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As Android malware is growing and evolving, deep learning has been introduced into malware detection, resulting in great effectiveness.
M. Gori, G. Monfardini, and F. Scarselli, “A new model for learning in graph domains,” in 2005 IEEE International Joint Conference on Neural Networks , vol. 2. IEEE, 2005, pp. 729–734
2005
Earlier work this paper cites.
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The graph neural network model,” IEEE Trans. Neural Networks , vol. 20, no. 1, pp. 61–80, 2009
2009
Earlier work this paper cites.
W. Enck, P. Gilbert, B. Chun, L. P. Cox, J. Jung, P. D. McDaniel, and A. Sheth, “TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones,” in OSDI’10 , 2010, pp. 393–407
2010
Earlier work this paper cites.
M. Zheng, M. Sun, and J. C. S. Lui, “Droid analytics: A signature based analytic system to collect, extract, analyze and associate android malware,” in TrustCom/ISPA/IUCC’13 . IEEE Computer Society, 2013, pp. 163–171
2013
Earlier work this paper cites.
Y. Aafer, W. Du, and H. Yin, “DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android,” in SecureComm’13 , 2013, pp. 86–103
2013
Earlier work this paper cites.
Z. Yuan, Y. Lu, Z. Wang, and Y. Xue, “Droid-sec: deep learning in android malware detection,” in SIGCOMM’14 . ACM, 2014, pp. 371–372
2014
Earlier work this paper cites.
S. Arzt, S. Rasthofer, C. Fritz, E. Bodden, A. Bartel, J. Klein, Y. L. Traon, D. Octeau, and P. D. McDaniel, “FlowDroid: precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for Android apps,” in PLDI’14 , 2014, pp. 259–269
2014
Earlier work this paper cites.
S. Rasthofer, S. Arzt, and E. Bodden, “A Machine-learning Approach for Classifying and Categorizing Android Sources and Sinks,” in NDSS’14 , 2014
2014
Earlier work this paper cites.
F. Wei, S. Roy, X. Ou, and Robby, “Amandroid: A Precise and General Inter-component Data Flow Analysis Framework for Security Vetting of Android Apps,” in CCS’14 , 2014, pp. 1329–1341
2014
Earlier work this paper cites.
D. Arp, M. Spreitzenbarth, M. Hubner, H. Gascon, and K. Rieck, “DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket,” in NDSS’14 , 2014
2014
Earlier work this paper cites.
M. Zhang, Y. Duan, H. Yin, and Z. Zhao, “Semantics-aware android malware classification using weighted contextual API dependency graphs,” in CCS’14 . ACM, 2014, pp. 1105–1116
2014
Earlier work this paper cites.
C. Yang, Z. Xu, G. Gu, V. Yegneswaran, and P. A. Porras, “DroidMiner: Automated Mining and Characterization of Fine-grained Malicious Behaviors in Android Applications,” in ESORICS’14 , 2014, pp. 163–182
2014
Earlier work this paper cites.
V. Avdiienko, K. Kuznetsov, A. Gorla, A. Zeller, S. Arzt, S. Rasthofer, and E. Bodden, “Mining apps for abnormal usage of sensitive data,” in ICSE’15 . IEEE Computer Society, 2015, pp. 426–436
2015
Earlier work this paper cites.
Y. Cao, Y. Fratantonio, A. Bianchi, M. Egele, C. Kruegel, G. Vigna, and Y. Chen, “EdgeMiner: Automatically Detecting Implicit Control Flow Transitions through the Android Framework,” in NDSS’15 , 2015
2015
Earlier work this paper cites.
D. Octeau, D. Luchaup, M. Dering, S. Jha, and P. D. McDaniel, “Composite constant propagation: Application to android inter-component communication analysis,” in ICSE’15 . IEEE Computer Society, 2015, pp. 77–88
2015
Earlier work this paper cites.
S. Roy, J. DeLoach, Y. Li, N. Herndon, D. Caragea, X. Ou, V. P. Ranganath, H. Li, and N. Guevara, “Experimental study with real-world data for android app security analysis using machine learning,” in ACSAC’15 . ACM, 2015, pp. 81–90
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in ICLR’15 , 2015
2015
Earlier work this paper cites.
