{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T21:06:53Z","timestamp":1774991213241,"version":"3.50.1"},"reference-count":117,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,10,23]],"date-time":"2021-10-23T00:00:00Z","timestamp":1634947200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,10,23]],"date-time":"2021-10-23T00:00:00Z","timestamp":1634947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s00521-021-06597-0","type":"journal-article","created":{"date-parts":[[2021,10,23]],"date-time":"2021-10-23T13:02:41Z","timestamp":1634994161000},"page":"1007-1032","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Applications of deep learning for mobile malware detection: A systematic literature review"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0959-2930","authenticated-orcid":false,"given":"Cagatay","family":"Catal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G\u00f6rkem","family":"Giray","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bedir","family":"Tekinerdogan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,23]]},"reference":[{"key":"6597_CR1","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.jnca.2016.08.022","volume":"75","author":"MF Ab Razak","year":"2016","unstructured":"Ab Razak MF, Anuar NB, Salleh R, Firdaus A (2016) The rise of \u201cmalware\u201d: bibliometric analysis of malware study. J Netw Comput Appl 75:58\u201376","journal-title":"J Netw Comput Appl"},{"key":"6597_CR2","doi-asserted-by":"crossref","unstructured":"Ali NB, Petersen K (2014) Evaluating strategies for study selection in systematic literature studies. In: Proceedings of the 8th ACM\/IEEE international symposium on empirical software engineering and measurement pp. 1\u20134","DOI":"10.1145\/2652524.2652557"},{"key":"6597_CR3","unstructured":"Antonakakis M, April T, Bailey M, Bernhard M, Bursztein E, Cochran J, Durumeric Z, Halderman JA, Invernizzi L, Kallitsis M, Kumar D (2017) Understanding the mirai botnet. In: 26th {USENIX} security symposium ({USENIX} Security 17) pp. 1093\u20131110"},{"key":"6597_CR4","unstructured":"AppBrain, \u201cNumber of Android apps on Google Play.\u201d [Online]. Available: https:\/\/www.appbrain.com\/stats\/number-of-android-apps. [Accessed: 17-July-2020]."},{"key":"6597_CR5","doi-asserted-by":"crossref","first-page":"6249","DOI":"10.1109\/ACCESS.2019.2963724","volume":"8","author":"\u00d6A Aslan","year":"2020","unstructured":"Aslan \u00d6A, Samet R (2020) A comprehensive review on malware detection approaches. IEEE Access 8:6249\u20136271","journal-title":"IEEE Access"},{"issue":"2","key":"6597_CR6","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","volume":"41","author":"T Baltru\u0161aitis","year":"2018","unstructured":"Baltru\u0161aitis T, Ahuja C, Morency LP (2018) Multimodal machine learning: a survey and taxonomy. IEEE Trans Pattern Anal Mach Intell 41(2):423\u2013443","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6597_CR7","doi-asserted-by":"crossref","unstructured":"Bazrafshan Z, Hashemi H, Fard SMH, Hamzeh A (2013) A survey on heuristic malware detection techniques. In: The 5th conference on information and knowledge technology IEEE, pp. 113\u2013120","DOI":"10.1109\/IKT.2013.6620049"},{"issue":"4","key":"6597_CR8","doi-asserted-by":"crossref","first-page":"122","DOI":"10.3390\/info10040122","volume":"10","author":"DS Berman","year":"2019","unstructured":"Berman DS, Buczak AL, Chavis JS, Corbett CL (2019) A survey of deep learning methods for cyber security. Information 10(4):122","journal-title":"Information"},{"key":"6597_CR9","volume-title":"Deep learning with Python: develop deep learning models on Theano and TensorFlow using Keras","author":"J Brownlee","year":"2016","unstructured":"Brownlee J (2016) Deep learning with Python: develop deep learning models on Theano and TensorFlow using Keras. Machine Learning Mastery, Vermont"},{"key":"6597_CR10","volume-title":"Long Short-term memory networks with Python: develop sequence prediction models with deep learning","author":"J Brownlee","year":"2017","unstructured":"Brownlee J (2017) Long Short-term memory networks with Python: develop sequence prediction models with deep learning. Machine Learning Mastery, Vermont"},{"key":"6597_CR11","volume-title":"Deep learning for computer vision: image classification, object detection, and face recognition in Python","author":"J Brownlee","year":"2019","unstructured":"Brownlee J (2019) Deep learning for computer vision: image classification, object detection, and face recognition in Python. Machine Learning Mastery, Vermont"},{"key":"6597_CR12","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.infsof.2017.10.017","volume":"95","author":"D Budgen","year":"2018","unstructured":"Budgen D, Brereton P, Drummond S, Williams N (2018) Reporting systematic reviews: some lessons from a tertiary study. Inf Softw Technol 95:62\u201374","journal-title":"Inf Softw Technol"},{"issue":"2","key":"6597_CR13","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/MSEC.2019.2920585","volume":"18","author":"D