{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:25:07Z","timestamp":1775471107738,"version":"3.50.1"},"reference-count":57,"publisher":"Tech Science Press","issue":"3","license":[{"start":{"date-parts":[[2024,12,29]],"date-time":"2024-12-29T00:00:00Z","timestamp":1735430400000},"content-version":"vor","delay-in-days":363,"URL":"https:\/\/doi.org\/10.32604\/TSP-CROSSMARKPOLICY"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2024]]},"DOI":"10.32604\/cmc.2024.058168","type":"journal-article","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T02:31:27Z","timestamp":1733970687000},"page":"4371-4396","update-policy":"https:\/\/doi.org\/10.32604\/tsp-crossmarkpolicy","source":"Crossref","is-referenced-by-count":4,"title":["A Novel Approach for Android Malware Detection Based on Intelligent Computing"],"prefix":"10.32604","volume":"81","author":[{"given":"Manh Vu","family":"Minh","sequence":"first","affiliation":[]},{"given":"Cho Do","family":"Xuan","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2024]]},"reference":[{"key":"ref1","unstructured":"\u201cMobile operating system market share worldwide,\u201d StatCounter Global Stats. Accessed: May 06, 2024. [Online]. Available: https:\/\/gs.statcounter.com\/os-market-share\/mobile\/worldwide\/"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"371","DOI":"10.4018\/978-1-7998-5348-0.ch020","author":"Yamaguchi","year":"2021","journal-title":"Research Anthology on Combating Denial-of-Service Attacks"},{"key":"ref3","unstructured":"\u201cMobile malware statistics, Q3 2023,\u201d Accessed: May 06, 2024. [Online]. Available: https:\/\/securelist.com\/it-threat-evolution-q3-2023-mobile-statistics\/111224\/"},{"key":"ref4","doi-asserted-by":"crossref","DOI":"10.1080\/17517575.2021.2023764","article-title":"A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system","volume":"17","author":"Gaurav","year":"Mar. 2023, Art. no. 2023764","journal-title":"Enterp Inf. Syst."},{"key":"ref5","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.neucom.2020.10.054","article-title":"Learning features from enhanced function call graphs for Android malware detection","volume":"423","author":"Cai","year":"Jan. 2021","journal-title":"Neurocomputing"},{"key":"ref6","article-title":"GDroid: Android malware detection and classification with graph convolutional network","volume":"106","author":"Gao","year":"Jul. 2021, Art. no. 102264","journal-title":"Comput. Secur."},{"key":"ref7","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121617","article-title":"Android malware detection method based on graph attention networks and deep fusion of multimodal features","volume":"237","author":"Chen","year":"Mar. 2024, Art. no. 121617","journal-title":"Expert. Syst. Appl."},{"key":"ref8","article-title":"Malware detection based on graph attention networks for intelligent transportation systems","volume":"10","author":"Catal","year":"Oct. 2021, Art. no. 2534","journal-title":"Electronics"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"3216","DOI":"10.1109\/TII.2017.2789219","article-title":"Significant permission identification for machine-learning-based android malware detection","volume":"14","author":"Li","year":"Jul. 2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref10","doi-asserted-by":"crossref","first-page":"4903","DOI":"10.1007\/s00521-021-05875-1","article-title":"A novel permission-based Android malware detection system using feature selection based on linear regression","volume":"35","author":"\u015eahin","year":"Mar. 2023","journal-title":"Neural Comput. Appl."},{"key":"ref11","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.cose.2016.11.007","article-title":"AndroDialysis: Analysis of android intent effectiveness in malware detection","volume":"65","author":"Feizollah","year":"Mar. 2017","journal-title":"Comput. Secur."},{"key":"ref12","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.cose.2017.03.011","article-title":"PIndroid: A novel Android malware detection system using ensemble learning methods","volume":"68","author":"Idrees","year":"Jul. 2017","journal-title":"Comput. Secur."