{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T19:39:55Z","timestamp":1771702795661,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2017,3,10]],"date-time":"2017-03-10T00:00:00Z","timestamp":1489104000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100013494","name":"West Light Foundation of The Chinese Academy of Sciences","doi-asserted-by":"crossref","award":["XBBS201313"],"award-info":[{"award-number":["XBBS201313"]}],"id":[{"id":"10.13039\/501100013494","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2018,12]]},"DOI":"10.1007\/s00521-017-2914-y","type":"journal-article","created":{"date-parts":[[2017,3,10]],"date-time":"2017-03-10T12:20:02Z","timestamp":1489148402000},"page":"3353-3361","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["HEMD: a highly efficient random forest-based malware detection framework for Android"],"prefix":"10.1007","volume":"30","author":[{"given":"Hui-Juan","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Tong-Hai","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Zhu-Hong","family":"You","sequence":"additional","affiliation":[]},{"given":"Wei-Lei","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Li","family":"Cheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,3,10]]},"reference":[{"key":"2914_CR1","doi-asserted-by":"crossref","unstructured":"Arp D, Spreitzenbarth M, Hubner M, Gascon H, Rieck K (2014) DREBIN: effective and explainable detection of android malware in your pocket. In: Network and distributed system security symposium","DOI":"10.14722\/ndss.2014.23247"},{"issue":"4","key":"2914_CR2","first-page":"35","volume":"3","author":"D Werth","year":"2012","unstructured":"Werth D, Emrich A, Chapko A (2012) An ecosystem for user-generated mobile services. J Converg 3(4):35\u201340","journal-title":"J Converg"},{"issue":"1","key":"2914_CR3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/2192-1962-3-1","volume":"3","author":"JWK Gnanaraj","year":"2013","unstructured":"Gnanaraj JWK, Ezra K, Rajsingh EB (2013) Smart card based time efficient authentication scheme for global grid computing. Hum Cent Comput Inf Sci 3(1):1\u201314","journal-title":"Hum Cent Comput Inf Sci"},{"key":"2914_CR4","unstructured":"Motive Security Labs. Malware report\u2014H1 2015 (2015) http:\/\/resources.alcatel-lucent.com\/asset\/189669"},{"key":"2914_CR5","doi-asserted-by":"crossref","unstructured":"Burguera I, Zurutuza U, Nadjm-Tehrani S (2011) Crowdroid: behavior-based malware detection system for android. In: ACM workshop on security and privacy in smartphones and mobile devices, pp 15\u201326","DOI":"10.1145\/2046614.2046619"},{"key":"2914_CR6","unstructured":"Schmidt AD, Camtepe SA, Albayrak S (2010) Static smartphone malware detection. In: The 5th security research conference (Future Security 2010). Berlin, p 146"},{"key":"2914_CR7","doi-asserted-by":"crossref","unstructured":"Sharma A, Dash SK (2014) Mining API calls and permissions for android malware detection. In: International conference on cryptology and network security. Springer, pp 191\u2013205","DOI":"10.1007\/978-3-319-12280-9_13"},{"key":"2914_CR8","doi-asserted-by":"crossref","unstructured":"Kou X, Wen Q (2011) Intrusion detection model based on android. In: 2011 4th IEEE international conference on broadband network and multimedia technology, pp 624\u2013628","DOI":"10.1109\/ICBNMT.2011.6156010"},{"key":"2914_CR9","doi-asserted-by":"crossref","unstructured":"Bose A, Hu X, Shin KG, Park T (2008) Behavioral detection of malware on mobile handsets. In: ACM proceedings of the 6th international conference on mobile systems, applications, and services, pp 225\u2013238","DOI":"10.1145\/1378600.1378626"},{"key":"2914_CR10","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1016\/j.procs.2016.03.084","volume":"79","author":"SS More","year":"2016","unstructured":"More SS, Gaikwad PP (2016) Trust-based voting method for efficient