{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T07:42:30Z","timestamp":1769845350295,"version":"3.49.0"},"reference-count":94,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T00:00:00Z","timestamp":1595462400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T00:00:00Z","timestamp":1595462400000},"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":["Fuzzy Optim Decis Making"],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1007\/s10700-020-09332-x","type":"journal-article","created":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T06:58:11Z","timestamp":1595487491000},"page":"1-49","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Towards fuzzy anomaly detection-based security: a comprehensive review"],"prefix":"10.1007","volume":"20","author":[{"given":"Mohammad","family":"Masdari","sequence":"first","affiliation":[]},{"given":"Hemn","family":"Khezri","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,7,23]]},"reference":[{"key":"9332_CR1","doi-asserted-by":"crossref","first-page":"7067","DOI":"10.1016\/j.eswa.2010.12.006","volume":"38","author":"MS Abadeh","year":"2011","unstructured":"Abadeh, M. S., Mohamadi, H., & Habibi, J. (2011). Design and analysis of genetic fuzzy systems for intrusion detection in computer networks. Expert Systems with Applications: An International Journal, 38, 7067\u20137075.","journal-title":"Expert Systems with Applications: An International Journal"},{"key":"9332_CR2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.jnca.2015.11.016","volume":"60","author":"M Ahmed","year":"2016","unstructured":"Ahmed, M., Naser Mahmood, A., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19\u201331.","journal-title":"Journal of Network and Computer Applications"},{"key":"9332_CR3","doi-asserted-by":"crossref","unstructured":"Aljawarneh, S. A., Radhakrishna, V., & Kumar, G. R. (2017). A fuzzy measure for intrusion and anomaly detection. In 2017 International conference on engineering and MIS (ICEMIS) (pp. 1\u20136).","DOI":"10.1109\/ICEMIS.2017.8273113"},{"key":"9332_CR4","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1587\/transfun.E100.A.176","volume":"100","author":"ME Aminanto","year":"2017","unstructured":"Aminanto, M. E., Kim, H., Kim, K.-M., & Kim, K. (2017). Another fuzzy anomaly detection system based on ant clustering algorithm. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 100, 176\u2013183.","journal-title":"IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences"},{"key":"9332_CR5","doi-asserted-by":"crossref","unstructured":"Aparicio-Navarro, F. J., Kyriakopoulos, K. G., Parish, D. J., & Chambers, J. A. (2016). Adding contextual information to intrusion detection systems using fuzzy cognitive maps. In 2016 IEEE International multi-disciplinary conference on cognitive methods in situation awareness and decision support (CogSIMA) (pp. 180\u2013186).","DOI":"10.1109\/COGSIMA.2016.7497807"},{"key":"9332_CR6","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.1007\/s13042-016-0557-4","volume":"8","author":"RAR Ashfaq","year":"2017","unstructured":"Ashfaq, R. A. R., He, Y.-L., & Chen, D.-G. (2017). Toward an efficient fuzziness based instance selection methodology for intrusion detection system. International Journal of Machine Learning and Cybernetics, 8, 1767\u20131776.","journal-title":"International Journal of Machine Learning and Cybernetics"},{"key":"9332_CR7","doi-asserted-by":"crossref","unstructured":"Asmuss, J., & Lauks, G. (2015). Network traffic classification for anomaly detection fuzzy clustering based approach. In 2015 12th International conference on fuzzy systems and knowledge discovery (FSKD) (pp. 313\u2013318).","DOI":"10.1109\/FSKD.2015.7381960"},{"key":"9332_CR8","doi-asserted-by":"crossref","first-page":"9485","DOI":"10.1109\/ACCESS.2017.2702341","volume":"5","author":"MVOD Assis","year":"2017","unstructured":"Assis, M. V. O. D., Hamamoto, A. H., Abr\u00e3o, T., & Proen\u00e7a, M. L. (2017). A game theoretical based system using holt-winters and genetic algorithm with fuzzy logic for DoS\/DDoS mitigation on SDN networks. IEEE Access, 5, 9485\u20139496.","journal-title":"IEEE Access"},{"key":"9332_CR9","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1109\/SURV.2013.052213.00046","volume":"16","author":"MH Bhuyan","year":"2013","unstructured":"Bhuyan, M. H., Bhattacharyya, D. K., & Kalita, J. K. (2013). Network