{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T16:07:19Z","timestamp":1779898039552,"version":"3.53.1"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T00:00:00Z","timestamp":1629072000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T00:00:00Z","timestamp":1629072000000},"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":["Cluster Comput"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s10586-021-03388-z","type":"journal-article","created":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T18:02:57Z","timestamp":1629136977000},"page":"2125-2141","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Towards building data analytics benchmarks for IoT intrusion detection"],"prefix":"10.1007","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7154-7295","authenticated-orcid":false,"given":"Rasheed","family":"Ahmad","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Izzat","family":"Alsmadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wasim","family":"Alhamdani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lo\u2019ai","family":"Tawalbeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,8,16]]},"reference":[{"key":"3388_CR1","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1007\/s13198-017-0685-6","volume":"9","author":"V Agrawal","year":"2018","unstructured":"Agrawal, V., Rastogi, R., Tiwari, D.C.: Spider monkey optimization: a survey. Int. J. Syst. Assur. Eng. Manag. 9, 929\u2013941 (2018). https:\/\/doi.org\/10.1007\/s13198-017-0685-6","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"3388_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-021-03231-5","author":"A Alhowaide","year":"2021","unstructured":"Alhowaide, A., Alsmadi, I., Tang, J.: Towards the design of real-time autonomous IoT NIDS. Clust. Comput. (2021). https:\/\/doi.org\/10.1007\/s10586-021-03231-5","journal-title":"Clust. Comput."},{"key":"3388_CR3","doi-asserted-by":"publisher","unstructured":"Alsamiri, J., Alsubhi, K.: Internet of things cyber attacks detection using machine learning. Int. J. Adv. Comput. Sci. Appl. (2019). https:\/\/doi.org\/10.14569\/IJACSA.2019.0101280","DOI":"10.14569\/IJACSA.2019.0101280"},{"key":"3388_CR4","doi-asserted-by":"publisher","first-page":"9042","DOI":"10.1109\/JIOT.2019.2926365","volume":"6","author":"E Anthi","year":"2019","unstructured":"Anthi, E., Williams, L., S\u0142owi\u0144ska, M., Theodorakopoulos, G., Burnap, P.: A supervised intrusion detection system for smart home IoT devices. IEEE Internet Things J. 6, 9042\u20139053 (2019). https:\/\/doi.org\/10.1109\/JIOT.2019.2926365","journal-title":"IEEE Internet Things J."},{"key":"3388_CR5","doi-asserted-by":"publisher","first-page":"106495","DOI":"10.1109\/ACCESS.2019.2929487","volume":"7","author":"A Binbusayyis","year":"2019","unstructured":"Binbusayyis, A., Vaiyapuri, T.: Identifying and benchmarking key features for cyber intrusion detection: an ensemble approach. IEEE Access 7, 106495\u2013106513 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2929487","journal-title":"IEEE Access"},{"key":"3388_CR6","doi-asserted-by":"publisher","unstructured":"Brun, O., Yin, Y.: Random neural networks and deep learning for attack detection at the edge. In: 2019 IEEE International Conference on Fog Computing (ICFC), pp 11\u201314 (2019). https:\/\/doi.org\/10.1109\/ICFC.2019.00009","DOI":"10.1109\/ICFC.2019.00009"},{"key":"3388_CR7","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1016\/j.procs.2018.07.183","volume":"134","author":"O Brun","year":"2018","unstructured":"Brun, O., Yin, Y., Gelenbe, E.: Deep learning with dense random neural network for detecting attacks against IoT-connected home environments. Procedia Comput. Sci. 134, 458\u2013463 (2018). https:\/\/doi.org\/10.1016\/j.procs.2018.07.183","journal-title":"Procedia Comput. Sci."