參考文獻 |
[1] Heqing Huang et al., "A Large-Scale Study of Android Malware Development Phenomenon on Public Malware Submission and Scanning Platform," IEEE Transactions on Big Data, vol. 7, no. 2, 2021.
[2] Kakelli Anil Kumar, A. Raman, C. Gupta, and R. R. Pillai, "The Recent Trends in Malware Evolution, Detection and Analysis for Android Devices," Journal of Engineering Science and Technology Review, vol. 13, no. 4, pp. 240-248, 2020.
[3] Kevin Allix, Tegawend´e F. Bissyand´e, J. Klein, and Y. L. Traon, "Are Your Training Datasets Yet Relevant?An Investigation into the Importance of Timeline in Machine Learning-based Malware Detection," 2015: Springer International Publishing, in Engineering Secure Software and Systems, pp. 51-67.
[4] KASPERSKY LAB, "Machine Learning Methods for Malware Detection," 2020.
[5] Zeliang Kan, Feargus Pendlebury, Fabio Pierazzi, and L. Cavallaro, "Investigating Labelless Drift Adaptation for Malware Detection," presented at the Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security (AISec ’21), 2021.
[6] Jiayun Xu, Yingjiu Li, Robert H. Deng, and K. Xu, "SDAC: A Slow-Aging Solution for Android Malware Detection Using Semantic Distance Based API Clustering," IEEE Transactions on Dependable and Secure Computing, vol. 19, no. 2, pp. 1149-1163, 2020.
[7] Xiaohan Zhang et al., "Enhancing State-of-the-art Classifiers with API Semantics to Detect Evolved Android Malware," presented at the ACM SIGSAC Conference on Computer and Communications Security (CCS ’20), 2020.
[8] Sean Park, Iqbal Gondal, Joarder Kamruzzaman, and Leo Zhang, "One-Shot Malware Outbreak Detection using Spatio-Temporal Isomorphic Dynamic Features," presented at the 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), 2019.
[9] Borja Sanz, Igor Santos, Carlos Laorden, Xabier Ugarte-Pedrero, Pablo Garcia Bringas, and G. Alvarez, "PUMA: Permission Usage to Detect Malware in Android," presented at the International Joint Conference CISIS’12-ICEUTE´12-SOCO´12 Special Sessions, 2013.
[10] Naser Peiravian and X. Zhu, "Machine Learning for Android Malware Detection Using Permission and API Calls," presented at the IEEE 25th International Conference on Tools with Artificial Intelligence, 2013.
[11] 邱柏嘉, "結合多特徵及深度學習擴增技術提升Android小樣本惡意家族分類能力," 碩士論文, 資訊管理學系, 國立中央大學, 2021.
[12] "Cambridge Dictionary: sustainability." https://dictionary.cambridge.org/zht/%E8%A9%9E%E5%85%B8/%E8%8B%B1%E8%AA%9E/sustainability (accessed 2022).
[13] Daniel Arp, Michael Spreitzenbarth, Malte Huebner, Hugo Gascon, and Konrad Rieck, "Drebin: Efficient and Explainable Detection of Android Malware in Your Pocket," presented at the 21th Annual Network and Distributed System Security Symposium (NDSS), 2014.
[14] Fengguo Wei, Yuping Li, Sankardas Roy, Xinming Ou, and W. Zhou, "Deep Ground Truth Analysis of Current Android Malware," presented at the International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, 2017.
[15] Samaneh Mahdavifar, Andi Fitriah Abdul Kadir, Rasool Fatemi, Dima Alhadidi, and A. A. Ghorbani, "Dynamic Android Malware Category Classification using Semi-Supervised Deep Learning," presented at the 18th IEEE International Conference on Dependable, Autonomic, and Secure Computing (DASC), 2020.
[16] "VirusShare.com - Because Sharing is Caring." https://virusshare.com/ (accessed 2022).
[17] "VirusTotal." https://www.virustotal.com (accessed 2022).
