{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T18:07:00Z","timestamp":1758305220659,"version":"3.44.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:00:00Z","timestamp":1750204800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:00:00Z","timestamp":1750204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2023YFB3106900"],"award-info":[{"award-number":["2023YFB3106900"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1007\/s10489-025-06639-3","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T06:22:45Z","timestamp":1750227765000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GTIBS: secure smart home monitoring through gateway traffic analysis and behavioral signature identification"],"prefix":"10.1007","volume":"55","author":[{"given":"Yingjie","family":"Hu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5255-5639","authenticated-orcid":false,"given":"Weiping","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shigeng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"issue":"4","key":"6639_CR1","doi-asserted-by":"publisher","first-page":"1334","DOI":"10.1007\/s11036-022-02055-w","volume":"28","author":"I Cviti\u0107","year":"2023","unstructured":"Cviti\u0107 I, Perakovi\u0107 D, Peri\u0161a M, Jevremovi\u0107 A, Shalaginov A (2023) An overview of smart home iot trends and related cybersecurity challenges. Mobile Netw Appl 28(4):1334\u20131348","journal-title":"Mobile Netw Appl"},{"key":"6639_CR2","doi-asserted-by":"crossref","unstructured":"Dos Santos BV, Verg\u00fctz A, Macedo RT, Nogueira M (2023) A dynamic method to protect user privacy against traffic-based attacks on smart home. Ad Hoc Netw 149:103226","DOI":"10.1016\/j.adhoc.2023.103226"},{"key":"6639_CR3","doi-asserted-by":"crossref","unstructured":"Jmila H, Blanc G, Shahid MR, Lazrag M (2022) A survey of smart home iot device classification using machine learning-based network traffic analysis. IEEE Access 10:97117\u201397141","DOI":"10.1109\/ACCESS.2022.3205023"},{"key":"6639_CR4","doi-asserted-by":"crossref","unstructured":"De Keersmaeker F, Cao Y, Ndonda GK, Sadre R (2023) A survey of public iot datasets for network security research. IEEE Commun Surv Tutorials 25(3):1808\u20131840","DOI":"10.1109\/COMST.2023.3288942"},{"key":"6639_CR5","doi-asserted-by":"crossref","unstructured":"Alex C, Creado G, Almobaideen W, Alghanam OA, Saadeh M (2023) A comprehensive survey for iot security datasets taxonomy, classification and machine learning mechanisms. Comput Sec 132:103283","DOI":"10.1016\/j.cose.2023.103283"},{"key":"6639_CR6","doi-asserted-by":"crossref","unstructured":"Sheng C, Zhou W, Han Q.-L, Ma W, Zhu X, Wen S, Xiang Y (2025) Network traffic fingerprinting for iiot device identification: A survey. IEEE Trans Indust Inf","DOI":"10.1109\/TII.2025.3534441"},{"key":"6639_CR7","doi-asserted-by":"crossref","unstructured":"Miettinen M, Marchal S, Hafeez I, Asokan N, Sadeghi A-R, Tarkoma S (2017) Iot sentinel: automated device-type identification for security enforcement in iot. In: 2017 IEEE 37th International conference on distributed computing systems (ICDCS), IEEE, pp 2177\u20132184","DOI":"10.1109\/ICDCS.2017.283"},{"key":"6639_CR8","doi-asserted-by":"crossref","unstructured":"Sivanathan A, Gharakheili HH, Loi F, Radford A, Wijenayake C, Vishwanath A, Sivaraman V (2018) Classifying iot devices in smart environments using network traffic characteristics. IEEE Trans Mobile Comput 18(8):1745\u20131759","DOI":"10.1109\/TMC.2018.2866249"},{"key":"6639_CR9","doi-asserted-by":"crossref","unstructured":"Kumar R, Swarnkar M, Singal G, Kumar N (2021) Iot network traffic classification using machine learning algorithms: an experimental analysis. IEEE Int Things J 9(2):989\u20131008","DOI":"10.1109\/JIOT.2021.3121517"},{"key":"6639_CR10","doi-asserted-by":"crossref","unstructured":"Chakraborty B, Divakaran DM, Nevat I, Peters GW, Gurusamy M (2021) Cost-aware feature selection for iot device classification. IEEE Int Things J 8(14):11052\u201311064","DOI":"10.1109\/JIOT.2021.3051480"},{"key":"6639_CR11","doi-asserted-by":"crossref","unstructured":"Huang S (2024) Intelligent device recognition of internet of things based on machine learning. Intell Syst Appl 22:200368","DOI":"10.1016\/j.iswa.2024.200368"},{"key":"6639_CR12","doi-asserted-by":"crossref","unstructured":"Brahma J, Matam R, Choudhury N (2023) Device identification using network traces in a smart home iot network. In: 2023 IEEE International conference on advanced networks and