},{"key":"3388_CR8","doi-asserted-by":"publisher","first-page":"2671","DOI":"10.1109\/COMST.2019.2896380","volume":"21","author":"N Chaabouni","year":"2019","unstructured":"Chaabouni, N., Mosbah, M., Zemmari, A., Sauvignac, C., Faruki, P.: Network intrusion detection for IoT security based on learning techniques. IEEE Commun. Surv. Tutor. 21, 2671\u20132701 (2019). https:\/\/doi.org\/10.1109\/COMST.2019.2896380","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"3388_CR9","doi-asserted-by":"publisher","unstructured":"Chung, Y., Kraska, T., Polyzotis, N., Tae, K.H., Whang, S.E.: Slice finder: automated data slicing for model validation. In: IEEE 35th International Conference on Data Engineering (ICDE) p 1550\u20131553 (2019). https:\/\/doi.org\/10.1109\/ICDE.2019.00139","DOI":"10.1109\/ICDE.2019.00139"},{"key":"3388_CR10","unstructured":"Cisco: Cisco annual internet report (2018-2023) white paper (2020). https:\/\/www.cisco.com\/c\/en\/us\/solutions\/collateral\/executive-perspectives\/annual-internet-report\/white-paper-c11-741490.html"},{"key":"3388_CR11","doi-asserted-by":"publisher","unstructured":"Costa, J., Dessai, N., Gaonkar, S., Aswale, S., Shetgaonkar, P.: Iot-botnet detection using long short-term memory recurrent neural network. Int. J. Eng. Res. V9. https:\/\/doi.org\/10.17577\/IJERTV9IS080236 (2020)","DOI":"10.17577\/IJERTV9IS080236"},{"key":"3388_CR12","doi-asserted-by":"publisher","unstructured":"Doshi, R., Apthorpe, N., Feamster, N.: Machine learning ddos detection for consumer internet of things devices. In: 2018 IEEE Security and Privacy Workshops (SPW), pp 29\u201335 (2018). https:\/\/doi.org\/10.1109\/SPW.2018.00013","DOI":"10.1109\/SPW.2018.00013"},{"key":"3388_CR13","doi-asserted-by":"publisher","first-page":"524","DOI":"10.22161\/ijaers.73.68","volume":"7","author":"A Dushimimana","year":"2020","unstructured":"Dushimimana, A., Tao, T., Kindong, R., Nishyirimbere, A.: Bi-directional recurrent neural network for intrusion detection system (IDS) in the internet of things (IoT). Int. J. Adv. Eng. Res. Sci. 7, 524\u2013539 (2020). https:\/\/doi.org\/10.22161\/ijaers.73.68","journal-title":"Int. J. Adv. Eng. Res. Sci."},{"key":"3388_CR14","doi-asserted-by":"publisher","unstructured":"Elshawi, R., Wahab, A., Barnawi, A., Sakr, S.: Dlbench: a comprehensive experimental evaluation of deep learning frameworks. Clust. Comput. (2021). https:\/\/doi.org\/10.1007\/s10586-021-03240-4","DOI":"10.1007\/s10586-021-03240-4"},{"key":"3388_CR15","doi-asserted-by":"publisher","unstructured":"Feng Z, Xu C, Tao D (2019) Self-supervised representation learning from multi-domain data. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV). https:\/\/doi.org\/10.1109\/ICCV.2019.00334","DOI":"10.1109\/ICCV.2019.00334"},{"key":"3388_CR16","first-page":"8","volume":"15","author":"N Fu","year":"2019","unstructured":"Fu, N., Kamili, N., Huang, Y., Shi, J.: A novel deep intrusion detection model based on a convolutional neural network. Aust. J. Intell. Inf. Process. Syst. 15, 8 (2019)","journal-title":"Aust. J. Intell. Inf. Process. Syst."},{"key":"3388_CR17","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.cose.2008.08.003","volume":"28","author":"P Garc\u00eda-Teodoro","year":"2009","unstructured":"Garc\u00eda-Teodoro, P., D\u00edaz-Verdejo, J., Maci\u00e1-Fern\u00e1ndez, G., V\u00e1zquez, E.: Anomaly-based network intrusion detection: techniques, systems and challenges. Comput. Secur. 28, 18\u201328 (2009). https:\/\/doi.org\/10.1016\/j.cose.2008.08.003","journal-title":"Comput. Secur."