{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:09:26Z","timestamp":1760710166373},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2020,3,17]],"date-time":"2020-03-17T00:00:00Z","timestamp":1584403200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,3,17]],"date-time":"2020-03-17T00:00:00Z","timestamp":1584403200000},"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":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,5]]},"DOI":"10.1007\/s11042-020-08804-x","type":"journal-article","created":{"date-parts":[[2020,3,17]],"date-time":"2020-03-17T18:03:26Z","timestamp":1584468206000},"page":"16713-16729","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Adversarial android malware detection for mobile multimedia applications in IoT environments"],"prefix":"10.1007","volume":"80","author":[{"given":"Rahim","family":"Taheri","sequence":"first","affiliation":[]},{"given":"Reza","family":"Javidan","sequence":"additional","affiliation":[]},{"given":"Zahra","family":"Pooranian","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,3,17]]},"reference":[{"key":"8804_CR1","unstructured":"Contagio dataset. http:\/\/contagiominidump.blogspot.com\/ (2019). [Online; accessed 30-October-2019]"},{"key":"8804_CR2","doi-asserted-by":"crossref","unstructured":"Arp D, Spreitzenbarth M, Hubner M, Gascon H, Rieck K, Siemens C (2014) Drebin: Effective and explainable detection of android malware in your pocket. Ndss, vol 14. pp 23\u201326","DOI":"10.14722\/ndss.2014.23247"},{"key":"8804_CR3","doi-asserted-by":"crossref","unstructured":"Bazrafshan Z., Hashemi H., Fard S. M. H., 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":"3","key":"8804_CR4","doi-asserted-by":"publisher","first-page":"2815","DOI":"10.1007\/s11042-018-5853-4","volume":"78","author":"F Carrara","year":"2019","unstructured":"Carrara F, Falchi F, Caldelli R, Amato G, Becarelli R (2019) Adversarial image detection in deep neural networks. Multimed Tools Appl 78(3):2815\u20132835","journal-title":"Multimed Tools Appl"},{"key":"8804_CR5","unstructured":"Chang TJ, He Y, Li P (2018) Efficient two-step adversarial defense for deep neural networks, arXiv:1810.03739"},{"key":"8804_CR6","doi-asserted-by":"crossref","unstructured":"Chen X, Li C, Wang D, Wen S, Zhang J, Nepal S, Xiang Y, Ren K (2019) Android hiv: A study of repackaging malware for evading machine-learning detection. IEEE Transactions on Information Forensics and Security","DOI":"10.1109\/TIFS.2019.2932228"},{"key":"8804_CR7","unstructured":"Demetrio L, Biggio B, Lagorio G, Roli F, Armando A (2019) Explaining vulnerabilities of deep learning to adversarial malware binaries. arXiv:1901.03583"},{"key":"8804_CR8","doi-asserted-by":"crossref","unstructured":"Dinakarrao SMP, Sayadi H, Makrani HM, Nowzari C, Rafatirad S, Homayoun H (2019) Lightweight node-level malware detection and network-level malware confinement in iot networks. In: 2019 Design, automation & test in europe conference & exhibition (DATE), IEEE, pp 776\u2013781","DOI":"10.23919\/DATE.2019.8715057"},{"key":"8804_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.sysarc.2019.01.017","volume":"97","author":"EM Dovom","year":"2019","unstructured":"Dovom EM, Azmoodeh A, Dehghantanha A, Newton DE, Parizi RM, Karimipour H (2019) Fuzzy pattern tree for edge malware detection and categorization in iot. J Syst Archit 97:1\u20137","journal-title":"J Syst Archit"},{"key":"8804_CR10","doi-asserted-by":"crossref","unstructured":"Fan W, Sun G, Su Y, Liu Z, Lu X (2019) Integration of statistical detector and gaussian noise injection detector for adversarial example detection in deep neural networks. Multimed Tools Appl, pp. 1\u201321","DOI":"10.1007\/s11042-019-7353-6"},{"key":"8804_CR11","unstructured":"Goodfellow IJ, Shlens J, Szegedy C (2014) Explaining and harnessing adversarial examples. arXiv:1412.6572"},{"key":"8804_CR12","doi-asserted-by":"crossref","unstructured":"Ham HS, Kim HH, Kim MS, Choi MJ (2014) Linear svm-based android malware detection for reliable iot services. J Appl Math, 2014","DOI":"10.1155\/2014\/594501"},{"key":"8804_CR13","doi-asserted-by":"crossref","unstructured":"Hossain MM, Hasan R, Zawoad S (2018) Probe-iot: a public digital ledger based forensic investigation framework for iot. In: INFOCOM workshops, pp 1\u20132","DOI":"10.1109\/INFCOMW.2018.8406875"},{"key":"8804_CR14","doi-asserted-by":"crossref","unstructured":"Hu X, Chiueh Tc, Shin KG (2009) Large-scale malware indexing using function-call graphs. In: Proceedings of the 16th ACM conference on computer and communications security, pp 