},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"Y. Aafer, W. Du, and H. Yin, \u201cDroidAPIMiner: Mining API-level features for robust malware detection in android,\u201d presented at the Secur. Priv. Commun. Netw. Conf., Cham, Switzerland, Sep. 2013, pp. 86\u2013103.","DOI":"10.1007\/978-3-319-04283-1_6"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"14:1","DOI":"10.1145\/3313391","article-title":"MaMaDroid: Detecting android malware by building markov chains of behavioral models (Extended version)","volume":"22","author":"Onwuzurike","year":"Apr. 2019","journal-title":"ACM Trans. Priv. Secur."},{"key":"ref15","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2023.103651","article-title":"A novel Android malware detection method with API semantics extraction","volume":"137","author":"Yang","year":"Feb. 2024, Art. no. 103651","journal-title":"Comput. Secur."},{"key":"ref16","unstructured":"M. C. Grace, Y. Zhou, Z. Wang, and X. Jiang, \u201cSystematic detection of capability leaks in stock android smartphones,\u201d presented at the 19th Ann. Symp. Netw. Distrib. Syst. Secur. (NDSS), San Diego, CA, USA, Feb. 5\u20138, 2012."},{"key":"ref17","unstructured":"X. Chen et al., \u201cAndroid HIV: A study of repackaging malware for evading machine-learning detection,\u201d 2021, arXiv:1808.04218v4."},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"V. K. Vinayaka and C. D. Jaidhara, \u201cAndroid malware detection using function call graph with graph convolutional networks,\u201d presented at the 2nd Int. Conf. Secur. Cyber Comput. Commun. (ICSCCC), Jalandhar, India, May 10\u201312, 2021, pp. 279\u2013287.","DOI":"10.1109\/ICSCCC51823.2021.9478141"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"P. Feng, J. Ma, T. Li, X. Ma, N. Xi and D. Lu, \u201cAndroid malware detection based on call graph via graph neural network,\u201d presented at the Int. Conf. Netw. Netw. Appl. (NaNA), Haikou, China, Dec. 8\u201310, 2020, pp. 368\u2013374.","DOI":"10.1109\/NaNA51271.2020.00069"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"P. Xu, C. Eckert, and A. Zarras, \u201cDetecting and categorizing Android malware with graph neural networks,\u201d presented at the 36th Ann. ACM Symp. Appl. Comput. (SAC), New York, NY, USA, Apr. 22\u201326, 2021, pp. 22\u201326.","DOI":"10.1145\/3412841.3442080"},{"key":"ref21","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121125","article-title":"SeGDroid: An Android malware detection method based on sensitive function call graph learning","volume":"235","author":"Liu","year":"Jan. 2024, Art. no. 121125","journal-title":"Expert. Syst. Appl."},{"key":"ref22","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2024.103807","article-title":"GSEDroid: GNN-based Android malware detection framework using lightweight semantic embedding","volume":"140","author":"Gu","year":"May 2024, Art. no. 103807","journal-title":"Comput. Secur."},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"T. Bhatia and R. Kaushal, \u201cMalware detection in android based on dynamic analysis,\u201d presented at the Int. Conf. Cyber Securi. Protect. Dig. Serv. (Cyber Secur.), London, UK, Jun. 19\u201320, 2017, pp. 1\u20136.","DOI":"10.1109\/CyberSecPODS.2017.8074847"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s11416-014-0226-7","article-title":"Identifying Android malware using dynamically obtained features","volume":"11","author":"Afonso","year":"Feb. 2015","journal-title":"J. Comput. Virol. Hacking Tech."},{"key":"ref25","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1109\/TIFS.2018.2879302","article-title":"DroidCat: Effective android malware detection and categorization via app-level profiling","volume":"14","author":"Cai","year":"Jun. 2019","journal-title":"IEEE Trans. Inf. Forens. Secur"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"A. Zulkifli, I. R. A. Hamid, W. Md Shah, and Z. Abdullah, \u201cAndroid malware detection based on network traffic using decision tree algorithm,\u201d presented at the Int. Conf. Adv. Comput. Sci. Inf. Technol. (ACSIT), Kuching, Malaysia, Dec. 10\u201311, 2016, pp. 485\u2013494.","DOI":"10.1007\/978-3-319-72550-5_46"},{"key":"ref27","doi-asserted-by":"crossref","unstructured":"Q. He, Z. Chen, A. Yan, L. Peng, C. Zhao and Y. Shi, \u201cTrafficPSSF: A fast and effective malware detection under online and offline conditions,\u201d presented at the Int. Conf. Secur., Pattern Anal., Cybernet. (SPAC), Wuhan, China, Dec. 15\u201317, 2018, pp. 14\u201319.","DOI":"10.1109\/SPAC46244.2018.8965602"},{"key":"ref28","article-title":"A TAN based hybrid model for android malware detection","volume":"54","author":"Surendran","year":"Oct. 2020, Art. no. 102483","journal-title":"J. Inf. Secur. Appl."