft large-scale Android malware? an automated poisoning attack,” in AI4Mobile , 2019
2019
Later among the works it cites.
X. Chen, C. Li, D. Wang, S. Wen, J. Zhang, S. Nepal, Y. Xiang, and K. Ren, “Android hiv: A study of repackaging malware for evading machine-learning detection,” TIFS , 2019
2019
Later among the works it cites.
S. Chen, L. Fan, G. Meng, T. Su, M. Xue, Y. Xue, Y. Liu, and L. Xu, “An empirical assessment of security risks of global Android banking apps,” in ICSE , 2020
2020
Closest in time.
B. Wu, S. Chen, C. Gao, L. Fan, Y. Liu, W. Wen, and L. Michael, “Why an Android app is classified as malware? Towards malware classification interpretation,” TOSEM , 2020
2020
Closest in time.
R. Feng, J. Q. Lim, S. Chen, S.-W. Lin, and Y. Liu, “Seqmobile: An efficient sequence-based malware detection system using rnn on mobile devices,” in ICECCS , 2020
2020
Closest in time.
K. Basu, P. Krishnamurthy, F. Khorrami, and R. Karri, “A theoretical study of hardware performance counters-based malware detection,” TIFS , 2020
2020
Closest in time.
G. Chen, S. Chen, L. Fan, X. Du, Z. Zhao, F. Song, and Y. Liu, “Who is real bob? adversarial attacks on speaker recognition systems,” S&P , 2021
2021
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