Carlin","year":"2019","unstructured":"Carlin D, Burgess J, O\u2019Kane P, Sezer S (2019) You could be mine (d): the rise of cryptojacking. IEEE Secur Priv 18(2):16\u201322","journal-title":"IEEE Secur Priv"},{"key":"6597_CR14","doi-asserted-by":"crossref","unstructured":"Catal C (2012) On the application of genetic algorithms for test case prioritization: a systematic literature review. In: Proceedings of the 2nd international workshop on Evidential assessment of software technologies pp. 9\u201314.","DOI":"10.1145\/2372233.2372238"},{"issue":"3","key":"6597_CR15","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1007\/s11219-012-9181-z","volume":"21","author":"C Catal","year":"2013","unstructured":"Catal C, Mishra D (2013) Test case prioritization: a systematic mapping study. Software Qual J 21(3):445\u2013478","journal-title":"Software Qual J"},{"key":"6597_CR16","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/978-90-481-8776-8_17","volume-title":"Electronic Engineering and Computing Technology","author":"C Catal","year":"2010","unstructured":"Catal C, Sevim U, Diri B (2010) Metrics-driven software quality prediction without prior fault data. In: Ao SI, Gelman L (eds) Electronic Engineering and Computing Technology. Springer, Dordrecht, pp 189\u2013199"},{"key":"6597_CR17","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.compeleceng.2018.02.043","volume":"67","author":"GR Choudhary","year":"2018","unstructured":"Choudhary GR, Kumar S, Kumar K, Mishra A, Catal C (2018) Empirical analysis of change metrics for software fault prediction. Comput Electr Eng 67:15\u201324","journal-title":"Comput Electr Eng"},{"issue":"7","key":"6597_CR18","doi-asserted-by":"crossref","first-page":"3187","DOI":"10.1109\/TII.2018.2822680","volume":"14","author":"Z Cui","year":"2018","unstructured":"Cui Z, Xue F, Cai X, Cao Y, Wang GG, Chen J (2018) Detection of malicious code variants based on deep learning. IEEE Trans Industr Inf 14(7):3187\u20133196","journal-title":"IEEE Trans Industr Inf"},{"issue":"6","key":"6597_CR19","doi-asserted-by":"crossref","first-page":"e5173","DOI":"10.1002\/cpe.5173","volume":"32","author":"H Darabian","year":"2020","unstructured":"Darabian H, Dehghantanha A, Hashemi S, Homayoun S, Choo KKR (2020) An opcode-based technique for polymorphic Internet of Things malware detection. Concurr Comput Practice Exp 32(6):e5173","journal-title":"Concurr Comput Practice Exp"},{"key":"6597_CR20","doi-asserted-by":"publisher","DOI":"10.1017\/atsip.2013.9","author":"L Deng","year":"2014","unstructured":"Deng L (2014) A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans Signal Inform Process. https:\/\/doi.org\/10.1017\/atsip.2013.9","journal-title":"APSIPA Trans Signal Inform Process"},{"issue":"4","key":"6597_CR21","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1007\/s12239-020-0085-9","volume":"21","author":"Z Du","year":"2020","unstructured":"Du Z, Miao Q, Zong C (2020) Trajectory planning for automated parking systems using deep reinforcement learning. Int J Automot Technol 21(4):881\u2013887","journal-title":"Int J Automot Technol"},{"key":"6597_CR22","doi-asserted-by":"crossref","unstructured":"Elkahky AM, Song Y, He X (2015) A multi-view deep learning approach for cross domain user modeling in recommendation systems. In Proceedings of the 24th international conference on world wide web pp. 278\u2013288","DOI":"10.1145\/2736277.2741667"},{"key":"6597_CR23","doi-asserted-by":"crossref","unstructured":"Farfade SS, Saberian MJ, Li LJ (2015) Multi-view face detection using deep convolutional neural networks. In: Proceedings of the 5th ACM on international conference on multimedia retrieval pp. 643\u2013650","DOI":"10.1145\/2671188.2749408"},{"key":"6597_CR24","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.diin.2015.02.001","volume":"13","author":"A Feizollah","year":"2015","unstructured":"Feizollah A, Anuar NB, Salleh R, Wahab AWA (2015) A review on feature selection in mobile malware detection. Digit Investig 13:22\u201337","journal-title":"Digit Investig"},{"key":"6597_CR25","doi-asserted-by":"crossref","unstructured":"Gay G, Menzies T, Cukic B, Turhan B (2009) How to build repeatable experiments. In: Proceedings of the 5th international conference on predictor models in software engineering pp. 1\u20139","DOI":"10.1145\/1540438.1540460"},{"key":"6597_CR26","doi-asserted-by":"crossref","first-page":"102526","DOI":"10.1016\/j.jnca.2019.102526","volume":"153","author":"D Gibert","year":"2020","unstructured":"Gibert D, Mateu C, Planes J (2020) The rise of machine learning for detection and classification of malware: research developments, trends and challenges. J Network Comput Appl 153:102526","journal-title":"J Network Comput Appl"},{"key":"6597_CR27","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Bengio Y (2014) Generative adversarial nets. Adv Neural Info Process Syst 27"},{"key":"6597_CR28","doi-asserted-by":"crossref","unstructured":"Griffin K, Schneider S, Hu X, Chiueh TC (2009) Automatic generation of string signatures for malware detection. In: International