},{"key":"ref29","doi-asserted-by":"crossref","first-page":"4321","DOI":"10.1109\/ACCESS.2018.2792941","article-title":"SAMADroid: A novel 3-level hybrid malware detection model for android operating system","volume":"6","author":"Arshad","year":"2018","journal-title":"IEEE Access"},{"key":"ref30","doi-asserted-by":"crossref","unstructured":"S. Chen, M. Xue, Z. Tang, L. Xu, and H. Zhu, \u201cStormDroid: A streaminglized machine learning-based system for detecting android malware,\u201d presented at the 11th ACM Asia Conf. Comput. Commun. Secur. (ASIA CCS), New York, NY, USA, May 30\u2013Jun. 3, 2016, pp. 377\u2013388.","DOI":"10.1145\/2897845.2897860"},{"key":"ref31","doi-asserted-by":"crossref","unstructured":"Z. Li and Y. Zhou, \u201cEnsemble-learning-based android malware detection using hybrid features,\u201d presented at the Int. Conf. Innov. Artif. Intell. (ICIAI \u201924), New York, NY, USA, Aug. 5\u20137, 2024, pp. 154\u2013158.","DOI":"10.1145\/3655497.3655504"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1007\/s11277-024-11366-y","article-title":"Hybrid deep learning approach based on LSTM and CNN for malware detection","volume":"136","author":"Thakur","year":"Jun. 2024","journal-title":"Wirel Pers. Commun."},{"key":"ref33","unstructured":"Androguard. Accessed: May 06, 2024. [Online]. Available: https:\/\/github.com\/androguard\/androguard"},{"key":"ref34","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/0378-8733(78)90021-7","article-title":"Centrality in social networks conceptual clarification","volume":"1","author":"Freeman","year":"Jan. 1978","journal-title":"Soc. Netw."},{"key":"ref35","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9781139088510","author":"Zafarani","year":"2014","journal-title":"Social Media Mining: An Introduction"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1038\/30918","article-title":"Collective dynamics of \u2018small-world\u2019 networks","volume":"393","author":"Watts","year":"Jun. 1998","journal-title":"Nature"},{"key":"ref37","unstructured":"T. Mikolov, K. Chen, G. Corrado, and J. Dean, \u201cEfficient estimation of word representations in vector space,\u201d Sep. 6, 2013, arXiv:1301.3781."},{"key":"ref38","unstructured":"X. Rong, \u201cWord2vec parameter learning explained,\u201d Jun. 5, 2016. doi: 10.48550\/arXiv.1411.2738."},{"key":"ref39","unstructured":"T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, \u201cDistributed representations of words and phrases and their compositionality,\u201d Oct. 16, 2013. doi: 10.48550\/arXiv.1310.4546."},{"key":"ref40","doi-asserted-by":"crossref","DOI":"10.1080\/23311916.2023.2221962","article-title":"A new approach to software vulnerability detection based on CPG analysis","volume":"10","author":"Xuan","year":"Dec. 2023, Art. no. 2221962","journal-title":"Cogent Eng."},{"key":"ref41","doi-asserted-by":"crossref","first-page":"14005","DOI":"10.1007\/s10489-021-03138-z","article-title":"A new approach for APT malware detection based on deep graph network for endpoint systems","volume":"52","author":"Xuan","year":"Sep. 2022","journal-title":"Appl. Intell."},{"key":"ref42","unstructured":"W. L. Hamilton, R. Ying, and J. Leskovec, \u201cInductive representation learning on large graphs,\u201d Sep. 10, 2018. doi: 10.48550\/arXiv.1706.02216."},{"key":"ref43","unstructured":"M. Zhang and Y. Chen, \u201cLink prediction based on graph neural networks,\u201d presented at the 32nd Conf. Neur. Inf. Process. Syst. (NeurIPS), Montr\u00e9al, Canada, Dec. 3\u20138, 2018, pp. 5165\u20135175."},{"key":"ref44","article-title":"Graph neural networks in node classification: Survey and evaluation","volume":"33","author":"Xiao","year":"Nov. 2021","journal-title":"Mach Vis. Appl."