malware detection. Proced Comput Sci 79:657\u2013667","journal-title":"Proced Comput Sci"},{"issue":"1","key":"2914_CR11","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. Inf Secur Tech Rep 14(1):16\u201329","journal-title":"Inf Secur Tech Rep"},{"issue":"9","key":"2914_CR12","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/MC.2012.36","volume":"45","author":"M Chandramohan","year":"2012","unstructured":"Chandramohan M, Tan HBK (2012) Detection of mobile malware in the wild. Computer 45(9):65\u201371","journal-title":"Computer"},{"key":"2914_CR13","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/978-3-642-35473-1_12","volume-title":"Advances in intelligent systems and applications-volume 2","author":"CY Huang","year":"2013","unstructured":"Huang CY, Tsai YT, Hsu CH (2013) Performance evaluation on permission-based detection for android malware. Advances in intelligent systems and applications-volume 2. Springer, Berlin, pp 111\u2013120"},{"key":"2914_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2275-y","author":"BB Gupta","year":"2016","unstructured":"Gupta BB, Tewari A, Jain AK, Agrawal DP (2016) Fighting against phishing attacks: state of the art and future challenges. Neural Comput Appl. doi: 10.1007\/s00521-016-2275-y","journal-title":"Neural Comput Appl"},{"key":"2914_CR15","doi-asserted-by":"crossref","unstructured":"Moser A, Kruegel C, Kirda E (2007) Limits of static analysis for malware detection. In: IEEE computer security applications conference, 2007. Twenty-third annual, pp 421\u2013430","DOI":"10.1109\/ACSAC.2007.21"},{"issue":"1","key":"2914_CR16","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1109\/TII.2013.2271506","volume":"10","author":"Y Li","year":"2014","unstructured":"Li Y, Li S, Song Q, Liu H, Meng QH (2014) Fast and robust data association using posterior based approximate joint compatibility test. IEEE Trans Indus Inf 10(1):331\u2013339","journal-title":"IEEE Trans Indus Inf"},{"key":"2914_CR17","unstructured":"Schmidt AD, Schmidt HG, Clausen J, Camtepe A, Albayrak S (2008) Enhancing security of linux-based android devices. In: 15th international Linux Kongress. Lehmann"},{"key":"2914_CR18","doi-asserted-by":"crossref","unstructured":"Cheng J, Wong SHY, Yang H, Lu S (2007) Smartsiren: virus detection and alert for smartphones. In: Proceedings of the 5th international conference on mobile systems, applications and services. ACM, pp 258\u2013271","DOI":"10.1145\/1247660.1247690"},{"key":"2914_CR19","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1007\/978-3-642-04342-0_13","volume-title":"International workshop on recent advances in intrusion detection","author":"L Liu","year":"2009","unstructured":"Liu L, Yan G, Zhang X, Chen S (2009) Virusmeter: preventing your cellphone from spies. International workshop on recent advances in intrusion detection. Springer, Berlin, pp 244\u2013264"},{"key":"2914_CR20","doi-asserted-by":"crossref","unstructured":"Burguera I, Zurutuza U, Nadjm-Tehrani S (2011) Crowdroid: behavior-based malware detection system for android. In: Proceedings of the 1st ACM workshop on security and privacy in smartphones and mobile devices. ACM, pp 15\u201326","DOI":"10.1145\/2046614.2046619"},{"issue":"1","key":"2914_CR21","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 Inf Syst 38(1):161\u2013190","journal-title":"J Intell Inf Syst"},{"key":"2914_CR22","first-page":"240","volume-title":"International conference on mathematical methods, models, and architectures for computer network security","author":"G Dini","year":"2012","unstructured":"Dini G, Martinelli F, Saracino A, Sgamdirra D (2012) MADAM: a multi-level anomaly detector for android malware. International conference on mathematical methods, models, and architectures for computer network security. Springer, Berlin, pp 240\u2013253"},{"issue":"2","key":"2914_CR23","doi-asserted-by":"crossref","first-page":"138","DOI":"10.14429\/dsj.66.9701","volume":"66","author":"A