workshop on recent advances in intrusion detection. Springer, Berlin, Heidelberg. pp. 101\u2013120","DOI":"10.1007\/978-3-642-04342-0_6"},{"key":"6597_CR29","doi-asserted-by":"crossref","unstructured":"Hassler E, Carver JC, Kraft NA, Hale D (2014) Outcomes of a community workshop to identify and rank barriers to the systematic literature review process. In: Proceedings of the 18th international conference on evaluation and assessment in software engineering. pp. 1\u201310","DOI":"10.1145\/2601248.2601274"},{"issue":"9","key":"6597_CR30","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263\u20131284","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"6597_CR31","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"6597_CR32","doi-asserted-by":"crossref","first-page":"1863","DOI":"10.1016\/j.procs.2019.09.358","volume":"159","author":"SC Hsiao","year":"2019","unstructured":"Hsiao SC, Kao DY, Liu ZY, Tso R (2019) Malware image classification using one-shot learning with Siamese networks. Proced Comput Sci 159:1863\u20131871","journal-title":"Proced Comput Sci"},{"key":"6597_CR33","doi-asserted-by":"crossref","unstructured":"Jerome Q, Allix K, State R, Engel T (2014) Using opcode-sequences to detect malicious Android applications. In: 2014 IEEE international conference on communications (ICC) IEEE. pp. 914\u2013919","DOI":"10.1109\/ICC.2014.6883436"},{"key":"6597_CR34","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","volume":"147","author":"A Kamilaris","year":"2018","unstructured":"Kamilaris A, Prenafeta-Bold\u00fa FX (2018) Deep learning in agriculture: a survey. Comput Electron Agric 147:70\u201390","journal-title":"Comput Electron Agric"},{"key":"6597_CR35","doi-asserted-by":"crossref","unstructured":"Kitchenham BA, Dyba T, Jorgensen M (2004) Evidence-based software engineering. In: Proceedings. 26th international conference on software engineering IEEE. pp. 273\u2013281","DOI":"10.1109\/ICSE.2004.1317449"},{"issue":"8","key":"6597_CR36","doi-asserted-by":"crossref","first-page":"792","DOI":"10.1016\/j.infsof.2010.03.006","volume":"52","author":"B Kitchenham","year":"2010","unstructured":"Kitchenham B, Pretorius R, Budgen D, Brereton OP, Turner M, Niazi M, Linkman S (2010) Systematic literature reviews in software engineering\u2013a tertiary study. Inf Softw Technol 52(8):792\u2013805","journal-title":"Inf Softw Technol"},{"issue":"1","key":"6597_CR37","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.infsof.2008.09.009","volume":"51","author":"B Kitchenham","year":"2009","unstructured":"Kitchenham B, Pearl Brereton O, Budgen D, Turner M, Bailey J, Linkman S (2009) Systematic literature reviews in software engineering\u2014a systematic literature review. Inf Softw Technol 51(1):7\u201315. https:\/\/doi.org\/10.1016\/j.infsof.2008.09.009","journal-title":"Inf Softw Technol"},{"issue":"2","key":"6597_CR38","first-page":"136","volume":"19","author":"SH Kok","year":"2019","unstructured":"Kok SH, Abdullah A, Jhanjhi NZ, Supramaniam M (2019) Ransomware, threat and detection techniques: a review. Int J Comput Sci Network Secur 19(2):136","journal-title":"Int J Comput Sci Network Secur"},{"issue":"7","key":"6597_CR39","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/MC.2017.201","volume":"50","author":"C Kolias","year":"2017","unstructured":"Kolias C, Kambourakis G, Stavrou A, Voas J (2017) DDoS in the IoT: mirai and other botnets. Computer 50(7):80\u201384","journal-title":"Computer"},{"issue":"2","key":"6597_CR40","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1587\/transinf.2019INI0003","volume":"103","author":"V Kouliaridis","year":"2020","unstructured":"Kouliaridis V, Barmpatsalou K, Kambourakis G, Chen S (2020) A survey on mobile malware detection techniques. IEICE Trans Inf Syst 103(2):204\u2013211","journal-title":"IEICE Trans Inf Syst"},{"key":"6597_CR41","doi-asserted-by":"crossref","unstructured":"Kuznietsov Y, Stuckler J, Leibe B (2017) Semi-supervised deep learning for monocular depth map prediction. In: Proceedings of the IEEE conference on computer vision and pattern recognition pp. 6647\u20136655","DOI":"10.1109\/CVPR.2017.238"},{"issue":"4","key":"6597_CR42","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1109\/TSE.2008.35","volume":"34","author":"S Lessmann","year":"2008","unstructured":"Lessmann S, Baesens B, Mues C, Pietsch S (2008) Benchmarking classification models for software defect prediction: a proposed framework and novel findings. IEEE Trans Softw Eng 34(4):485\u2013496","journal-title":"IEEE Trans Softw Eng"},{"key":"6597_CR43","doi-asserted-by":"crossref","unstructured":"Li R, Wang S, Long Z, Gu D (2018) Undeepvo: monocular visual odometry through unsupervised deep learning. In: 2018 IEEE international conference on robotics and automation (ICRA) IEEE, pp. 7286\u20137291","DOI":"10.1109\/ICRA.2018.8461251"},{"issue":"10","key":"6597_CR44","doi-asserted-by":"crossref","first-page":"1863","DOI":"10.1109\/TKDE.2018.2872063","volume":"31","author":"Y Li","year":"2018","unstructured":"Li Y, Yang M, Zhang Z (2018) A survey