telecommunications systems (ANTS), IEEE pp 1\u20136","DOI":"10.1109\/ANTS59832.2023.10469513"},{"key":"6639_CR13","doi-asserted-by":"crossref","unstructured":"Danso PK, Dadkhah S, Neto ECP, Zohourian A, Molyneaux H, Lu R, Ghorbani AA (2023) Transferability of machine learning algorithm for iot device profiling and identification. IEEE Int Things J 11(2):2322\u20132335","DOI":"10.1109\/JIOT.2023.3292319"},{"key":"6639_CR14","doi-asserted-by":"crossref","unstructured":"Yousefnezhad N, Malhi A, Fr\u00e4mling K (2021) Automated iot device identification based on full packet information using real-time network traffic. Sensors 21(8):2660","DOI":"10.3390\/s21082660"},{"key":"6639_CR15","doi-asserted-by":"crossref","unstructured":"Fan L, He L, Wu Y, Zhang S, Wang Z, Li J, Yang J, Xiang C, Ma X (2022) Autoiot: automatically updated iot device identification with semi-supervised learning. IEEE Trans Mobile Comput 22(10):5769\u20135786","DOI":"10.1109\/TMC.2022.3183118"},{"key":"6639_CR16","doi-asserted-by":"crossref","unstructured":"Marchal S, Miettinen M, Nguyen TD, Sadeghi A-R, Asokan N (2019) Audi: toward autonomous iot device-type identification using periodic communication. IEEE J Selected Areas Commun 37(6):1402\u20131412","DOI":"10.1109\/JSAC.2019.2904364"},{"key":"6639_CR17","doi-asserted-by":"crossref","unstructured":"Zhao J, Li Q, Sun J, Dong M, Ota K, Shen M (2023) Efficient iot device identification via network behavior analysis based on time series dictionary. IEEE Int Things J 11(3):5129\u20135142","DOI":"10.1109\/JIOT.2023.3305585"},{"key":"6639_CR18","doi-asserted-by":"crossref","unstructured":"Kostas K, Just M, Lones MA (2022) Iotdevid: a behavior-based device identification method for the iot. IEEE Int Things J 9(23):23741\u201323749","DOI":"10.1109\/JIOT.2022.3191951"},{"key":"6639_CR19","doi-asserted-by":"crossref","unstructured":"Ma X, Qu J, Li J, Lui JC, Li Z, Guan X (2020) Pinpointing hidden iot devices via spatial-temporal traffic fingerprinting. In: IEEE INFOCOM 2020-IEEE conference on computer communications, IEEE, pp 894\u2013903","DOI":"10.1109\/INFOCOM41043.2020.9155346"},{"key":"6639_CR20","doi-asserted-by":"crossref","unstructured":"Luo Y, Chen X, Ge N, Feng W, Lu J (2022) Transformer-based device-type identification in heterogeneous iot traffic. IEEE Int Things J 10(6):5050\u20135062","DOI":"10.1109\/JIOT.2022.3221967"},{"key":"6639_CR21","unstructured":"Yu L, Luo B, Ma J, Zhou Z, Liu Q (2020) You are what you broadcast: Identification of mobile and [CDATA[\\{]]$$\\{$$IoT$$\\}$$[CDATA[\\}]] devices from (public)$$\\{$$[CDATA[\\{]]WiFi$$\\}$$[CDATA[\\}]]. In: 29th USENIX Security Symposium (USENIX Security 20), pp 55\u201372"},{"key":"6639_CR22","doi-asserted-by":"crossref","unstructured":"Zhang Y, Gong B, Wang Q (2024) Bls-identification: a device fingerprint classification mechanism based on broad learning for internet of things. Digital Commun Netw 10(3):728\u2013739","DOI":"10.1016\/j.dcan.2022.10.003"},{"key":"6639_CR23","doi-asserted-by":"crossref","unstructured":"Hu X, Zhu C, Cheng G, Li R, Wu H, Gong J (2022) A deep subdomain adaptation network with attention mechanism for malware variant traffic identification at an iot edge gateway. IEEE Int Things J 10(5):3814\u20133826","DOI":"10.1109\/JIOT.2022.3160755"},{"key":"6639_CR24","doi-asserted-by":"crossref","unstructured":"Wang H, Eklund D, Oprea A, Raza S (2023) Fl4iot: Iot device fingerprinting and identification using federated learning. ACM Trans Int Things 4(3):1\u201324","DOI":"10.1145\/3603257"},{"key":"6639_CR25","doi-asserted-by":"crossref","unstructured":"Zhang S, Xiao K, Yu J, Liu X, Wang W (2023) Accurate iot device identification based on a few network traffic. In: 2023 IEEE\/ACM 31st international symposium on quality of service (IWQoS), IEEE, pp 01\u201310","DOI":"10.1109\/IWQoS57198.2023.10188721"},{"key":"6639_CR26","doi-asserted-by":"crossref","unstructured":"OConnor T, Mohamed R, Miettinen M, Enck W, Reaves B, Sadeghi A-R (2019) Homesnitch: behavior transparency and control for smart home iot devices. In: Proceedings of the 12th conference on security and privacy in wireless and mobile networks, pp 128\u2013138","DOI":"10.1145\/3317549.3323409"},{"key":"6639_CR27","doi-asserted-by":"crossref","unstructured":"Xiao Z, Tong H (2025) Federated contrastive learning with feature-based distillation for human activity recognition. IEEE Trans Computat Social Syst","DOI":"10.1109\/TCSS.2024.3510428"},{"key":"6639_CR28","unstructured":"Xiao Z, Tong H, Qu