Kapoor","year":"2016","unstructured":"Kapoor A, Dhavale S (2016) Control flow graph based multiclass malware detection using bi-normal separation. Def Sci J 66(2):138\u2013145","journal-title":"Def Sci J"},{"key":"2914_CR24","doi-asserted-by":"crossref","unstructured":"Peiravian N, Zhu X (2013) Machine learning for android malware detection using permission and API calls. In: 2013 IEEE 25th international conference on tools with artificial intelligence, pp 300\u2013305","DOI":"10.1109\/ICTAI.2013.53"},{"issue":"2","key":"2914_CR25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2089125.2089126","volume":"44","author":"M Egele","year":"2012","unstructured":"Egele M, Scholte T, Kirda E, Kruegel C (2012) A survey on automated dynamic malware-analysis techniques and tools. ACM Comput Surv (CSUR) 44(2):1\u201342","journal-title":"ACM Comput Surv (CSUR)"},{"key":"2914_CR26","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1007\/978-3-642-27503-6_22","volume-title":"International conference on information computing and applications","author":"M Zhao","year":"2011","unstructured":"Zhao M, Ge F, Zhang T, Yuan Z (2011) AntiMalDroid: an efficient SVM-based malware detection framework for android. International conference on information computing and applications. Springer, Berlin, pp 158\u2013166"},{"issue":"1","key":"2914_CR27","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1049\/iet-ifs.2013.0095","volume":"8","author":"SY Yerima","year":"2014","unstructured":"Yerima SY, Sezer S, Mcwilliams G (2014) Analysis of Bayesian classification-based approaches for android malware detection. IET Inf Secur 8(1):25\u201336","journal-title":"IET Inf Secur"},{"issue":"1","key":"2914_CR28","doi-asserted-by":"crossref","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"},{"key":"2914_CR29","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/978-3-642-33018-6_28","volume-title":"International joint conference CISIS\u201912-ICEUTE\u00b4 12-SOCO\u00b4 12 special sessions","author":"I Santos","year":"2013","unstructured":"Santos I, Devesa J, Brezo F, Nieves J, Bringas PG (2013) Opem: a static-dynamic approach for machine-learning-based malware detection. International joint conference CISIS\u201912-ICEUTE\u00b4 12-SOCO\u00b4 12 special sessions. Springer, Berlin, pp 271\u2013280"},{"issue":"1","key":"2914_CR30","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/s10664-014-9352-6","volume":"21","author":"K Allix","year":"2016","unstructured":"Allix K, Bissyand\u00e9 TF, J\u00e9rome Q, Klein J, State R (2016) Empirical assessment of machine learning-based malware detectors for Android. Empir Softw Eng 21(1):183\u2013211","journal-title":"Empir Softw Eng"},{"key":"2914_CR31","doi-asserted-by":"crossref","unstructured":"Ham HS, Kim HH, Kim MS, Choi MJ (2014) Linear SVM-based android malware detection. In: Frontier and innovation in future computing and communications, vol 301. Springer, pp 575\u2013585","DOI":"10.1007\/978-94-017-8798-7_68"},{"issue":"8","key":"2914_CR32","doi-asserted-by":"crossref","first-page":"2279","DOI":"10.1007\/s00521-015-2064-z","volume":"27","author":"E Elyan","year":"2016","unstructured":"Elyan E, Gaber MM (2016) A fine-grained random forests using class decomposition: an application to medical diagnosis. Neural Comput Appl 27(8):2279\u20132288","journal-title":"Neural Comput Appl"},{"key":"2914_CR33","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.diin.2015.06.002","volume":"14","author":"J Jang","year":"2015","unstructured":"Jang J, Kang H, Woo J, Mohaisen A, Kim HK (2015) Andro-autopsy: anti-malware system based on similarity matching of malware and malware creator-centric information. Digital Investig 14:17\u201335","journal-title":"Digital Investig"},{"key":"2914_CR34","doi-asserted-by":"crossref","unstructured":"Li W, Ge J, Dai G (2015) Detecting malware for