of multi-view representation learning. IEEE Trans Knowl Data Eng 31(10):1863\u20131883","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"6597_CR45","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Jeroen Van Der, Laak JA, Van Ginneken B, S\u00e1nchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60\u201388","journal-title":"Med Image Anal"},{"key":"6597_CR46","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3006143","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. https:\/\/doi.org\/10.1109\/ACCESS.2020.3006143","journal-title":"IEEE Access"},{"key":"6597_CR47","doi-asserted-by":"crossref","unstructured":"Liu X, Liu J (2014) A two-layered permission-based android malware detection scheme. In: 2014 2nd IEEE international conference on mobile cloud computing, services, and engineering IEEE, pp. 142\u2013148","DOI":"10.1109\/MobileCloud.2014.22"},{"issue":"2","key":"6597_CR48","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TGRS.2016.2612821","volume":"55","author":"E Maggiori","year":"2016","unstructured":"Maggiori E, Tarabalka Y, Charpiat G, Alliez P (2016) Convolutional neural networks for large-scale remote-sensing image classification. IEEE Trans Geosci Remote Sens 55(2):645\u2013657","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"6597_CR49","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.neucom.2019.02.056","volume":"347","author":"S Mahdavifar","year":"2019","unstructured":"Mahdavifar S, Ghorbani AA (2019) Application of deep learning to cybersecurity: a survey. Neurocomputing 347:149\u2013176","journal-title":"Neurocomputing"},{"key":"6597_CR50","doi-asserted-by":"crossref","unstructured":"McLaughlin N, Martinez del Rincon J, Kang B, Yerima S, Miller P, Sezer S, Safaei Y, Trickel E, Zhao Z, Doup\u00e9 A, Joon Ahn G (2017) Deep android malware detection. In: Proceedings of the seventh ACM on conference on data and application security and privacy. pp. 301\u2013308","DOI":"10.1145\/3029806.3029823"},{"key":"6597_CR51","volume-title":"Qualitative data analysis: a methods sourcebook","author":"MB Miles","year":"2014","unstructured":"Miles MB, Huberman AM, Saldana J (2014) Qualitative data analysis: a methods sourcebook, 3rd edn. SAGE Publications Inc., Thousand Oaks, CA","edition":"3"},{"issue":"7540","key":"6597_CR52","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G, Petersen S (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529\u2013533","journal-title":"Nature"},{"key":"6597_CR53","doi-asserted-by":"crossref","unstructured":"Nataraj L, Karthikeyan S, Jacob G, Manjunath BS (2011) Malware images: visualization and automatic classification. In: Proceedings of the 8th international symposium on visualization for cyber security. pp. 1\u20137","DOI":"10.1145\/2016904.2016908"},{"key":"6597_CR54","unstructured":"Ngiam J, Khosla A, Kim M, Nam J, Lee H, Ng AY (2011) Multimodal deep learning. In: Proceedings of the 28th international conference on machine learning (ICML-11). pp 689\u2013696"},{"key":"6597_CR55","doi-asserted-by":"crossref","unstructured":"Oussidi A, Elhassouny A (2018) Deep generative models: survey. In: 2018 international conference on intelligent systems and computer vision (ISCV). IEEE, pp. 1\u20138","DOI":"10.1109\/ISACV.2018.8354080"},{"key":"6597_CR56","doi-asserted-by":"crossref","first-page":"116363","DOI":"10.1109\/ACCESS.2020.3002842","volume":"8","author":"Y Pan","year":"2020","unstructured":"Pan Y, Ge X, Fang C, Fan Y (2020) A systematic literature review of android malware detection using static analysis. IEEE Access 8:116363\u2013116379","journal-title":"IEEE Access"},{"key":"6597_CR57","doi-asserted-by":"crossref","unstructured":"Petersen K, Feldt R, Mujtaba S, Mattsson M (2008) Systematic mapping studies in software engineering. In: 12th international conference on evaluation and assessment in software engineering (EASE) 12. pp. 1\u201310","DOI":"10.14236\/ewic\/EASE2008.8"},{"key":"6597_CR58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.infsof.2015.03.007","volume":"64","author":"K Petersen","year":"2015","unstructured":"Petersen K, Vakkalanka S, Kuzniarz L (2015) Guidelines for conducting systematic mapping studies in software engineering: an update. Inf Softw Technol 64:1\u201318","journal-title":"Inf Softw Technol"},{"issue":"5","key":"6597_CR59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3234150","volume":"51","author":"S Pouyanfar","year":"2018","unstructured":"Pouyanfar S, Sadiq S, Yan Y, Tian H, Tao Y, Reyes MP, Shyu ML, Chen SC, Iyengar SS (2018) A survey on deep learning: algorithms, techniques, and applications. ACM Comput Surv (CSUR) 51(5):1\u201336","journal-title":"ACM Comput Surv (CSUR)"},{"key":"6597_CR60","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1016\/j.future.2019.03.007","volume":"97","author":"A Qamar","year":"2019","unstructured":"Qamar A, Karim A, Chang V (2019) Mobile malware attacks: review, taxonomy & future directions. Futur Gener Comput Syst 97:887\u2013909","journal-title":"Futur Gener Comput Syst"},{"key":"6597_CR61","unstructured":"Salakhutdinov