{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T02:37:40Z","timestamp":1776134260329,"version":"3.50.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T00:00:00Z","timestamp":1688601600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T00:00:00Z","timestamp":1688601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Innovations Syst Softw Eng"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s11334-023-00530-5","type":"journal-article","created":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T08:09:42Z","timestamp":1688630982000},"page":"303-311","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["AndyWar: an intelligent android malware detection using machine learning"],"prefix":"10.1007","volume":"21","author":[{"given":"Sandipan","family":"Roy","sequence":"first","affiliation":[]},{"given":"Samit","family":"Bhanja","sequence":"additional","affiliation":[]},{"given":"Abhishek","family":"Das","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,6]]},"reference":[{"key":"530_CR1","doi-asserted-by":"publisher","first-page":"124579","DOI":"10.1109\/ACCESS.2020.3006143","volume":"8","author":"K Liu","year":"2020","unstructured":"Liu K, Xu S, Xu G, Zhang M, Sun D, Liu H (2020) A review of android malware detection approaches based on machine learning. IEEE Access 8:124579\u2013124607","journal-title":"IEEE Access"},{"issue":"3","key":"530_CR2","first-page":"1","volume":"30","author":"Y Zhao","year":"2021","unstructured":"Zhao Y, Li L, Wang H, Cai H, Bissyand\u00e9 TF, Klein J, Grundy J (2021) On the impact of sample duplication in machine-learning-based android malware detection. ACM Trans Softw Eng Methodol 30(3):1\u201338","journal-title":"ACM Trans Softw Eng Methodol"},{"key":"530_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2020.101861","volume":"112","author":"J Singh","year":"2021","unstructured":"Singh J, Singh J (2021) A survey on machine learning-based malware detection in executable files. J Syst Architect 112:101861","journal-title":"J Syst Architect"},{"issue":"13","key":"530_CR4","doi-asserted-by":"publisher","first-page":"1606","DOI":"10.3390\/electronics10131606","volume":"10","author":"J Senanayake","year":"2021","unstructured":"Senanayake J, Kalutarage H, Al-Kadri MO (2021) Android mobile malware detection using machine learning: a systematic review. Electronics 10(13):1606","journal-title":"Electronics"},{"issue":"4","key":"530_CR5","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1109\/TDSC.2017.2700270","volume":"16","author":"A Demontis","year":"2017","unstructured":"Demontis A, Melis M, Biggio B, Maiorca D, Arp D, Rieck K, Corona I, Giacinto G, Roli F (2017) Yes, machine learning can be more secure! a case study on android malware detection. IEEE Trans Depend Secure Comput 16(4):711\u2013724","journal-title":"IEEE Trans Depend Secure Comput"},{"key":"530_CR6","doi-asserted-by":"crossref","unstructured":"Gunasekera S (2012) Android architecture. In: Android apps security. Springer, pp 1\u201312","DOI":"10.1007\/978-1-4302-4063-1_1"},{"key":"530_CR7","doi-asserted-by":"crossref","unstructured":"Kumar S, Shukla SK (2020) The state of android security. In: Cyber security in India. Springer, pp 17\u201322","DOI":"10.1007\/978-981-15-1675-7_2"},{"key":"530_CR8","doi-asserted-by":"crossref","unstructured":"Bing H (2012) Analysis and research of system security based on android. In: 2012 Fifth international conference on intelligent computation technology and automation. IEEE, pp 581\u2013584","DOI":"10.1109\/ICICTA.2012.152"},{"key":"530_CR9","doi-asserted-by":"crossref","unstructured":"Wu DJ, Mao CH, Wei TE, Lee HM, Wu KP (2012) Droidmat: android malware detection through manifest and API calls tracing. In: 2012 Seventh Asia joint conference on information security. IEEE, pp 62\u201369","DOI":"10.1109\/AsiaJCIS.2012.18"},{"key":"530_CR10","doi-asserted-by":"crossref","unstructured":"Sahs J, Khan L (2012) A machine learning approach to android malware detection. In: 2012 European intelligence and security informatics conference. IEEE, pp 141\u2013147","DOI":"10.1109\/EISIC.2012.34"},{"issue":"2","key":"530_CR11","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1109\/TCYB.2017.2777960","volume":"49","author":"SY Yerima","year":"2018","unstructured":"Yerima SY, Sezer S (2018) Droidfusion: a novel multilevel classifier fusion approach for android malware detection. IEEE Trans Cybern 49(2):453\u2013466","journal-title":"IEEE Trans Cybern"},{"issue":"7","key":"530_CR12","doi-asserted-by":"publisher","first-page":"3216","DOI":"10.1109\/TII.2017.2789219","volume":"14","author":"J Li","year":"2018","unstructured":"Li J, Sun L, Yan Q, Li Z, Srisa-An W, Ye H (2018) Significant permission identification for machine-learning-based android malware detection. IEEE Trans Ind Inf 14(7):3216\u20133225","journal-title":"IEEE Trans Ind Inf"},{"key":"530_CR13","doi-asserted-by":"publisher","unstructured":"Mondal S, Das A (2023) Quality aware cost efficient reward mechanism in mobile crowdsensing system with uncertainty constraints. Microsyst Technol. https:\/\/doi.org\/10.1007\/s00542-023-05495-w","DOI":"10.1007\/s00542-023-05495-w"},{"key":"530_CR14","doi-asserted-by":"publisher","unstructured":"Bhanja S, Metia S, Das A (2022) A hybrid neuro-fuzzy prediction system with butterfly optimization algorithm for PM2.5 forecasting. Microsyst Technol 28:2577\u20132592. https:\/\/doi.org\/10.1007\/s00542-022-05252-5","DOI":"10.1007\/s00542-022-05252-5"},{"key":"530_CR15","doi-asserted-by":"publisher","unstructured":"Bhanja