android platform: an SVM-based approach. In: Cyber security and cloud computing (CSCloud), 2015 IEEE 2nd international conference, pp 464\u2013469","DOI":"10.1109\/CSCloud.2015.50"},{"issue":"3","key":"2914_CR35","first-page":"409","volume":"2","author":"M Oulehla","year":"2016","unstructured":"Oulehla M, Malanik D (2016) Techniques that allow hidden activity based malware on android mobile devices. Int J Sci Eng Appl Sci (IJSEAS) 2(3):409\u2013419","journal-title":"Int J Sci Eng Appl Sci (IJSEAS)"},{"key":"2914_CR36","unstructured":"Chan PPK, Song WK (2014) Static detection of android malware by using permissions and API calls. In: IEEE 2014 international conference on machine learning and cybernetics, vol 1, pp 82\u201387"},{"key":"2914_CR37","doi-asserted-by":"crossref","unstructured":"Wolfe B, Elish KO, Yao D (2014) Comprehensive behavior profiling for proactive android malware detection. In: International conference on information security. Springer, pp 328\u2013344","DOI":"10.1007\/978-3-319-13257-0_19"},{"key":"2914_CR38","doi-asserted-by":"crossref","unstructured":"Idrees F, Rajarajan M (2014) Investigating the android intents and permissions for malware detection. In: 2014 IEEE 10th international conference on wireless and mobile computing, networking and communications (WiMob). IEEE, pp 354\u2013358","DOI":"10.1109\/WiMOB.2014.6962194"},{"key":"2914_CR39","doi-asserted-by":"crossref","unstructured":"Aafer Y, Du W, Yin H (2013) DroidAPIMiner: mining API-level features for robust malware detection in android. In: International conference on security and privacy in communication systems. Springer, pp 86\u2013103","DOI":"10.1007\/978-3-319-04283-1_6"},{"key":"2914_CR40","doi-asserted-by":"crossref","unstructured":"Wu D J, Mao C H, Lee H M, Wu KP (2012) Droidmat: Android malware detection through manifest and API calls tracing. In: Information security (Asia JCIS), 2012 seventh Asia joint conference on. IEEE, pp 62\u201369","DOI":"10.1109\/AsiaJCIS.2012.18"},{"issue":"11","key":"2914_CR41","doi-asserted-by":"crossref","first-page":"2191","DOI":"10.1088\/0967-3334\/35\/11\/2191","volume":"35","author":"K Ellis","year":"2014","unstructured":"Ellis K, Kerr J, Godbole S, Lanckriet G, Wing D, Marshall S (2014) A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers. Physiol Meas 35(11):2191","journal-title":"Physiol Meas"},{"issue":"2","key":"2914_CR42","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L (1996) Bagging predictors. Mach Learn 24(2):123\u2013140","journal-title":"Mach Learn"},{"key":"2914_CR43","unstructured":"Ham HS, Choi MJ (2013) Analysis of android malware detection performance using machine learning classifiers. In: IEEE 2013 international conference on ICT convergence (ICTC), pp 490\u2013495"},{"key":"2914_CR44","unstructured":"Kim T, Choi Y, Han S, Chung J Y (2012) Monitoring and detecting abnormal behavior in mobile cloud infrastructure. In: 2012 IEEE network operations and management symposium, pp 1303\u20131310"},{"key":"2914_CR45","doi-asserted-by":"crossref","unstructured":"Sahs J, Khan L (2012) A machine learning approach to android malware detection. In: IEEE intelligence and security informatics conference (EISIC), 2012 European, pp 141\u2013147","DOI":"10.1109\/EISIC.2012.34"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-017-2914-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-2914-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-2914-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,5,17]],"date-time":"2020-05-17T17:44:10Z","timestamp":1589737450000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-017-2914-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,3,10]]},"references-count":45,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2018,12]]}},"alternative-id":["2914"],"URL":"https:\/\/doi.org\/10.1007\/s00521-017-2914-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,3,10]]}}}