},{"key":"3388_CR18","doi-asserted-by":"publisher","unstructured":"Ge, M., Fu, X., Syed, N., Baig, Z., Teo, G., Robles-Kelly, A.: Deep learning-based intrusion detection for IoT networks. In: 2019 IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC), pp. 256\u201325609, (2019). https:\/\/doi.org\/10.1109\/PRDC47002.2019.00056","DOI":"10.1109\/PRDC47002.2019.00056"},{"key":"3388_CR19","doi-asserted-by":"publisher","first-page":"82721","DOI":"10.1109\/access.2019.2924045","volume":"7","author":"V Hassija","year":"2019","unstructured":"Hassija, V., Chamola, V., Saxena, V., Goyal, P., Sikdar, B., Jain, D.: A survey on IoT security: application areas, security threats, and solution architectures. IEEE Access 7, 82721\u201382743 (2019). https:\/\/doi.org\/10.1109\/access.2019.2924045","journal-title":"IEEE Access"},{"key":"3388_CR20","unstructured":"Hinnefeld, J.H., Cooman, P., Mammo, N., Deese, R.: Evaluating fairness metrics in the presence of dataset bias. ArXiv (2018)"},{"key":"3388_CR21","doi-asserted-by":"publisher","unstructured":"Hwang, R.H., Peng, M.C., Nguyen, V.L., Chang, Y.L.: An ISTM-based deep learning approach for classifying malicious traffic at the packet level. Appl. Sci. (2019). https:\/\/doi.org\/10.3390\/app9163414","DOI":"10.3390\/app9163414"},{"key":"3388_CR22","doi-asserted-by":"publisher","first-page":"30387","DOI":"10.1109\/ACCESS.2020.2973023","volume":"8","author":"RH Hwang","year":"2020","unstructured":"Hwang, R.H., Peng, M.C., Huang, C.W., Lin, P.C., Nguyen, V.L.: An unsupervised deep learning model for early network traffic anomaly detection. IEEE Access 8, 30387\u201330399 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2973023","journal-title":"IEEE Access"},{"key":"3388_CR23","doi-asserted-by":"publisher","unstructured":"Ibitoye, O., Shafiq, O., Matrawy, A.: Analyzing adversarial attacks against deep learning for intrusion detection in iot networks. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp. 1\u20136 (2019). https:\/\/doi.org\/10.1109\/GLOBECOM38437.2019.9014337","DOI":"10.1109\/GLOBECOM38437.2019.9014337"},{"key":"3388_CR24","doi-asserted-by":"publisher","unstructured":"Ingre, B., Yadav, A.: Performance analysis of nsl-kdd dataset using ann. In: 2015 International Conference on Signal Processing and Communication Engineering Systems, pp. 92\u201396 (2015). https:\/\/doi.org\/10.1109\/SPACES.2015.7058223","DOI":"10.1109\/SPACES.2015.7058223"},{"key":"3388_CR25","doi-asserted-by":"publisher","unstructured":"Kelly, C., Pitropakis, N., McKeown, S., Lambrinoudakis, C.: Testing and hardening iot devices against the mirai botnet. In: 2020 International Conference on Cyber Security and Protection of Digital Services (Cyber Security), pp 1\u20138 (2020). https:\/\/doi.org\/10.1109\/CyberSecurity49315.2020.9138887","DOI":"10.1109\/CyberSecurity49315.2020.9138887"},{"key":"3388_CR26","doi-asserted-by":"publisher","first-page":"583","DOI":"10.3390\/sym11040583","volume":"11","author":"MA Khan","year":"2019","unstructured":"Khan, M.A., Karim, M., Kim, Y.: A scalable and hybrid intrusion detection system based on the convolutional-lSTM network. Symmetry 11, 583 (2019). https:\/\/doi.org\/10.3390\/sym11040583","journal-title":"Symmetry"},{"key":"3388_CR27","doi-asserted-by":"publisher","first-page":"11743","DOI":"10.1109\/ACCESS.2019.2959047","volume":"8","author":"AY Khan","year":"2020","unstructured":"Khan, A.Y., Latif, R., Latif, S., Tahir, S., Gohar Batool, T.S.: Malicious insider attack detection in IoTs using data analytics. IEEE Access 8, 11743\u201311753 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2019.2959047","journal-title":"IEEE Access"},{"key":"3388_CR28","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1016\/j.future.2019.05.041","volume":"100","author":"N Koroniotis","year":"2019","unstructured":"Koroniotis, N., Moustafa, N., Sitnikova, E., Turnbull, B.: Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-IoT dataset. Futur. Gener. Comput. Syst. 100, 779\u2013796 (2019). https:\/\/doi.org\/10.1016\/j.future.2019.05.041","journal-title":"Futur. Gener. Comput. Syst."},{"key":"3388_CR29","doi-asserted-by":"publisher","unstructured":"Kurniabudi, K., Stiawan, D., Dr, D., Idris, M., Bamhdi, A., Budiarto, R.: Cicids-2017 dataset feature analysis with information gain for anomaly detection. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2020.3009843 (2020)","DOI":"10.1109\/ACCESS.2020.3009843"},{"key":"3388_CR30","doi-asserted-by":"publisher","first-page":"4","DOI":"10.37335\/ijek.v7i1.84","volume":"7","author":"G Lampropoulos","year":"2019","unstructured":"Lampropoulos, G., Siakas, K., Anastasiadis, T.: Internet of things in the context of industry 4.0: an overview. Int. J. Entrep. Knowl. 7, 4\u201319 (2019)","journal-title":"Int. J. Entrep. Knowl."},{"key":"3388_CR31","doi-asserted-by":"publisher","unstructured":"Liang, X., Znati, T.: A long short-term memory enabled framework for DDOS detection. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp. 1\u20136 (2019). https:\/\/doi.org\/10.1109\/GLOBECOM38437.2019.9013450","DOI":"10.1109\/GLOBECOM38437.2019.9013450"},{"key":"3388_CR32","doi-asserted-by":"publisher","first-page":"4329","DOI":"10.1109\/JIOT.2019.2963635","volume":"7","author":"F Liang","year":"2020","unstructured":"Liang, F., Yu, W., Liu, X., Griffith, D., Golmie, N.: Toward edge-based deep learning in industrial internet of things. IEEE Internet Things J. 7, 4329\u20134341 (2020). https:\/\/doi.org\/10.1109\/JIOT.2019.2963635","journal-title":"IEEE Internet Things J."},{"key":"3388_CR33","doi-asserted-by":"publisher","first-page":"20","DOI":"10.4018\/IJMCMC.2014100102","volume":"6","author":"Y Liu","year":"2014","unstructured":"Liu, Y., Zhou, Y., Wen, S., Tang, C.: A strategy on selecting performance metrics for classifier evaluation. Int. J. Mob. Comput. Multimed. Commun. 6, 20\u201335 (2014). https:\/\/doi.org\/10.4018\/IJMCMC.2014100102","journal-title":"Int. J. Mob. Comput. Multimed. Commun."},{"key":"3388_CR34","doi-asserted-by":"publisher","unstructured":"Liu, J., Liu, S., Zhang, S.: Detection of IoT botnet based on deep learning. In: 2019 Chinese Control Conference (CCC), pp 8381\u20138385 (2019). https:\/\/doi.org\/10.23919\/ChiCC.2019.8866088","DOI":"10.23919\/ChiCC.2019.8866088"},{"key":"3388_CR35","doi-asserted-by":"publisher","unstructured":"Ma, L., Chai, Y., Cui, L., Ma, D., Fu, Y., Xiao, A.: A deep learning-based DDOS detection framework for internet of things. In: ICC 2020\u20132020 IEEE International Conference on Communications (ICC), pp 1\u20136 (2020). https:\/\/doi.org\/10.1109\/ICC40277.2020.9148944","DOI":"10.1109\/ICC40277.2020.9148944"},{"key":"3388_CR36","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1109\/MPRV.2018.03367731","volume":"17","author":"Y Meidan","year":"2018","unstructured":"Meidan, Y., Bohadana, Michael, Mirsky, Y., Shabtai, Y., Breitenbacher, D., Shabtai, A., Elovici, Y.: N-baiot: Network-based detection of IoT botnet attacks using deep autoencoders. IEEE Pervasive Comput. 17, 12\u201322 (2018). https:\/\/doi.org\/10.1109\/MPRV.2018.03367731","journal-title":"IEEE Pervasive Comput."},{"key":"3388_CR37","doi-asserted-by":"publisher","unstructured":"Mergendahl, S., Li, J.: Rapid: Robust and adaptive detection of distributed denial-of-service traffic from the internet of things. In: 2020 IEEE Conference on Communications and Network Security (CNS) pp 1\u20139 (2020). https:\/\/doi.org\/10.1109\/CNS48642.2020.9162278","DOI":"10.1109\/CNS48642.2020.9162278"},{"key":"3388_CR38","doi-asserted-by":"publisher","unstructured":"Moussa, M.M., Alazzawi, L.: Cyber attacks detection based on deep learning for cloud-dew computing in automotive iot applications. In: 2020 IEEE International Conference on Smart Cloud (SmartCloud) pp 55\u201361 (2020). https:\/\/doi.org\/10.1109\/SmartCloud49737.2020.00019","DOI":"10.1109\/SmartCloud49737.2020.00019"},{"key":"3388_CR39","doi-asserted-by":"publisher","unstructured":"Moustafa, N., Slay, J.: Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In: 2015 Military Communications and Information Systems Conference (MilCIS), pp. 1\u20136 (2015). https:\/\/doi.org\/10.1109\/MilCIS.2015.7348942","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"3388_CR40","doi-asserted-by":"publisher","first-page":"4815","DOI":"10.1109\/JIOT.2018.2871719","volume":"6","author":"N Moustafa","year":"2019","unstructured":"Moustafa, N., Turnbull, B., Choo, K.R.: An ensemble intrusion detection technique based on proposed statistical flow features for protecting network traffic of internet of things. IEEE Internet Things J. 6, 4815\u20134830 (2019). https:\/\/doi.org\/10.1109\/JIOT.2018.2871719","journal-title":"IEEE Internet Things J."