anomaly detection: Methods, systems and tools. IEEE Communications Surveys & Tutorials, 16, 303\u2013336.","journal-title":"IEEE Communications Surveys & Tutorials"},{"key":"9332_CR10","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1109\/SURV.2013.052213.00046","volume":"16","author":"MH Bhuyan","year":"2014","unstructured":"Bhuyan, M. H., Bhattacharyya, D. K., & Kalita, J. K. (2014). Network anomaly detection: Methods, systems and tools. IEEE Communications Surveys & Tutorials, 16, 303\u2013336.","journal-title":"IEEE Communications Surveys & Tutorials"},{"key":"9332_CR11","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1016\/j.future.2015.09.021","volume":"56","author":"A Botta","year":"2016","unstructured":"Botta, A., De Donato, W., Persico, V., & Pescap\u00e9, A. (2016). Integration of cloud computing and internet of things: A survey. Future Generation Computer Systems, 56, 684\u2013700.","journal-title":"Future Generation Computer Systems"},{"key":"9332_CR12","doi-asserted-by":"crossref","unstructured":"Chandrasekhar, A., & Raghuveer, K. (2013). An effective technique for intrusion detection using neuro-fuzzy and radial SVM classifier. In Computer networks and communications (NetCom): Proceedings of the fourth international conference on networks and communications (p. 499).","DOI":"10.1007\/978-1-4614-6154-8_49"},{"key":"9332_CR13","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1016\/j.compeleceng.2017.10.011","volume":"71","author":"M Chen","year":"2017","unstructured":"Chen, M., Wang, N., Zhou, H., & Chen, Y. (2017). FCM technique for efficient intrusion detection system for wireless networks in cloud environment. Computers & Electrical Engineering, 71, 978\u2013987.","journal-title":"Computers & Electrical Engineering"},{"key":"9332_CR14","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.cose.2018.01.023","volume":"75","author":"Z Chiba","year":"2018","unstructured":"Chiba, Z., Abghour, N., Moussaid, K., El Omri, A., & Rida, M. (2018). A novel architecture combined with optimal parameters for back propagation neural networks applied to anomaly network intrusion detection. Computers & Security, 75, 36\u201358.","journal-title":"Computers & Security"},{"key":"9332_CR15","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.aeue.2017.01.025","volume":"74","author":"R Devi","year":"2017","unstructured":"Devi, R., Jha, R. K., Gupta, A., Jain, S., & Kumar, P. (2017). Implementation of intrusion detection system using adaptive neuro-fuzzy inference system for 5G wireless communication network. AEUE-International Journal of Electronics and Communications, 74, 94\u2013106.","journal-title":"AEUE-International Journal of Electronics and Communications"},{"key":"9332_CR16","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s00521-016-2812-8","volume":"30","author":"OE Elejla","year":"2018","unstructured":"Elejla, O. E., Belaton, B., Anbar, M., & Alnajjar, A. (2018). Intrusion detection systems of ICMPv6-based DDoS attacks. Neural Computing and Applications, 30, 45\u201356.","journal-title":"Neural Computing and Applications"},{"key":"9332_CR17","doi-asserted-by":"crossref","first-page":"4349","DOI":"10.1016\/j.asoc.2010.12.004","volume":"11","author":"HT Elshoush","year":"2011","unstructured":"Elshoush, H. T., & Osman, I. M. (2011). Alert correlation in collaborative intelligent intrusion detection systems\u2014A survey. Applied Soft Computing, 11, 4349\u20134365.","journal-title":"Applied Soft Computing"},{"key":"9332_CR18","doi-asserted-by":"crossref","unstructured":"Feizollah, A., Shamshirband, S., Anuar, N. B., Salleh, R., & Mat Kiah, M. L. (2013). Anomaly detection using cooperative fuzzy logic controller. In FIRA RoboWorld Congress (pp. 220\u2013231). Berlin.","DOI":"10.1007\/978-3-642-40409-2_19"},{"key":"9332_CR19","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1007\/s11235-018-0475-8","volume":"70","author":"G Fernandes","year":"2019","unstructured":"Fernandes, G., Rodrigues, J. J. P. C., Carvalho, L. F., Al-Muhtadi, J. F., & Proen\u00e7a, M. L. (2019). A comprehensive survey on network anomaly detection. Telecommunication Systems, 70, 447\u2013489.","journal-title":"Telecommunication Systems"},{"key":"9332_CR20","doi-asserted-by":"crossref","first-page":"1750","DOI":"10.1016\/j.proeng.2012.06.213","volume":"38","author":"S