},{"key":"ref45","doi-asserted-by":"crossref","unstructured":"D. He et al., \u201cCommunity-centric graph convolutional network for unsupervised community detection,\u201d presented at the 29th Int. Joint Conf. Artif. Intell., Yokohama, Japan, Jul. 11\u201317, 2020, pp. 3515\u20133521.","DOI":"10.24963\/ijcai.2020\/486"},{"key":"ref46","unstructured":"M. Balcilar, G. Renton, P. Heroux, B. Gauzere, S. Adam and P. Honeine, \u201cBridging the gap between spectral and spatial domains in graph neural networks,\u201d Mar. 25, 2020. doi: 10.48550\/arXiv.2003.11702."},{"key":"ref47","doi-asserted-by":"crossref","first-page":"145:1","DOI":"10.1145\/3638240","article-title":"A survey of recent advances in deep learning models for detecting malware in desktop and mobile platforms","volume":"56","author":"Maniriho","year":"Jan. 2024","journal-title":"ACM Comput. Surv."},{"key":"ref48","doi-asserted-by":"crossref","unstructured":"K. Allix, T. F. Bissyand\u00e9, J. Klein, and Y. Le Traon, \u201cAndroZoo: Collecting millions of Android apps for the research community,\u201d presented at the 13th Int. Conf. Min. Softw. Reposit., Austin, TX, USA, May 14\u201315, 2016, pp. 468\u2013471.","DOI":"10.1145\/2901739.2903508"},{"key":"ref49","unstructured":"\u201cVirusShare.com,\u201d Accessed: Sep. 20, 2024. [Online]. Available: https:\/\/virusshare.com\/"},{"key":"ref50","unstructured":"T. N. Kipf and M. Welling, \u201cSemi-supervised classification with graph convolutional networks,\u201d Feb. 22, 2017. doi: 10.48550\/arXiv.1609.02907."},{"key":"ref51","unstructured":"P. Veli\u010dkovi\u0107, G. Cucurull, A. Casanova, A. Romero, P. Li\u00f2 and Y. Bengio, \u201cGraph attention networks,\u201d Feb. 4, 2018. doi: 10.48550\/arXiv.1710.10903."},{"key":"ref52","unstructured":"J. Du, S. Zhang, G. Wu, J. M. F. Moura, and S. Kar, \u201cTopology adaptive graph convolutional networks,\u201d Feb. 11, 2018. doi: 10.48550\/arXiv.1710.10370."},{"key":"ref53","doi-asserted-by":"crossref","unstructured":"N. Daoudi, J. Samhi, A. K. Kabore, K. Allix, T. F. Bissyand\u00e9 and J. Klein, \u201cDexRay: A simple, yet effective deep learning approach to android malware detection based on image representation of bytecode,\u201d presented at the Deployable Mach. Learn. Secur. Def. Conf., Cham, Switzerland, 2021, pp. 81\u2013106.","DOI":"10.1007\/978-3-030-87839-9_4"},{"key":"ref54","doi-asserted-by":"crossref","unstructured":"Y. Wu, X. Li, D. Zou, W. Yang, X. Zhang and H. Jin, \u201cFast market-wide mobile malware scanning by social-network centrality analysis,\u201d presented at the 34th IEEE\/ACM Int. Conf. Autom. Softw. Eng. (ASE), San Diego, CA, USA, Nov. 11\u201315, 2019, pp. 139\u2013150.","DOI":"10.1109\/ASE.2019.00023"},{"key":"ref55","article-title":"NATICUSdroid: A malware detection framework for Android using native and custom permissions","volume":"58","author":"Mathur","year":"May 2021, Art. no. 102696","journal-title":"J. Inf. Secur. Appl."},{"key":"ref56","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1142\/S0218194019500116","article-title":"Permission-based android malware detection system using feature selection with genetic algorithm","volume":"29","author":"Yildiz","year":"Feb. 2019","journal-title":"Int. J. Softw. Eng. Knowl. Eng."},{"key":"ref57","doi-asserted-by":"crossref","unstructured":"N. McLaughlin et al., \u201cDeep android malware detection,\u201d presented at the 7th ACM Conf. Data Appl. Secur. Priv. (CODASPY \u201917), New York, NY, USA, Mar. 22\u201324, 2017, pp. 301\u2013308.","DOI":"10.1145\/3029806.3029823"}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.techscience.com\/cmc\/v81n3\/59055\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T06:10:46Z","timestamp":1741327846000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v81n3\/59055"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":57,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024]]},"published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2024.058168","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"2024-09-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-28","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-19","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}