},{"key":"3388_CR41","doi-asserted-by":"publisher","unstructured":"Nagisetty, A., Gupta, G.P.: Framework for detection of malicious activities in IoT networks using Keras deep learning library. In: 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC) pp. 633\u2013637 (2019). https:\/\/doi.org\/10.1109\/ICCMC.2019.8819688","DOI":"10.1109\/ICCMC.2019.8819688"},{"key":"3388_CR42","doi-asserted-by":"publisher","unstructured":"Naung, Y., Feng, Y., Santosa, P.I., Hartanto, R., Sakurai, K.: A sequential scheme for detecting cyber attacks in iot environment. In: 2019 IEEE International Conference on Dependable, Autonomic and Secure Computing, pp. 238\u2013244 (2019). https:\/\/doi.org\/10.1109\/DASC\/PiCom\/CBDCom\/CyberSciTech.2019.00051","DOI":"10.1109\/DASC\/PiCom\/CBDCom\/CyberSciTech.2019.00051"},{"key":"3388_CR43","unstructured":"Naveed, K., Wu, H.: Poster: A semi-supervised framework to detect botnets in IoT devices. In: 2020 IFIP Networking Conference (Networking), pp. 649\u2013651 (2020)"},{"key":"3388_CR44","doi-asserted-by":"publisher","unstructured":"Ng, W., Minasny, B., de\u00a0Sousa\u00a0Mendes, W., Dematt\u00ea, J.A.M.: Estimation of effective calibration sample size using visible near infrared spectroscopy: deep learning vs machine learning. In: SOIL Discussions, pp. 1\u201321 (2019). https:\/\/doi.org\/10.5194\/soil-2019-48","DOI":"10.5194\/soil-2019-48"},{"key":"3388_CR45","doi-asserted-by":"publisher","unstructured":"Otoum, Y., Liu, D., Nayak, A.: Dl-ids: a deep learning-based intrusion detection framework for securing IoT. Trans. Emerg. Telecommun. Technol. (2019). https:\/\/doi.org\/10.1002\/ett.3803","DOI":"10.1002\/ett.3803"},{"key":"3388_CR46","doi-asserted-by":"publisher","unstructured":"Patidarm, S., Bains, I.S.: Web security in IoT networks using deep learning model. In: 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 848\u2013855 (2020). https:\/\/doi.org\/10.1109\/ICSSIT48917.2020.9214114","DOI":"10.1109\/ICSSIT48917.2020.9214114"},{"key":"3388_CR47","unstructured":"Pratz: Decoding the confusion matrix. KeyToDataScience (2019). https:\/\/keytodatascience.com\/confusion-matrix\/"},{"key":"3388_CR48","unstructured":"Rawat, S.: Is accuracy everything? (2019). https:\/\/towardsdatascience.com\/is-accuracy-everything-96da9afd540d"},{"key":"3388_CR49","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/MCOM.2019.1800819","volume":"57","author":"S Rezaei","year":"2019","unstructured":"Rezaei, S., Liu, X.: Deep learning for encrypted traffic classification: an overview. IEEE Commun. Mag. 57, 76\u201381 (2019). https:\/\/doi.org\/10.1109\/MCOM.2019.1800819","journal-title":"IEEE Commun. Mag."},{"key":"3388_CR50","doi-asserted-by":"publisher","unstructured":"Roopak, M., Tian, G.Y., Chambers, J.: Deep learning models for cyber security in iot networks. In: 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0452\u20130457. https:\/\/doi.org\/10.1109\/CCWC.2019.8666588","DOI":"10.1109\/CCWC.2019.8666588"},{"key":"3388_CR51","doi-asserted-by":"publisher","unstructured":"Roopak, M., Tian, G.Y., Chambers, J.: An intrusion detection system against DDOS attacks in IoT networks. In: 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), pp 0562\u20130567 (2020). https:\/\/doi.org\/10.1109\/CCWC47524.2020.9031206","DOI":"10.1109\/CCWC47524.2020.9031206"},{"key":"3388_CR52","doi-asserted-by":"publisher","first-page":"167059","DOI":"10.1109\/ACCESS.2020.3022855","volume":"8","author":"K