R, Hinton G (2009) Deep boltzmann machines. In: Artificial intelligence and statistics. pp 448\u2013455. PMLR"},{"issue":"1","key":"6597_CR62","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s10844-010-0148-x","volume":"38","author":"A Shabtai","year":"2012","unstructured":"Shabtai A, Kanonov U, Elovici Y, Glezer C, Weiss Y (2012) \u201cAndromaly\u201d: a behavioral malware detection framework for android devices. J Intell Inform Syst 38(1):161\u2013190","journal-title":"J Intell Inform Syst"},{"issue":"1","key":"6597_CR63","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.istr.2009.03.003","volume":"14","author":"A Shabtai","year":"2009","unstructured":"Shabtai A, Moskovitch R, Elovici Y, Glezer C (2009) Detection of malicious code by applying machine learning classifiers on static features: a state-of-the-art survey. Inform Secur Tech Rep 14(1):16\u201329","journal-title":"Inform Secur Tech Rep"},{"key":"6597_CR64","first-page":"2141","volume":"27","author":"K Sohn","year":"2014","unstructured":"Sohn K, Shang W, Lee H (2014) Improved multimodal deep learning with variation of information. Adv Neural Inform Process Syst 27:2141\u20132149","journal-title":"Adv Neural Inform Process Syst"},{"issue":"1","key":"6597_CR65","first-page":"3","volume":"8","author":"A Souri","year":"2018","unstructured":"Souri A, Hosseini R (2018) A state-of-the-art survey of malware detection approaches using data mining techniques. HCIS 8(1):3","journal-title":"HCIS"},{"issue":"3","key":"6597_CR66","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s11416-018-0328-8","volume":"15","author":"S Suresh","year":"2019","unstructured":"Suresh S, Di Troia F, Potika K, Stamp M (2019) An analysis of Android adware. J Comput Virol Hacking Tech 15(3):147\u2013160","journal-title":"J Comput Virol Hacking Tech"},{"key":"6597_CR67","doi-asserted-by":"crossref","unstructured":"Tarhan A, Giray G (2017) On the use of ontologies in software process assessment: a systematic literature review. In: Proceedings of the 21st international conference on evaluation and assessment in software engineering. pp. 2\u201311","DOI":"10.1145\/3084226.3084261"},{"key":"6597_CR68","unstructured":"Tarvainen A, Valpola H (2017) Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. Adv Neural Inf Process Syst 30"},{"key":"6597_CR69","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.compag.2018.12.044","volume":"157","author":"J Tummers","year":"2019","unstructured":"Tummers J, Kassahun A, Tekinerdogan B (2019) Obstacles and features of farm management information systems: a systematic literature review. Comput Electron Agric 157:189\u2013204. https:\/\/doi.org\/10.1016\/j.compag.2018.12.044","journal-title":"Comput Electron Agric"},{"key":"6597_CR70","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.cose.2018.11.001","volume":"81","author":"D Ucci","year":"2019","unstructured":"Ucci D, Aniello L, Baldoni R (2019) Survey of machine learning techniques for malware analysis. Comput Secur 81:123\u2013147","journal-title":"Comput Secur"},{"key":"6597_CR71","doi-asserted-by":"publisher","unstructured":"Wohlin C (2014) Guidelines for snowballing in systematic literature studies and a replication in software engineering. In: Proceedings of the 18th international conference on evaluation and assessment in software engineering\u2014EASE \u201914, 1\u201310. Doi: https:\/\/doi.org\/10.1145\/2601248.2601268","DOI":"10.1145\/2601248.2601268"},{"issue":"2","key":"6597_CR72","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/s10115-017-1058-9","volume":"54","author":"Y Ye","year":"2018","unstructured":"Ye Y, Chen L, Hou S, Hardy W, Li X (2018) DeepAM: a heterogeneous deep learning framework for intelligent malware detection. Knowl Inf Syst 54(2):265\u2013285","journal-title":"Knowl Inf Syst"},{"issue":"3","key":"6597_CR73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3073559","volume":"50","author":"Y Ye","year":"2017","unstructured":"Ye Y, Li T, Adjeroh D, Iyengar SS (2017) A survey on malware detection using data mining techniques. ACM Comput Surv (CSUR) 50(3):1\u201340","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"2","key":"6597_CR74","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1007\/s00521-017-3077-6","volume":"31","author":"D Yuxin","year":"2019","unstructured":"Yuxin D, Siyi Z (2019) Malware detection based on deep learning algorithm. Neural Comput Appl 31(2):461\u2013472","journal-title":"Neural Comput Appl"},{"key":"6597_CR75","doi-asserted-by":"crossref","unstructured":"Zeng J, Hu J, Zhang Y (2018) Adaptive traffic signal control with deep recurrent Q-learning. In: 2018 IEEE intelligent vehicles symposium (IV), IEEE, pp. 1215\u20131220","DOI":"10.1109\/IVS.2018.8500414"},{"issue":"3","key":"6597_CR76","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.1109\/COMST.2019.2904897","volume":"21","author":"C Zhang","year":"2019","unstructured":"Zhang C, Patras P, Haddadi H (2019) Deep learning in mobile and wireless networking: a survey. IEEE Commun Surv Tutor 21(3):2224\u20132287","journal-title":"IEEE