Ganapathy","year":"2012","unstructured":"Ganapathy, S., Kulothungan, K., Yogesh, P., & Kannan, A. (2012). A novel weighted fuzzy C-means clustering based on immune genetic algorithm for intrusion detection. Procedia Engineering, 38, 1750\u20131757.","journal-title":"Procedia Engineering"},{"key":"9332_CR21","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/s40815-015-0080-x","volume":"3","author":"P Ganeshkumar","year":"2016","unstructured":"Ganeshkumar, P., & Pandeeswari, N. (2016). Adaptive neuro-fuzzy-based anomaly detection system in cloud. International Journal of Fuzzy Systems, 3, 367\u2013378.","journal-title":"International Journal of Fuzzy Systems"},{"key":"9332_CR22","doi-asserted-by":"crossref","first-page":"6797","DOI":"10.1007\/s00500-018-3407-3","volume":"22","author":"D Gao","year":"2018","unstructured":"Gao, D., Liu, Z., Liu, Y., Foh, C. H., Zhi, T., & Chao, H.-C. (2018). Defending against packet-in messages flooding attack under SDN context. Soft Computing, 22, 6797\u20136809.","journal-title":"Soft Computing"},{"key":"9332_CR23","doi-asserted-by":"crossref","unstructured":"Garcia, J. M. G. (2011). Discrete fuzzy transform applied to computer anomaly detection. In 2011 Annual meeting of the North American fuzzy information processing society (NAFIPS) (pp. 1\u20134).","DOI":"10.1109\/NAFIPS.2011.5751919"},{"key":"9332_CR24","doi-asserted-by":"crossref","first-page":"798","DOI":"10.1016\/j.compeleceng.2017.07.008","volume":"71","author":"S Garg","year":"2017","unstructured":"Garg, S., & Batra, S. (2017). Fuzzified cuckoo based clustering technique for network anomaly detection. Computers & Electrical Engineering, 71, 798\u2013817.","journal-title":"Computers & Electrical Engineering"},{"key":"9332_CR25","first-page":"352","volume":"14","author":"F Geramiraz","year":"2012","unstructured":"Geramiraz, F., Memaripour, A. S., & Abbaspour, M. (2012). Adaptive anomaly-based intrusion detection system using fuzzy controller. International Journal of Network Security, 14, 352\u2013361.","journal-title":"International Journal of Network Security"},{"key":"9332_CR26","doi-asserted-by":"crossref","unstructured":"Gladkykh, T., Hnot, T., & Solskyy, V. (2016). Fuzzy logic inference for unsupervised anomaly detection. In IEEE First international conference on data stream mining and processing (DSMP) (pp. 42\u201347).","DOI":"10.1109\/DSMP.2016.7583504"},{"key":"9332_CR27","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.neucom.2016.06.021","volume":"214","author":"C Guo","year":"2016","unstructured":"Guo, C., Ping, Y., Liu, N., & Luo, S.-S. (2016). A two-level hybrid approach for intrusion detection. Neurocomputing, 214, 391\u2013400.","journal-title":"Neurocomputing"},{"key":"9332_CR28","doi-asserted-by":"crossref","unstructured":"Hadri, A., Chougdali, K., & Touahni, R. (2016). Intrusion detection system using PCA and fuzzy PCA techniques. In International conference on advanced communication systems and information security (ACOSIS) (pp. 1\u20137).","DOI":"10.1109\/ACOSIS.2016.7843930"},{"key":"9332_CR29","doi-asserted-by":"crossref","unstructured":"Hadri, A., Chougdali, K., & Touahni, R. (2017). Identifying intrusions in computer networks using robust fuzzy PCA. In 2017 IEEE\/ACS 14th International conference on computer systems and applications (AICCSA) (pp. 1261\u20131268).","DOI":"10.1109\/AICCSA.2017.78"},{"key":"9332_CR30","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.comnet.2018.02.028","volume":"136","author":"V Hajisalem","year":"2018","unstructured":"Hajisalem, V., & Babaie, S. (2018). A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection. Computer Networks, 136, 37\u201350.","journal-title":"Computer Networks"},{"key":"9332_CR31","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.eswa.2017.09.013","volume":"92","author":"AH Hamamoto","year":"2018","unstructured":"Hamamoto, A. H., Carvalho, L. F., Sampaio, L. D. H., Abr\u00e3o, T., & Proen\u00e7a, M. L., Jr. (2018). Network anomaly detection system using genetic algorithm and fuzzy logic. Expert Systems with Applications, 92, 390\u2013402.","journal-title":"Expert Systems with Applications"},{"key":"9332_CR32","first-page":"427","volume":"53","author":"SM Hameed","year":"2012","unstructured":"Hameed, S. M., & Sulaiman, S. S. (2012). Intrusion detection using a mixed features fuzzy clustering algorithm. Iraq Journal of