{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T06:23:22Z","timestamp":1766298202822,"version":"3.30.1"},"reference-count":28,"publisher":"Tech Science Press","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.32604\/iasc.2023.030527","type":"journal-article","created":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T02:42:18Z","timestamp":1658198538000},"page":"2413-2429","source":"Crossref","is-referenced-by-count":9,"title":["Investigation of Android Malware Using Deep Learning Approach"],"prefix":"10.32604","volume":"35","author":[{"given":"V.","family":"Joseph Raymond","sequence":"first","affiliation":[]},{"given":"R.","family":"Jeberson Retna Raj","sequence":"additional","affiliation":[]}],"member":"17807","reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1109\/TR.2019.2956690","article-title":"Understanding the evolution of android app vulnerabilities","volume":"70","author":"Gao","year":"2021","journal-title":"IEEE Transactions on Reliability"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1109\/TR.2020.2982537","article-title":"On existence of common malicious system call codes in android malware families","volume":"70","author":"Surendran.","year":"2021","journal-title":"IEEE Transactions on Reliability"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1504\/IJESDF.2020.110674","article-title":"Reversing and auditing of android malicious applications using sandboxing environment","volume":"l","author":"Raymond","year":"2020","journal-title":"International Journal of Electronic Security and Digital Forensics"},{"key":"ref4","series-title":"Proc. IEEE Int. Conf. on Computer Communication and Informatics (ICCCI)","first-page":"1","article-title":"A machine learning approach to the detection and analysis of android malicious apps","author":"Shibija","year":"2018"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"102086","DOI":"10.1016\/j.cose.2020.102086","article-title":"JOWMDroid: Android malware detection based on feature weighting with joint optimization of weight-mapping and classifier parameters","volume":"100","author":"Cai","year":"2021","journal-title":"Computers & Security"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"102166","DOI":"10.1016\/j.cose.2020.102166","article-title":"Catch them alive: A malware detection approach through memory forensics, manifold learning and computer vision","volume":"103","author":"Bozkir.","year":"2021","journal-title":"Computers & Security"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"102216","DOI":"10.1016\/j.cose.2021.102216","article-title":"Structural features with nonnegative matrix factorization for metamorphic malware detection","volume":"103","author":"Ling","year":"2021","journal-title":"Computers and Security"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1109\/TR.2020.2982537","article-title":"On existence of common malicious system call codes in Android malware families","volume":"70","author":"Surendran","year":"2020","journal-title":"IEEE Transactions on Reliability"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.32604\/iasc.2022.024192","article-title":"Tuning rules for fractional order pid controller using data analytics","volume":"33","author":"Varshini","year":"2022","journal-title":"Intelligent Automation and Soft Computing"},{"key":"ref10","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":"2021","journal-title":"Neurocomputing"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"101336","DOI":"10.1016\/j.pmcj.2021.101336","article-title":"ProDroid-An Android malware detection framework based on profile hidden Markov model","volume":"23","author":"Sasidharan","year":"2021","journal-title":"Pervasive and Mobile Computing"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"102250","DOI":"10.1016\/j.cose.2021.102250","article-title":"Optimal feature configuration for dynamic malware detection","volume":"103","author":"Garc\u00eda","year":"2021","journal-title":"Computers & Security"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"107069","DOI":"10.1016\/j.asoc.2020.107069","article-title":"Deep learning feature exploration for Android malware detection","volume":"102","author":"Zhang","year":"2021","journal-title":"Applied Soft Computing"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"113973","DOI":"10.1016\/j.eswa.2020.113973","article-title":"Portfolio optimization with return prediction using deep learning and machine learning","volume":"165","author":"Ma","year":"2020","journal-title":"Expert Systems with Applications"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"100358","DOI":"10.1016\/j.cosrev.2020.100358","article-title":"A survey of malware detection in Android apps: Recommendations and perspectives for future research","volume":"39","author":"Razgallah","year":"2021","journal-title":"Computer Science Review"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"100365","DOI":"10.1016\/j.cosrev.2021.100365","article-title":"A survey of android application and malware hardening","volume":"39","author":"Sihag","year":"2021","journal-title":"Computer Science Review"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"100179","DOI":"10.1016\/j.bdr.2020.100179","article-title":"A data-driven method for hybrid data assimilation with multilayer perceptron","volume":"23","author":"Huang","year":"2021","journal-title":"Big Data Research"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"100022","DOI":"10.1016\/j.array.2020.100022","article-title":"Black box analysis of android malware detectors","volume":"6","author":"Nellaivadivelu","year":"2020","journal-title":"Array"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"102718","DOI":"10.1016\/j.jisa.2020.102718","article-title":"Multi-view deep learning for zero-day Android malware detection","volume":"58","author":"Millar","year":"2021","journal-title":"Journal of Information Security and Applications"},{"key":"ref20","first-page":"107069","article-title":"Deep learning feature exploration for Android malware detection","volume":"l","author":"Zhang","year":"2020","journal-title":"Applied Soft Computing"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1016\/j.future.2020.10.008","article-title":"DeepAMD: Detection and identification of Android malware using high-efficient deep artificial neural network","volume":"115","author":"Imtiaz","year":"2020","journal-title":"Future Generation Computer Systems"},{"key":"ref22","series-title":"Reconciling Data Analytics, Automation, Privacy, and Security: A Big Data Challenge (RDAAPS)","first-page":"1","article-title":"EntropLyzer: Android malware classification and characterization using entropy analysis of dynamic characteristics","author":"Keyes","year":"2021"},{"key":"ref23","series-title":"Proc. IEEE Seventh Asia Joint Conf. on Information Security","first-page":"62","article-title":"Droidmat: Android malware detection through manifest and api calls tracing","author":"Wu","year":"2012"},{"key":"ref24","series-title":"Proc. IEEE Int. Conf. on Machine Learning and Cybernetics","first-page":"82","article-title":"Static detection of Android malware by using permissions and API calls","author":"Chan","year":"2014"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/TST.2016.7399288","article-title":"Droiddetector: Android malware characterization and detection using deep learning","volume":"21","author":"Yuan","year":"2018","journal-title":"Tsinghua Science and Technology"},{"key":"ref26","series-title":"Proc. of the ACM Special Interest Group on Data Communication","first-page":"371","article-title":"Droid-sec: Deep learning in android malware detection","author":"Yuan","year":"2014"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"527","DOI":"10.32604\/iasc.2021.016240","article-title":"AI\/ML in security orchestration, automation and response: Future research directions","volume":"28","author":"Kinyua","year":"2021","journal-title":"Intelligent Automation and Soft Computing"},{"key":"ref28","series-title":"Proc. 2018 IEEE European Symp. on Security and Privacy (EuroS&P)","first-page":"473","article-title":"Deeprefiner: Multi-layer android malware detection system applying deep neural networks","author":"Xu","year":"2018"}],"container-title":["Intelligent Automation &amp; Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.techscience.com\/ueditor\/files\/iasc\/TSP_IASC-35-2\/TSP_IASC_30527\/TSP_IASC_30527.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T21:30:49Z","timestamp":1733520649000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/iasc\/v35n2\/48937"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":28,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.32604\/iasc.2023.030527","relation":{},"ISSN":["1079-8587"],"issn-type":[{"type":"print","value":"1079-8587"}],"subject":[],"published":{"date-parts":[[2023]]}}}