Commun Surv Tutor"},{"key":"6597_CR77","unstructured":"Zhang X, Zhao J, LeCun Y (2015) Character-level convolutional networks for text classification. Adv Neural Inf Process Syst 28:649\u2013657"},{"key":"6597_CR1001","doi-asserted-by":"crossref","first-page":"163128","DOI":"10.1109\/ACCESS.2019.2951751","volume":"7","author":"A Alotaibi","year":"2019","unstructured":"Alotaibi A (2019) Identifying malicious software using deep residual long-short term memory. IEEE Access 7:163128\u2013163137","journal-title":"IEEE Access"},{"key":"6597_CR78","doi-asserted-by":"crossref","first-page":"101663","DOI":"10.1016\/j.cose.2019.101663","volume":"89","author":"MK Alzaylaee","year":"2020","unstructured":"Alzaylaee MK, Yerima SY, Sezer S (2020) DL-Droid: deep learning based android malware detection using real devices. Comput Secur 89:101663","journal-title":"Comput Secur"},{"key":"6597_CR79","doi-asserted-by":"publisher","DOI":"10.1002\/ett.3675","author":"M Amin","year":"2019","unstructured":"Amin M, Shah B, Sharif A, Ali T, Kim KL, Anwar S (2019) Android malware detection through generative adversarial networks. Trans Emerg Telecommun Technol. https:\/\/doi.org\/10.1002\/ett.3675","journal-title":"Trans Emerg Telecommun Technol"},{"key":"6597_CR80","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.future.2019.07.070","volume":"102","author":"M Amin","year":"2020","unstructured":"Amin M, Tanveer TA, Tehseen M, Khan M, Khan FA, Anwar S (2020) Static malware detection and attribution in android bytecode through an end-to-end deep system. Futur Gener Comput Syst 102:112\u2013126","journal-title":"Futur Gener Comput Syst"},{"key":"6597_CR81","doi-asserted-by":"crossref","first-page":"2789","DOI":"10.1007\/s10586-019-03045-6","volume":"23","author":"A Ananya","year":"2020","unstructured":"Ananya A, Aswathy A, Amal TR, Swathy PG, Vinod P, Mohammad S (2020) SysDroid: a dynamic ML-based android malware analyzer using system call traces. Cluster Comput 23:2789\u20132808","journal-title":"Cluster Comput"},{"issue":"2","key":"6597_CR82","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1049\/iet-ifs.2019.0159","volume":"14","author":"N Bakhshinejad","year":"2019","unstructured":"Bakhshinejad N, Hamzeh A (2019) Parallel-CNN network for malware detection. IET Inf Secur 14(2):210\u2013219","journal-title":"IET Inf Secur"},{"issue":"4","key":"6597_CR83","first-page":"2180","volume":"13","author":"T Chen","year":"2019","unstructured":"Chen T, Mao Q, Lv M, Cheng H, Li Y (2019) DroidVecDeep: android malware detection based on Word2Vec and deep belief network. TIIS 13(4):2180\u20132197","journal-title":"TIIS"},{"key":"6597_CR84","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.jpdc.2019.11.001","volume":"137","author":"G D\u2019Angelo","year":"2020","unstructured":"D\u2019Angelo G, Ficco M, Palmieri F (2020) Malware detection in mobile environments based on autoencoders and API-images. J Parallel Distrib Comput 137:26\u201333","journal-title":"J Parallel Distrib Comput"},{"key":"6597_CR85","first-page":"102423","volume":"50","author":"A De Lorenzo","year":"2020","unstructured":"De Lorenzo A, Martinelli F, Medvet E, Mercaldo F, Santone A (2020) Visualizing the outcome of dynamic analysis of Android malware with VizMal. J Inform Secur App 50:102423","journal-title":"J Inform Secur App"},{"key":"6597_CR86","doi-asserted-by":"crossref","first-page":"5071","DOI":"10.1007\/s12652-020-01957-5","volume":"12","author":"VP Dharmalingam","year":"2020","unstructured":"Dharmalingam VP, Palanisamy V (2020) A novel permission ranking system for android malware detection\u2014the permission grader. J Ambient Intell Human Comput 12:5071\u20135081","journal-title":"J Ambient Intell Human Comput"},{"issue":"4.29","key":"6597_CR87","first-page":"101","volume":"7","author":"S Jan","year":"2018","unstructured":"Jan S, Ali T, Alzahrani A, Musa S (2018) Deep convolutional generative adversarial networks for intent-based dynamic behavior capture. Int J Eng Technol 7(4.29):101\u2013103","journal-title":"Int J Eng Technol"},{"key":"6597_CR88","doi-asserted-by":"crossref","first-page":"S48","DOI":"10.1016\/j.diin.2018.01.007","volume":"24","author":"EB Karbab","year":"2018","unstructured":"Karbab EB, Debbabi M, Derhab A, Mouheb D (2018) MalDozer: automatic framework for android malware detection using deep learning. Digit Investig 24:S48\u2013S59","journal-title":"Digit Investig"},{"issue":"3","key":"6597_CR89","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1109\/TIFS.2018.2866319","volume":"14","author":"T Kim","year":"2018","unstructured":"Kim T, Kang B, Rho M, Sezer S, Im EG (2018) A multimodal deep learning method for android malware detection using various features. IEEE Trans Inf Forensics Secur 14(3):773\u2013788","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"6597_CR90","doi-asserted-by":"crossref","first-page":"e5308","DOI":"10.1002\/cpe.5308","volume":"32","author":"D Li","year":"2019","unstructured":"Li D, Zhao L, Cheng Q, Lu N, Shi W (2019) Opcode sequence analysis of Android malware by a