611\u2013620. ACM","DOI":"10.1145\/1653662.1653736"},{"issue":"17","key":"8804_CR15","doi-asserted-by":"publisher","first-page":"18,153","DOI":"10.1007\/s11042-016-4189-1","volume":"76","author":"ES Jeong","year":"2017","unstructured":"Jeong ES, Kim IS, Lee DH (2017) Safeguard: a behavior based real-time malware detection scheme for mobile multimedia applications in android platform. Multimed Tools Appl 76(17):18,153\u201318,173","journal-title":"Multimed Tools Appl"},{"key":"8804_CR16","doi-asserted-by":"crossref","unstructured":"Jiang X, Zhou Y (2012) Dissecting android malware: Characterization and evolution. In: Proc of IEEE S&P, pp 95\u2013109","DOI":"10.1109\/SP.2012.16"},{"key":"8804_CR17","doi-asserted-by":"publisher","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"},{"key":"8804_CR18","doi-asserted-by":"crossref","unstructured":"Lei T, Qin Z, Wang Z, Li Q, Ye D (2019) Evedroid: event-aware android malware detection against model degrading for iot devices. IEEE Internet of Things Journal","DOI":"10.1109\/JIOT.2019.2909745"},{"issue":"4","key":"8804_CR19","doi-asserted-by":"publisher","first-page":"974","DOI":"10.3390\/s19040974","volume":"19","author":"X Liu","year":"2019","unstructured":"Liu X, Du X, Zhang X, Zhu Q, Wang H, Guizani M (2019) Adversarial samples on android malware detection systems for iot systems. Sensors 19(4):974","journal-title":"Sensors"},{"issue":"1","key":"8804_CR20","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1007\/s00500-014-1511-6","volume":"20","author":"FA Narudin","year":"2016","unstructured":"Narudin FA, Feizollah A, Anuar NB, Gani A (2016) Evaluation of machine learning classifiers for mobile malware detection. Soft Comput 20(1):343\u2013357","journal-title":"Soft Comput"},{"issue":"3","key":"8804_CR21","first-page":"2","volume":"8","author":"K Shaerpour","year":"2013","unstructured":"Shaerpour K, Dehghantanha A, Mahmod R (2013) Trends in android malware detection. Journal of Digital Forensics, Security and Law 8(3):2","journal-title":"Journal of Digital Forensics, Security and Law"},{"issue":"2","key":"8804_CR22","doi-asserted-by":"publisher","first-page":"1043","DOI":"10.1109\/JIOT.2018.2795549","volume":"5","author":"S Shen","year":"2018","unstructured":"Shen S, Huang L, Zhou H, Yu S, Fan E, Cao Q (2018) Multistage signaling game-based optimal detection strategies for suppressing malware diffusion in fog-cloud-based iot networks. IEEE Internet of Things Journal 5(2):1043\u20131054","journal-title":"IEEE Internet of Things Journal"},{"key":"8804_CR23","doi-asserted-by":"crossref","unstructured":"Su J, Vasconcellos VD, Prasad S, Daniele S, Feng Y, Sakurai K (2018) Lightweight classification of iot malware based on image recognition. In: 2018 IEEE 42Nd annual computer software and applications conference (COMPSAC), vol 2. IEEE, pp 664\u2013669","DOI":"10.1109\/COMPSAC.2018.10315"},{"key":"8804_CR24","unstructured":"Tram\u00e8r F, Kurakin A, Papernot N, Goodfellow I, Boneh D, McDaniel P (2017) Ensemble adversarial training: Attacks and defenses. arXiv:1705.07204"},{"key":"8804_CR25","doi-asserted-by":"publisher","first-page":"38,367","DOI":"10.1109\/ACCESS.2018.2854599","volume":"6","author":"Z Wang","year":"2018","unstructured":"Wang Z (2018) Deep learning-based intrusion detection with adversaries. IEEE Access 6:38,367\u201338,384","journal-title":"IEEE Access"},{"key":"8804_CR26","doi-asserted-by":"crossref","unstructured":"Zhang M, Duan Y, Yin H, Zhao Z (2014) Semantics-aware android malware classification using weighted contextual api dependency graphs. In: Proceedings of the 2014 ACM SIGSAC conference on computer and communications security, ACM, pp 1105\u20131116","DOI":"10.1145\/2660267.2660359"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-08804-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-020-08804-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-08804-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,19]],"date-time":"2021-05-19T15:51:01Z","timestamp":1621439461000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-020-08804-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,17]]},"references-count":26,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2021,5]]}},"alternative-id":["8804"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-08804-x","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,17]]},"assertion":[{"value":"3 November 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 February 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"There is no any conflict of interest for the paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of interests"}}]}}