[18] Kevin Allix, Tegawendé F. Bissyandé, Jacques Klein, and Yves Le Traon, "AndroZoo: Collecting Millions of Android Apps for the Research Community," in Proceedings of the 13th International Conference on Mining Software Repositories, 2016: ACM, pp. 468-471.
[19] Ke Xu, Yingjiu Li, Robert H. Deng, Kai Chen, and Jiayun Xu, "DroidEvolver: Self-Evolving Android Malware Detection System," presented at the IEEE European Symposium on Security and Privacy (EuroS&P), 2019.
[20] Feargus Pendlebury, Fabio Pierazzi, Roberto Jordaney, Johannes Kinder, and Lorenzo Cavallaro, "TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time," presented at the Proceedings of the 28th USENIX Security Symposium, 2019.
[21] Z. Q. Tao Lei, Zhibo Wang, Qi Li, and D. Ye, "EveDroid: Event-Aware Android Malware Detection Against Model Degrading for IoT Devices," IEEE Internet of Things Journal, vol. 6, 2019.
[22] "Monkey." https://developer.android.com/studio/test/other-testing-tools/monkey (accessed 2022).
[23] "Androguard." https://github.com/androguard/androguard (accessed 2022).
[24] "Soot." http://soot-oss.github.io/soot/ (accessed 2022).
[25] "Apktool." https://ibotpeaches.github.io/Apktool/ (accessed 2022).
[26] HAIPENG CAI, "Assessing and Improving Malware Detection Sustainability through App Evolution Studies," ACM Transactions on Software Engineering and Methodology, vol. 29, no. 2, 2020, Art no. 8.
[27] Alejandro Guerra-Manzanares, Hayretdin Bahsi, and S. Nõmm, "KronoDroid: Time-based Hybrid-featured Dataset for Effective Android Malware Detection and Characterization," Computers & Security, vol. 110, 2021.
[28] Rahul Pandita, Xusheng Xiao, Wei Yang, William Enck, and Tao Xie, "WHYPER: Towards Automating Risk Assessment of Mobile Applications," in Proceedings of the 22nd USENIX Security Symposium, 2013.
[29] JOSHUA GARCIA, MAHMOUD HAMMAD, and SAM MALEK, "Lightweight, Obfuscation-Resilient Detection and Family Identification of Android Malware," ACM Transactions on Software Engineering and Methodology, vol. 26, 2018, Art no. 3.
[30] BOZHI WU et al., "Why an Android App Is Classified as Malware: Toward Malware Classification Interpretation," ACM Transactions on Software Engineering and Methodology, vol. 30, 2021, Art no. 2.
[31] Ming Fan et al., "Android Malware Familial Classification and Representative Sample Selection via Frequent Subgraph Analysis," IEEE Transactions on Information Forensics and Security, vol. 13, 2018, Art no. 8.
[32] 葉季霏, "一種多特徵 RGB 圖像表示法結合深度學習之 Android惡意軟體偵測研究," 碩士論文, 資訊管理學系, 國立中央大學, 2021.
[33] Roberto Jordaney et al., "Transcend: Detecting Concept Drift in Malware Classification Models," in Open access to the Proceedings of the 26th USENIX Security Symposium, 2017.
[34] Enrico Mariconti, Lucky Onwuzurike, Panagiotis Andriotis, Emiliano De Cristofaro, Gordon Ross, and and Gianluca Stringhini, "MAMADROID: Detecting Android Malware by Building Markov Chains of Behavioral Models," presented at the Network and Distributed System Security (NDSS) Symposium, 2017.
[35] "Android權限官方列表." https://developer.android.com/reference/android/Manifest.permission (accessed 2022).
[36] Kathy Wain Yee Au, Yi Fan Zhou, Zhen Huang, and David Lie, "PScout: analyzing the Android permission specification," presented at the CCS ′12: Proceedings of the 2012 ACM Conference on Computer and Communications Security, 2012.
[37] https://bitbucket.org/gianluca_students/mamadroid_code/src/master/ (accessed 2022).
[38] Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, and Patrick McDaniel, "Adversarial Examples for Malware Detection," presented at the Computer Security – ESORICS 2017, 2017. |