convolutional neural network. Concurr Comput: Practice Exp 32:e5308","journal-title":"Concurr Comput: Practice Exp"},{"key":"6597_CR91","doi-asserted-by":"crossref","first-page":"14753","DOI":"10.1007\/s00521-020-04830-w","volume":"32","author":"S Mahdavifar","year":"2020","unstructured":"Mahdavifar S, Ghorbani AA (2020) DeNNeS: deep embedded neural network expert system for detecting cyber attacks. Neural Comput Appl 32:14753\u201314780","journal-title":"Neural Comput Appl"},{"key":"6597_CR92","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.engappai.2018.06.006","volume":"74","author":"A Mart\u00edn","year":"2018","unstructured":"Mart\u00edn A, Rodr\u00edguez-Fern\u00e1ndez V, Camacho D (2018) CANDYMAN: classifying android malware families by modelling dynamic traces with Markov chains. Eng Appl Artif Intell 74:121\u2013133","journal-title":"Eng Appl Artif Intell"},{"key":"6597_CR93","doi-asserted-by":"crossref","first-page":"2372","DOI":"10.1016\/j.procs.2017.08.216","volume":"112","author":"F Martinelli","year":"2017","unstructured":"Martinelli F, Marulli F, Mercaldo F (2017) Evaluating convolutional neural network for effective mobile malware detection. Proced Comput Sci 112:2372\u20132381","journal-title":"Proced Comput Sci"},{"key":"6597_CR94","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/s11416-019-00346-7","volume":"16","author":"F Mercaldo","year":"2020","unstructured":"Mercaldo F, Santone A (2020) Deep learning for image-based mobile malware detection. J Comput Virol Hacking Tech 16:157\u2013171","journal-title":"J Comput Virol Hacking Tech"},{"issue":"1","key":"6597_CR95","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1007\/s10586-017-0944-y","volume":"21","author":"M Nauman","year":"2018","unstructured":"Nauman M, Tanveer TA, Khan S, Syed TA (2018) Deep neural architectures for large scale android malware analysis. Clust Comput 21(1):569\u2013588","journal-title":"Clust Comput"},{"key":"6597_CR96","doi-asserted-by":"crossref","first-page":"101573","DOI":"10.1016\/j.cose.2019.101573","volume":"87","author":"L Nguyen-Vu","year":"2019","unstructured":"Nguyen-Vu L, Ahn J, Jung S (2019) Android fragmentation in malware detection. Comput Secur 87:101573","journal-title":"Comput Secur"},{"key":"6597_CR97","doi-asserted-by":"crossref","first-page":"101792","DOI":"10.1016\/j.cose.2020.101792","volume":"93","author":"X Pei","year":"2020","unstructured":"Pei X, Yu L, Tian S (2020) AMalNet: a deep learning framework based on graph convolutional networks for malware detection. Comput Secur 93:101792","journal-title":"Comput Secur"},{"key":"6597_CR98","doi-asserted-by":"crossref","first-page":"2151","DOI":"10.3233\/JIFS-190888","volume":"38","author":"X Pei","year":"2020","unstructured":"Pei X, Yu L, Tian S, Wang H, Peng Y (2020) Combining multi-features with a neural joint model for Android malware detection. J Intell Fuzzy Syst (Preprint) 38:2151\u20132163","journal-title":"J Intell Fuzzy Syst (Preprint)"},{"key":"6597_CR99","doi-asserted-by":"crossref","unstructured":"Pekta\u015f A, Acarman T (2020) Learning to detect Android malware via opcode sequences. Neurocomputing 396:599\u2013608","DOI":"10.1016\/j.neucom.2018.09.102"},{"issue":"2","key":"6597_CR100","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1007\/s00500-019-03940-5","volume":"24","author":"A Pekta\u015f","year":"2020","unstructured":"Pekta\u015f A, Acarman T (2020) Deep learning for effective Android malware detection using API call graph embeddings. Soft Comput 24(2):1027\u20131043","journal-title":"Soft Comput"},{"issue":"4","key":"6597_CR101","doi-asserted-by":"crossref","first-page":"4049","DOI":"10.1016\/j.aej.2018.10.008","volume":"57","author":"D Saif","year":"2018","unstructured":"Saif D, El-Gokhy SM, Sallam E (2018) Deep belief networks-based framework for malware detection in android systems. Alex Eng J 57(4):4049\u20134057","journal-title":"Alex Eng J"},{"key":"6597_CR102","doi-asserted-by":"crossref","first-page":"106089","DOI":"10.1016\/j.asoc.2020.106089","volume":"89","author":"S Sharmeen","year":"2020","unstructured":"Sharmeen S, Huda S, Abawajy J, Hassan MM (2020) An adaptive framework against android privilege escalation threats using deep learning and semi-supervised approaches. Appl Soft Comput 89:106089","journal-title":"Appl Soft Comput"},{"issue":"7","key":"6597_CR103","first-page":"3654","volume":"13","author":"L Shi-qi","year":"2019","unstructured":"Shi-qi L, Bo N, Ping J, Sheng-wei T, Long Y, Rui-jin W (2019) Deep learning in Drebin: android malware image texture median filter analysis and detection. KSII Trans Internet Inform Syst (TIIS) 13(7):3654\u20133670","journal-title":"KSII Trans Internet Inform Syst (TIIS)"},{"key":"6597_CR104","doi-asserted-by":"crossref","first-page":"6017","DOI":"10.1007\/s00500-019-04589-w","volume":"24","author":"X Su","year":"2020","unstructured":"Su X, Shi W, Qu X, Zheng Y, Liu X (2020) DroidDeep: using Deep Belief Network to characterize and detect android malware. Soft Comput 24:6017\u20136030","journal-title":"Soft