Science (IJS), 53, 427\u2013434.","journal-title":"Iraq Journal of Science (IJS)"},{"key":"9332_CR33","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1186\/s13638-019-1402-8","volume":"2019","author":"AP Haripriya","year":"2019","unstructured":"Haripriya, A. P., & Kulothungan, K. (2019). Secure-MQTT: An efficient fuzzy logic-based approach to detect DoS attack in MQTT protocol for internet of things. EURASIP Journal on Wireless Communications and Networking, 2019, 90.","journal-title":"EURASIP Journal on Wireless Communications and Networking"},{"key":"9332_CR34","doi-asserted-by":"crossref","unstructured":"Hosseinpour, M., Seno, S. A. H., Moghaddam, M. H. Y., & Roshkhari, H. K. (2016). An anomaly based VoIP DoS attack detection and prevention method using fuzzy logic. In 2016 8th International symposium on telecommunications (IST) (pp. 713\u2013718).","DOI":"10.1109\/ISTEL.2016.7881916"},{"key":"9332_CR35","unstructured":"Hu, L., Li, T., Xie, N., & Hu, J. (2015). False positive elimination in intrusion detection based on clustering. In 2015 12th International conference on fuzzy systems and knowledge discovery (FSKD) (pp. 519\u2013523)."},{"key":"9332_CR36","doi-asserted-by":"crossref","unstructured":"Iranmanesh, S. M., Mohammadi, M., Akbari, A., & Nassersharif, B. (2011). Improving detection rate in intrusion detection systems using FCM clustering to select meaningful landmarks in incremental landmark isomap algorithm. In Theoretical and mathematical foundations of computer science (pp. 46\u201353). Berlin: Springer.","DOI":"10.1007\/978-3-642-24999-0_7"},{"key":"9332_CR37","doi-asserted-by":"crossref","unstructured":"Kannan, A., Maguire, G. Q., Sharma, A., & Schoo, P. (2012). Genetic algorithm based feature selection algorithm for effective intrusion detection in cloud networks. In 2012 IEEE 12th International conference on data mining workshops (ICDMW) (pp. 416\u2013423).","DOI":"10.1109\/ICDMW.2012.56"},{"key":"9332_CR38","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.asoc.2016.07.039","volume":"49","author":"D Karaboga","year":"2016","unstructured":"Karaboga, D., & Kaya, E. (2016). An adaptive and hybrid artificial bee colony algorithm (aABC) for ANFIS training. Applied Soft Computing, 49, 423\u2013436.","journal-title":"Applied Soft Computing"},{"key":"9332_CR39","first-page":"1","volume":"52","author":"D Karaboga","year":"2018","unstructured":"Karaboga, D., & Kaya, E. (2018). Adaptive network based fuzzy inference system (ANFIS) training approaches: A comprehensive survey. Artificial Intelligence Review, 52, 1\u201331.","journal-title":"Artificial Intelligence Review"},{"key":"9332_CR40","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1016\/j.neucom.2014.08.070","volume":"149","author":"A Karami","year":"2015","unstructured":"Karami, A., & Guerrero-Zapata, M. (2015). A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks. Neurocomputing, 149, 1253\u20131269.","journal-title":"Neurocomputing"},{"key":"9332_CR41","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1007\/s13369-017-2634-8","volume":"43","author":"S Khan","year":"2017","unstructured":"Khan, S., Gani, A., Wahid, A., & Singh, P. (2017). Feature selection of denial-of-service attacks using entropy and granular computing. Arabian Journal for Science and Engineering, 43, 499\u2013508.","journal-title":"Arabian Journal for Science and Engineering"},{"key":"9332_CR42","doi-asserted-by":"crossref","unstructured":"Khazaee, S., & Rad, M. S. (2013). Using fuzzy C-means algorithm for improving intrusion detection performance. In 2013 13th Iranian conference on fuzzy systems (IFSC) (pp. 1\u20134).","DOI":"10.1109\/IFSC.2013.6675669"},{"key":"9332_CR43","doi-asserted-by":"crossref","unstructured":"Kumar, G. R., Mangathayaru, N., & Narsimha, G. (2016). An approach for intrusion detection using fuzzy feature clustering. In International conference on engineering and MIS (ICEMIS) (pp. 1\u20138).","DOI":"10.1109\/ICEMIS.2016.7745345"},{"key":"9332_CR44","doi-asserted-by":"crossref","unstructured":"Kumar, G. R., Mangathayaru, N., Narsimha, G., & Cheruvu, A. (2018). Feature clustering for anomaly detection using improved fuzzy membership function. Presented at the proceedings of the fourth international conference on engineering and MIS 2018, Istanbul, Turkey.","DOI":"10.1145\/3234698.3234733"},{"key":"9332_CR45","doi-asserted-by":"crossref","unstructured":"Kumar, K. A., & Mohan, V. N. (2014). Adaptive