R, Xing H, Luo S, Zhu Z, Song F, Feng L (2023) Capmatch: semi-supervised contrastive transformer capsule with feature-based knowledge distillation for human activity recognition. IEEE Trans Neural Netw Learn Syst"},{"key":"6639_CR29","doi-asserted-by":"crossref","unstructured":"Kalaria R, Kayes A, Rahayu W, Pardede E, Salehi A (2024) Iotpredictor: a security framework for predicting iot device behaviours and detecting malicious devices against cyber attacks. Comput Sec 146:104037","DOI":"10.1016\/j.cose.2024.104037"},{"key":"6639_CR30","doi-asserted-by":"crossref","unstructured":"Hu T, Dubois DJ, Choffnes D (2023) Behaviot: measuring smart home iot behavior using network-inferred behavior models. In: Proceedings of the 2023 ACM on internet measurement conference, pp 421\u2013436","DOI":"10.1145\/3618257.3624829"},{"key":"6639_CR31","doi-asserted-by":"crossref","unstructured":"Schmidt D, Tagliaro C, Borgolte K, Lindorfer M (2023) Iotflow: inferring iot device behavior at scale through static mobile companion app analysis. In: Proceedings of the 2023 ACM SIGSAC conference on computer and communications security, pp 681\u2013695","DOI":"10.1145\/3576915.3623211"},{"key":"6639_CR32","doi-asserted-by":"crossref","unstructured":"Acar A, Fereidooni H, Abera T, Sikder AK, Miettinen M, Aksu H, Conti M, Sadeghi A-R, Uluagac S (2020) Peek-a-boo: I see your smart home activities, even encrypted! In: Proceedings of the 13th ACM conference on security and privacy in wireless and mobile networks, pp 207\u2013218","DOI":"10.1145\/3395351.3399421"},{"key":"6639_CR33","doi-asserted-by":"crossref","unstructured":"Wan Y, Xu K, Wang F, Xue G (2021) Iotathena: unveiling iot device activities from network traffic. IEEE Trans Wireless Commun 21(1):651\u2013664","DOI":"10.1109\/TWC.2021.3098608"},{"key":"6639_CR34","doi-asserted-by":"crossref","unstructured":"Charyyev B, Gunes MH (2020) Locality-sensitive iot network traffic fingerprinting for device identification. IEEE Int Things J 8(3):1272\u20131281","DOI":"10.1109\/JIOT.2020.3035087"},{"key":"6639_CR35","doi-asserted-by":"crossref","unstructured":"Trimananda R, Varmarken J, Markopoulou A, Demsky B (2020) Packet-level signatures for smart home devices. In: Network and distributed systems security (NDSS) Symposium, vol. 2020","DOI":"10.14722\/ndss.2020.24097"},{"key":"6639_CR36","doi-asserted-by":"crossref","unstructured":"Kuzniar C, Neves M, Gurevich V, Haque I (2024) Poiriot: fingerprinting iot devices at tbps scale. IEEE\/ACM Trans Netw","DOI":"10.1109\/TNET.2024.3395278"},{"key":"6639_CR37","doi-asserted-by":"crossref","unstructured":"Ren J, Dubois DJ, Choffnes D, Mandalari AM, Kolcun R, Haddadi H (2019) Information exposure for consumer iot devices: a multidimensional, network-informed measurement approach. In: Proc. of the internet measurement conference (IMC)","DOI":"10.1145\/3355369.3355577"},{"key":"6639_CR38","doi-asserted-by":"crossref","unstructured":"Vaccari I, Chiola G, Aiello M, Mongelli M, Cambiaso E (2020) Mqttset, a new dataset for machine learning techniques on mqtt. Sensors 20(22):6578","DOI":"10.3390\/s20226578"},{"key":"6639_CR39","doi-asserted-by":"crossref","unstructured":"Sharafaldin I, Lashkari AH, Ghorbani AA et al (2018) Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp 1(2018):108\u2013116","DOI":"10.5220\/0006639801080116"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06639-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06639-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06639-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T13:58:33Z","timestamp":1758290313000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06639-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,18]]},"references-count":39,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["6639"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06639-3","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2025,6,18]]},"assertion":[{"value":"11 May 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The datasets used for this experiment are publicly available by the respective organizations\/authors to further improve the Multimodal Recommendation research field. Thus, informed consent is not required to use the dataset. References and citations to relevant datasets are included in the manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}},{"value":"All authors declare that they have no conflict of interest to this work.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"762"}}