M. I. Gordon, D. Kim, J. H. Perkins, L. Gilham, N. Nguyen, and M. C. Rinard, “Information flow analysis of android applications in droidsafe,” in NDSS’15 . The Internet Society, 2015
2015
Earlier work this paper cites.
Y. Li, T. Shen, X. Sun, X. Pan, and B. Mao, “Detection, classification and characterization of android malware using API data dependency,” in SecureComm’15 , vol. 164. Springer, 2015, pp. 23–40
2015
Earlier work this paper cites.
S. Hou, A. Saas, Y. Ye, and L. Chen, “Droiddelver: An android malware detection system using deep belief network based on API call blocks,” in WAIM’16 Workshops , ser. Lecture Notes in Computer Science, vol. 9998, 2016, pp. 54–66
2016
Earlier work this paper cites.
X. Su, D. Zhang, W. Li, and K. Zhao, “A deep learning approach to android malware feature learning and detection,” in Trustcom/BigDataSE/ISPA’16 . IEEE, 2016, pp. 244–251
2016
Earlier work this paper cites.
S. Hou, A. Saas, L. Chen, and Y. Ye, “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, WI 2016 Workshops . IEEE Computer Society, 2016, pp. 104–111
2016
Cited alongside, same era.
K. Allix, T. F. Bissyandé, J. Klein, and Y. L. Traon, “Androzoo: collecting millions of android apps for the research community,” in MSR’16 . ACM, 2016, pp. 468–471
2016
Cited alongside, same era.
M. Y. Wong and D. Lie, “IntelliDroid: A Targeted Input Generator for the Dynamic Analysis of Android Malware,” in NDSS’16 , 2016
2016
Cited alongside, same era.
S. K. Dash, G. Suarez-Tangil, S. J. Khan, K. Tam, M. Ahmadi, J. Kinder, and L. Cavallaro, “DroidScribe: Classifying Android Malware Based on Runtime Behavior,” in 2016 IEEE Security and Privacy Workshops , 2016, pp. 252–261
2016
Cited alongside, same era.
H. Cai, N. Meng, B. G. Ryder, and D. Yao, “DroidCat: Effective Android Malware Detection and Categorization via App-Level Profiling,” IEEE Trans. Information Forensics and Security , vol. 14, no. 6, pp. 1455–1470, 2019
2019
Later among the works it cites.
W. Wang, M. Zhao, and J. Wang, “Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network,” J. Ambient Intell. Humaniz. Comput. , vol. 10, no. 8, pp. 3035–3043, 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
H. Cai, “Assessing and improving malware detection sustainability through app evolution studies,” ACM Trans. Softw. Eng. Methodol. , vol. 29, no. 2, pp. 8:1–8:28, 2020
2020
Later among the works it cites.
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R. Nix and J. Zhang, “Classification of android apps and malware using deep neural networks,” in IJCNN’17: 2017 International Joint Conference on Neural Networks . IEEE, 2017, pp. 1871–1878
2017
Cited alongside, same era.
N. McLaughlin, J. M. del Rincón, B. Kang, S. Y. Yerima, P. C. Miller, S. Sezer, Y. Safaei, E. Trickel, Z. Zhao, A. Doupé, and G. Ahn, “Deep android malware detection,” in CODASPY’17 . ACM, 2017, pp. 301–308
2017
Cited alongside, same era.
S. Hou, Y. Ye, Y. Song, and M. Abdulhayoglu, “HinDroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network,” in KDD’17 , 2017, pp. 1507–1515
2017
Cited alongside, same era.
Z. Xu, K. Ren, S. Qin, and F. Craciun, “Cdgdroid: Android malware detection based on deep learning using CFG and DFG,” in ICFEM’18 , ser. Lecture Notes in Computer Science, vol. 11232. Springer, 2018, pp. 177–193
2018
Cited alongside, same era.
2018
Cited alongside, same era.
H. Cai and J. Jenkins, “Towards sustainable android malware detection,” in ICSE Companion . ACM, 2018, pp. 350–351
2018
Cited alongside, same era.
G. Tao, Z. Zheng, Z. Guo, and M. R. Lyu, “Malpat: Mining patterns of malicious and benign android apps via permission-related apis,” IEEE Trans. Reliab. , vol. 67, no. 1, pp. 355–369, 2018
2018
Cited alongside, same era.