Comput"},{"issue":"4","key":"6597_CR105","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1049\/iet-ifs.2018.5268","volume":"13","author":"M Tang","year":"2018","unstructured":"Tang M, Qian Q (2018) Dynamic API call sequence visualisation for malware classification. IET Inf Secur 13(4):367\u2013377","journal-title":"IET Inf Secur"},{"issue":"3","key":"6597_CR106","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.3233\/JIFS-169424","volume":"34","author":"R Vinayakumar","year":"2018","unstructured":"Vinayakumar R, Soman KP, Poornachandran P, Sachin Kumar S (2018) Detecting Android malware using long short-term memory (LSTM). J Intell Fuzzy Syst 34(3):1277\u20131288","journal-title":"J Intell Fuzzy Syst"},{"key":"6597_CR107","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1016\/j.ins.2019.11.008","volume":"513","author":"S Wang","year":"2020","unstructured":"Wang S, Chen Z, Yan Q, Ji K, Peng L, Yang B, Conti M (2020) Deep and broad URL feature mining for android malware detection. Inf Sci 513:600\u2013613","journal-title":"Inf Sci"},{"issue":"8","key":"6597_CR108","doi-asserted-by":"crossref","first-page":"3035","DOI":"10.1007\/s12652-018-0803-6","volume":"10","author":"W Wang","year":"2019","unstructured":"Wang W, Zhao M, Wang J (2019) Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network. J Ambient Intell Humaniz Comput 10(8):3035\u20133043","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"1","key":"6597_CR109","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1049\/iet-ifs.2015.0211","volume":"11","author":"X Xiao","year":"2016","unstructured":"Xiao X, Wang Z, Li Q, Xia S, Jiang Y (2016) Back-propagation neural network on Markov chains from system call sequences: a new approach for detecting Android malware with system call sequences. IET Inf Secur 11(1):8\u201315","journal-title":"IET Inf Secur"},{"issue":"4","key":"6597_CR110","doi-asserted-by":"crossref","first-page":"3979","DOI":"10.1007\/s11042-017-5104-0","volume":"78","author":"X Xiao","year":"2019","unstructured":"Xiao X, Zhang S, Mercaldo F, Hu G, Sangaiah AK (2019) Android malware detection based on system call sequences and LSTM. Multimed Tools Appl 78(4):3979\u20133999","journal-title":"Multimed Tools Appl"},{"key":"6597_CR111","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.microrel.2019.01.007","volume":"93","author":"YS Yen","year":"2019","unstructured":"Yen YS, Sun HM (2019) An android mutation malware detection based on deep learning using visualization of importance from codes. Microelectron Reliab 93:109\u2013114","journal-title":"Microelectron Reliab"},{"key":"6597_CR112","doi-asserted-by":"crossref","first-page":"101740","DOI":"10.1016\/j.cose.2020.101740","volume":"92","author":"B Yuan","year":"2020","unstructured":"Yuan B, Wang J, Liu D, Guo W, Wu P, Bao X (2020) Byte-level malware classification based on markov images and deep learning. Comput Secur 92:101740","journal-title":"Comput Secur"},{"key":"6597_CR113","unstructured":"Yuan W, Jiang Y, Li H, Cai M (2019) A lightweight on-device detection method for android malware. IEEE transactions on systems, man, and cybernetics: systems"},{"issue":"1","key":"6597_CR114","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/TST.2016.7399288","volume":"21","author":"Z Yuan","year":"2016","unstructured":"Yuan Z, Lu Y, Xue Y (2016) Droiddetector: android malware characterization and detection using deep learning. Tsinghua Sci Technol 21(1):114\u2013123","journal-title":"Tsinghua Sci Technol"},{"key":"6597_CR115","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.eswa.2019.04.064","volume":"133","author":"W Zhong","year":"2019","unstructured":"Zhong W, Gu F (2019) A multi-level deep learning system for malware detection. Expert Syst Appl 133:151\u2013162","journal-title":"Expert Syst Appl"},{"issue":"3","key":"6597_CR116","doi-asserted-by":"crossref","first-page":"3529","DOI":"10.1007\/s11042-018-6498-z","volume":"78","author":"Q Zhou","year":"2019","unstructured":"Zhou Q, Feng F, Shen Z, Zhou R, Hsieh MY, Li KC (2019) A novel approach for mobile malware classification and detection in Android systems. Multimed Tools Appl 78(3):3529\u20133552","journal-title":"Multimed Tools Appl"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06597-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06597-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06597-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,11]],"date-time":"2023-11-11T16:35:10Z","timestamp":1699720510000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06597-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,23]]},"references-count":117,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["6597"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06597-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,23]]},"assertion":[{"value":"27 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 October 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 October 2021","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 have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}