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                     classification models. In: Proc. of the 26th USENIX Security Symp. Vancouver: USENIX Association, 2017. 625–642.
                  [7]  Zhang  XH,  Zhang  Y,  Zhong  M,  Ding  DZ,  Cao  YZ,  Zhang  YK,  Zhang  M,  Yang  M.  Enhancing  state-of-the-art  classifiers  with  API
                     semantics to detect evolved Android malware. In: Proc. of the 2020 ACM SIGSAC Conf. on Computer and Communications Security.
                     ACM, 2020. 757–770. [doi: 10.1145/3372297.3417291]
                  [8]  Arp D, Spreitzenbarth M, Hübner M, Gascon H, Rieck K. DREBIN: Effective and explainable detection of Android malware in your
                     pocket. In: Proc. of the 21st Annual Network and Distributed System Security Symp. San Diego: The Internet Society, 2014. 1–15.
                  [9]  Aafer Y, Du WL, Yin H. DroidAPIMiner: Mining API-level features for robust malware detection in Android. In: Proc. of the 9th Int’l
                     ICST Conf. on Security and Privacy in Communication Networks. Sydney: Springer, 2013. 86–103. [doi: 10.1007/978-3-319-04283-1_6]
                 [10]  Feng RT, Chen S, Xie XF, Meng GZ, Lin SW, Liu Y. A performance-sensitive malware detection system using deep learning on mobile
                     devices. IEEE Trans. on Information Forensics and Security, 2021, 16: 1563–1578. [doi: 10.1109/TIFS.2020.3025436]
                 [11]  Allen J, Landen M, Chaba S, Ji Y, Chung SPH, Lee W. Improving accuracy of Android malware detection with lightweight contextual
                     awareness. In: Proc. of the 34th Annual Computer Security Applications Conf. San Juan: ACM, 2018. 210–221. [doi: 10.1145/3274694.
                     3274744]
                 [12]  Wu YM, Li XD, Zou DQ, Yang W, Zhang X, Jin H. MalScan: Fast market-wide mobile malware scanning by social-network centrality
                     analysis. In: Proc. of the 34th IEEE/ACM Int’l Conf. on Automated Software Engineering. San Diego: IEEE, 2019. 139–150. [doi: 10.
                     1109/ASE.2019.00023]
                 [13]  Mariconti E, Onwuzurike L, Andriotis P, de Cristofaro E, Ross G, Stringhini G. Mamadroid: Detecting android malware by building
                     Markov chains of behavioral models. In: Proc. of the 24th Annual Network and Distributed System Security Symp. San Diego: The
                     Internet Society, 2017. 1–16.
                 [14]  Lei T, Qin Z, Wang ZB, Li Q, Ye DP. EveDroid: Event-aware android malware detection against model degrading for IoT devices. IEEE
                     Internet of Things Journal, 2019, 6(4): 6668–6680. [doi: 10.1109/JIOT.2019.2909745]
                 [15]  Lau JH, Baldwin T. An empirical evaluation of doc2vec with practical insights into document embedding generation. In: Proc. of the 1st
                     Workshop on Representation Learning for NLP. Berlin: Association for Computational Linguistics, 2016. 78–86. [doi: 10.18653/v1/W16-
                     1609]
                 [16]  Au KWY, Zhou YF, Huang Z, Lie D. PScout: Analyzing the Android permission specification. In: Proc. of the 2012 ACM Conf. on
                     Computer and Communications Security. Raleigh North: ACM, 2012. 217–228. [doi: 10.1145/2382196.2382222]
                 [17]  Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O. Translating embeddings for modeling multi-relational data. In: Proc. of
                     the 26th Int’l Conf. on Neural Information Processing Systems. Lake Tahoe: Curran Associates Inc., 2013. 2787–2795.
                 [18]  Xu  JY,  Li  YJ,  Deng  RH,  Xu  K.  SDAC:  A  slow-aging  solution  for  Android  malware  detection  using  semantic  distance  based  API
                     clustering. IEEE Trans. on Dependable and Secure Computing, 2022, 19(2): 1149–1163. [doi: 10.1109/TDSC.2020.3005088]
                 [19]  Arp  D,  Quiring  E,  Pendlebury  F,  Pendlebury  F,  Warnecke  A,  Pierazzi  F,  Wressnegger  C,  Cavallaro  L,  Rieck  K.  Dos  and  don’ts  of
                     machine learning in computer security. In: Proc. of the 31st USENIX Security Symp. Boston: USENIX Association, 2022. 3971–3988.
                 [20]  Narayanan A, Liu Y, Chen LH, Liu JL. Adaptive and scalable Android malware detection through online learning. In: Proc. of the 2016
                     Int’l Joint Conf. on Neural Networks. Vancouver: IEEE, 2016. 2484–2491. [doi: 10.1109/IJCNN.2016.7727508]
                 [21]  Xu K, Li YJ, Deng R, Chen K, Xu JY. DroidEvolver: Self-evolving Android malware detection system. In: Proc. of the 2019 IEEE
                     European Symp. on Security and Privacy. Stockholm: IEEE, 2019. 47–62. [doi: 10.1109/EuroSP.2019.00014]
                 [22]  Gu YH, Li LX. GraphEvolveDroid: Mitigate model degradation in the scenario of Android ecosystem evolution. In: Proc. of the 30th
                     ACM Int’l Conf. on Information & Knowledge Management. ACM, 2021. 3588–3591.
                 [23]  Hei  YM,  Yang  RY,  Peng  H,  Wang  LH,  Xu  XL,  Liu  JW,  Liu  H,  Xu  J,  Sun  LC.  Hawk:  Rapid  Android  malware  detection  through
                     heterogeneous graph attention networks. IEEE Trans. on Neural Networks and Learning Systems, 2024, 35(4): 4703–4717. [doi: 10.1109/
                     TNNLS.2021.3105617]
                 [24]  Yuan C, Cai JX, Tian DH, Ma R, Jia XQ, Liu WM. Towards time evolved malware identification using two-head neural network. Journal
                     of Information Security and Applications, 2022, 65: 103098. [doi: 10.1016/j.jisa.2021.103098]
                 [25]  Karbab  EB,  Debbabi  M.  PetaDroid:  Adaptive  android  malware  detection  using  deep  learning.  In:  Proc.  of  the  18th  Int’l  Conf.  on
                     Detection of Intrusions and Malware, and Vulnerability Assessment. Springer, 2021. 319–340. [DOI: 10.1007/978-3-030-80825-9_16]
                 [26]  Devlin J, Chang MW, Lee K, Toutanova K. BERT: Pre-training of deep bidirectional Tansformers for language understanding. In: Proc.
                     of the 2019 Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
                     Minneapolis: Association for Computational Linguistics, 2019. 4171–4186. [doi: 10.18653/v1/n19-1423]
                 [27]  Syakur MA, Khotimah BK, Rochman EMS, Satoto BD. Integration k-means clustering method and elbow method for identification of the
                     best customer profile cluster. IOP Conf. Series: Materials Science and Engineering, 2018, 336: 012017. [doi 10.1088/1757-899X/336/1/
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