Sadaf","year":"2020","unstructured":"Sadaf, K., Sultana, J.: Intrusion detection based on autoencoder and isolation forest in fog computing. IEEE Access 8, 167059\u2013167068 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3022855","journal-title":"IEEE Access"},{"key":"3388_CR53","doi-asserted-by":"publisher","first-page":"74571","DOI":"10.1109\/ACCESS.2020.2988854","volume":"8","author":"A Samy","year":"2020","unstructured":"Samy, A., Yu, H., Zhang, H.: Fog-based attack detection framework for internet of things using deep learning. IEEE Access 8, 74571\u201374585 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2988854","journal-title":"IEEE Access"},{"key":"3388_CR54","doi-asserted-by":"publisher","unstructured":"Shahid, M.R., Blanc, G., Zhang, Z., Debar, H.: Anomalous communications detection in IoT networks using sparse autoencoders. In: 2019 IEEE 18th International Symposium on Network Computing and Applications (NCA), pp. 1\u20135 (2019). https:\/\/doi.org\/10.1109\/NCA.2019.8935007","DOI":"10.1109\/NCA.2019.8935007"},{"key":"3388_CR55","doi-asserted-by":"publisher","unstructured":"Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: 4th International Conference on Information Systems Security and Privacy, pp. 108\u2013116 (2018). https:\/\/doi.org\/10.5220\/0006639801080116","DOI":"10.5220\/0006639801080116"},{"key":"3388_CR56","doi-asserted-by":"publisher","unstructured":"Soe, Y.N., Santosa, P.I., Hartanto, R.: DDOS attack detection based on simple ANN with smote for IoT environment. In: 2019 Fourth International Conference on Informatics and Computing (ICIC), pp. 1\u20135 (2019). https:\/\/doi.org\/10.1109\/ICIC47613.2019.8985853","DOI":"10.1109\/ICIC47613.2019.8985853"},{"key":"3388_CR57","doi-asserted-by":"publisher","unstructured":"Sriram, S., Vinayakumar, R., Alazab, M., KP, S.: Network flow based iot botnet attack detection using deep learning. In: IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) pp. 189\u2013194 (2020). https:\/\/doi.org\/10.1109\/INFOCOMWKSHPS50562.2020.9162668","DOI":"10.1109\/INFOCOMWKSHPS50562.2020.9162668"},{"key":"3388_CR58","doi-asserted-by":"publisher","first-page":"279","DOI":"10.3390\/info11050279","volume":"11","author":"B Susilo","year":"2020","unstructured":"Susilo, B., Sari, R.F.: Intrusion detection in IoT networks using deep learning algorithm. Information 11, 279 (2020). https:\/\/doi.org\/10.3390\/info11050279","journal-title":"Information"},{"key":"3388_CR59","doi-asserted-by":"publisher","unstructured":"Tama, B.A., Rhee, K.H.: Attack classification analysis of IoT network via deep learning approach. In: Research Briefs on Information and Communication Technology Evolution (ReBICTE), 3 (2017). https:\/\/doi.org\/10.22667\/ReBiCTE.2017.11.15.015","DOI":"10.22667\/ReBiCTE.2017.11.15.015"},{"key":"3388_CR60","doi-asserted-by":"publisher","first-page":"1927","DOI":"10.1007\/s10586-020-03129-8","volume":"23","author":"Y Wang","year":"2020","unstructured":"Wang, Y., Liang, Y., Sun, H., Ma, Y.: Intrusion detection and performance simulation based on improved sequential pattern mining algorithm. Clust. Comput. 23, 1927\u20131936 (2020). https:\/\/doi.org\/10.1007\/s10586-020-03129-8","journal-title":"Clust. Comput."}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-021-03388-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-021-03388-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-021-03388-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T18:49:10Z","timestamp":1652813350000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-021-03388-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,16]]},"references-count":60,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["3388"],"URL":"https:\/\/doi.org\/10.1007\/s10586-021-03388-z","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,16]]},"assertion":[{"value":"27 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}