A. Saracino, D. Sgandurra, G. Dini, and F. Martinelli, “MADAM: effective and efficient behavior-based android malware detection and prevention,” IEEE Trans. Dependable Secur. Comput. , vol. 15, no. 1, pp. 83–97, 2018
2018
Cited alongside, same era.
D. Li, L. Zhao, Q. Cheng, N. Lu, and W. Shi, “Opcode sequence analysis of android malware by a convolutional neural network,” Concurr. Comput. Pract. Exp. , vol. 32, no. 18, 2020
2020
Later among the works it cites.
D. Chaulagain, P. Poudel, P. Pathak, S. Roy, D. Caragea, G. Liu, and X. Ou, “Hybrid analysis of android apps for security vetting using deep learning,” in CNS’20 . IEEE, 2020, pp. 1–9
2020
Later among the works it cites.
M. K. Alzaylaee, S. Y. Yerima, and S. Sezer, “Dl-droid: Deep learning based android malware detection using real devices,” Comput. Secur. , vol. 89, 2020
2020
Later among the works it cites.
T. S. John, T. Thomas, and S. Emmanuel, “Graph convolutional networks for android malware detection with system call graphs,” in 2020 Third ISEA Conference on Security and Privacy (ISEA-ISAP) , 2020, pp. 162–170
2020
Later among the works it cites.
A. Pektas and T. Acarman, “Learning to detect android malware via opcode sequences,” Neurocomputing , vol. 396, pp. 599–608, 2020
2020
Later among the works it cites.
S. Millar, N. McLaughlin, J. M. del Rincón, P. Miller, and Z. Zhao, “Dandroid: A multi-view discriminative adversarial network for obfuscated android malware detection,” in CODASPY’20 . ACM, 2020, pp. 353–364
2020
Later among the works it cites.
H. Cai, X. Fu, and A. Hamou-Lhadj, “A study of run-time behavioral evolution of benign versus malicious apps in android,” Inf. Softw. Technol. , vol. 122, p. 106291, 2020
2020
Later among the works it cites.
T. Lu, Y. Du, L. Ouyang, Q. Chen, and X. Wang, “Android malware detection based on a hybrid deep learning model,” Secur. Commun. Networks , 2020
2020
Later among the works it cites.
IDC, “Smartphone Market Share,” Available at https://www.idc.com/promo/smartphone-market-share
2021
Later among the works it cites.
McAfee, “McAfee Mobile Threat Report,” Available at https://www.mcafee.com/content/dam/global/infographics/McAfeeMobileThreatReport2021.pdf
2021
Later among the works it cites.
J. Qiu, J. Zhang, W. Luo, L. Pan, S. Nepal, and Y. Xiang, “A survey of android malware detection with deep neural models,” ACM Comput. Surv. , vol. 53, no. 6, pp. 126:1–126:36, 2021
2021
Later among the works it cites.
H. Gao, S. Cheng, and W. Zhang, “Gdroid: Android malware detection and classification with graph convolutional network,” Comput. Secur. , vol. 106, p. 102264, 2021
2021
Later among the works it cites.
P. Xu, C. Eckert, and A. Zarras, “Detecting and categorizing android malware with graph neural networks,” in SAC ’21: The 36th ACM/SIGAPP Symposium on Applied Computing . ACM, 2021, pp. 409–412
2021
Later among the works it cites.
H. Cai and B. G. Ryder, “A longitudinal study of application structure and behaviors in android,” IEEE Trans. Software Eng. , vol. 47, no. 12, pp. 2934–2955, 2021
2021
Later among the works it cites.
D. Fu and J. He, “SDG: A simplified and dynamic graph neural network,” in SIGIR ’21 . ACM, 2021, pp. 2273–2277
2021
Later among the works it cites.
P. Feng, J. Ma, T. Li, X. Ma, N. Xi, and D. Lu, “Android malware detection via graph representation learning,” Mob. Inf. Syst. , vol. 2021, pp. 5 538 841:1–5 538 841:14, 2021
2021
Later among the works it cites.
J. Busch, A. Kocheturov, V. Tresp, and T. Seidl, “NF-GNN: network flow graph neural networks for malware detection and classification,” in SSDBM’21: 33rd International Conference on Scientific and Statistical Database Management . ACM, 2021, pp. 121–132
2021
Later among the works it cites.