S, Metia S, Das A (2022) A black swan events based hybrid model for indian stock markets\u2019 trends prediction. Innovations Syst Softw Eng. https:\/\/doi.org\/10.1007\/s11334-021-00428-0","DOI":"10.1007\/s11334-021-00428-0"},{"key":"530_CR16","doi-asserted-by":"publisher","unstructured":"Mondal S, Das A (2022) Energy efficient and secure healthcare data transmission in the internet of medical things network. Microsyst Technol 29:539\u2013551. https:\/\/doi.org\/10.1007\/s00542-022-05398-2","DOI":"10.1007\/s00542-022-05398-2"},{"key":"530_CR17","doi-asserted-by":"crossref","unstructured":"Zhao M, Ge F, Zhang T, Yuan Z (2011) Antimaldroid: an efficient SVM-based malware detection framework for android. In: International conference on information computing and applications. Springer, pp 158\u2013166","DOI":"10.1007\/978-3-642-27503-6_22"},{"key":"530_CR18","doi-asserted-by":"crossref","unstructured":"Baldini G, Geneiatakis D (2019) A performance evaluation on distance measures in KNN for mobile malware detection. In: 2019 6th international conference on control, decision and information technologies (CoDIT). IEEE, pp 193\u2013198","DOI":"10.1109\/CoDIT.2019.8820510"},{"key":"530_CR19","doi-asserted-by":"crossref","unstructured":"Masum M, Shahriar H (2019) Droid-nnet: deep learning neural network for android malware detection. In: 2019 IEEE international conference on big data (Big Data). IEEE, pp 5789\u20135793","DOI":"10.1109\/BigData47090.2019.9006053"},{"key":"530_CR20","doi-asserted-by":"crossref","unstructured":"Utku A, Do\u011fru \u0130A, Akcayol MA (2018) Decision tree based android malware detection system. In: 2018 26th Signal processing and communications applications conference (SIU). IEEE, pp 1\u20134","DOI":"10.1109\/SIU.2018.8404151"},{"key":"530_CR21","doi-asserted-by":"crossref","unstructured":"Aslam M, Ye D, Hanif M, Asad M (2020) Adaptive machine learning: a framework for active malware detection. In: 2020 16th International conference on mobility, sensing and networking (MSN). IEEE, pp 57\u201364","DOI":"10.1109\/MSN50589.2020.00025"},{"issue":"1","key":"530_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10922-021-09634-4","volume":"30","author":"S Mahdavifar","year":"2022","unstructured":"Mahdavifar S, Alhadidi D, Ghorbani A et al (2022) Effective and efficient hybrid android malware classification using pseudo-label stacked auto-encoder. J Netw Syst Manag 30(1):1\u201334","journal-title":"J Netw Syst Manag"},{"key":"530_CR23","doi-asserted-by":"publisher","first-page":"14246","DOI":"10.1109\/ACCESS.2022.3146363","volume":"10","author":"D\u00d6 \u015eah\u0131n","year":"2022","unstructured":"\u015eah\u0131n D\u00d6, Akleylek S, Kili\u00e7 E (2022) Linregdroid: detection of android malware using multiple linear regression models-based classifiers. IEEE Access 10:14246\u201314259","journal-title":"IEEE Access"},{"key":"530_CR24","doi-asserted-by":"crossref","unstructured":"Lashkari AH, Kadir AFA, Taheri L, Ghorbani AA (2018) Toward developing a systematic approach to generate benchmark android malware datasets and classification. In: 2018 International Carnahan conference on security technology (ICCST). IEEE, pp 1\u20137","DOI":"10.1109\/CCST.2018.8585560"},{"key":"530_CR25","doi-asserted-by":"crossref","unstructured":"Mahdavifar S, Kadir AFA, Fatemi R, Alhadidi D, Ghorbani AA (2020) Dynamic android malware category classification using semi-supervised deep learning. In: 2020 IEEE international conference on dependable, autonomic and secure computing, international conference on pervasive intelligence and computing, international conference on cloud and big data computing, international conference on cyber science and technology congress (DASC\/PiCom\/CBDCom\/CyberSciTech). IEEE, pp 515\u2013522","DOI":"10.1109\/DASC-PICom-CBDCom-CyberSciTech49142.2020.00094"},{"key":"530_CR26","doi-asserted-by":"crossref","unstructured":"Wong MY, Lie D (2016) Intellidroid: a targeted input generator for the dynamic analysis of android malware. In: NDSS, vol\u00a016, pp 21\u201324","DOI":"10.14722\/ndss.2016.23118"},{"key":"530_CR27","doi-asserted-by":"crossref","unstructured":"Lazarescu M, Bunke H, Venkatesh S (2000) Graph matching: fast candidate elimination using machine learning techniques. In: Joint IAPR international workshops on statistical techniques in pattern recognition (SPR) and structural and syntactic pattern recognition (SSPR). Springer, pp 236\u2013245","DOI":"10.1007\/3-540-44522-6_25"},{"key":"530_CR28","unstructured":"For Cybersecurity CI (2022) Dataset. https:\/\/www.unb.ca\/cic\/datasets\/index.html"}],"container-title":["Innovations in Systems and Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11334-023-00530-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11334-023-00530-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11334-023-00530-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,8]],"date-time":"2025-03-08T02:40:31Z","timestamp":1741401631000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11334-023-00530-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,6]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["530"],"URL":"https:\/\/doi.org\/10.1007\/s11334-023-00530-5","relation":{},"ISSN":["1614-5046","1614-5054"],"issn-type":[{"value":"1614-5046","type":"print"},{"value":"1614-5054","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,6]]},"assertion":[{"value":"3 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors report no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}