fuzzy neural network model for intrusion detection. In 2014 International conference on contemporary computing and informatics (IC3I) (pp. 987\u2013991).","DOI":"10.1109\/IC3I.2014.7019811"},{"key":"9332_CR46","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.comcom.2012.09.010","volume":"36","author":"PAR Kumar","year":"2013","unstructured":"Kumar, P. A. R., & Selvakumar, S. (2013). Detection of distributed denial of service attacks using an ensemble of adaptive and hybrid neuro-fuzzy systems. Computer Communications, 36, 303\u2013319.","journal-title":"Computer Communications"},{"key":"9332_CR47","doi-asserted-by":"crossref","unstructured":"Lei, Y., Liu, J., & Yin, H. (2016). Intrusion detection techniques based on improved intuitionistic fuzzy neural networks. In 2016 International conference on intelligent networking and collaborative systems (INCoS) (pp. 518\u2013521).","DOI":"10.1109\/INCoS.2016.54"},{"key":"9332_CR48","doi-asserted-by":"crossref","unstructured":"Li, L., & Zhao, K.-N. (2011). A new intrusion detection system based on rough set theory and fuzzy support vector machine. In 2011 3rd International workshop on intelligent systems and applications (ISA) (pp. 1\u20135).","DOI":"10.1109\/ISA.2011.5873410"},{"key":"9332_CR49","doi-asserted-by":"crossref","unstructured":"Linda, O., Manic, M., Vollmer, T., & Wright, J. (2011). Fuzzy logic based anomaly detection for embedded network security cyber sensor. In 2011 IEEE Symposium on computational intelligence in cyber security (CICS) (pp. 202\u2013209).","DOI":"10.1109\/CICYBS.2011.5949392"},{"key":"9332_CR50","doi-asserted-by":"crossref","unstructured":"Liu, D., Lung, C.-H., Seddigh, N., & Nandy, B. (2014). Network traffic anomaly detection using adaptive density-based fuzzy clustering. In Proceedings of the 2014 IEEE 13th international conference on trust, security and privacy in computing and communications (pp. 823\u2013830).","DOI":"10.1109\/TrustCom.2014.109"},{"key":"9332_CR51","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1109\/TSMCC.2010.2050685","volume":"41","author":"S Mabu","year":"2011","unstructured":"Mabu, S., Chen, C., Lu, N., Shimada, K., & Hirasawa, K. (2011). An intrusion-detection model based on fuzzy class-association-rule mining using genetic network programming. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41, 130\u2013139.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)"},{"key":"9332_CR53","doi-asserted-by":"crossref","unstructured":"Masarat, S., Taheri, H., & Sharifian, S. (2014). A novel framework, based on fuzzy ensemble of classifiers for intrusion detection systems. In 2014 4th International eConference on computer and knowledge engineering (ICCKE) (pp. 165\u2013170).","DOI":"10.1109\/ICCKE.2014.6993345"},{"key":"9332_CR54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jnca.2017.03.003","volume":"87","author":"M Masdari","year":"2017","unstructured":"Masdari, M., & Ahmadzadeh, S. (2017). A survey and taxonomy of the authentication schemes in Telecare Medicine Information Systems. Journal of Network and Computer Applications, 87, 1\u201319.","journal-title":"Journal of Network and Computer Applications"},{"key":"9332_CR55","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.jnca.2017.04.008","volume":"91","author":"M Masdari","year":"2017","unstructured":"Masdari, M., Ahmadzadeh, S., & Bidaki, M. (2017). Key management in wireless body area network: Challenges and issues. Journal of Network and Computer Applications, 91, 36\u201351.","journal-title":"Journal of Network and Computer Applications"},{"key":"9332_CR56","doi-asserted-by":"crossref","first-page":"3724","DOI":"10.1002\/sec.1539","volume":"9","author":"M Masdari","year":"2016","unstructured":"Masdari, M., & Jalali, M. (2016). A survey and taxonomy of DoS attacks in cloud computing. Security and Communication Networks, 9, 3724\u20133751.","journal-title":"Security and Communication Networks"},{"key":"9332_CR52","doi-asserted-by":"crossref","first-page":"106301","DOI":"10.1016\/j.asoc.2020.106301","volume":"92","author":"M Masdari","year":"2020","unstructured":"Masdari, M., & Khezri, H. (2020). A survey and taxonomy of the fuzzy signature-based Intrusion Detection Systems. Applied Soft Computing, 92, 106301.","journal-title":"Applied Soft Computing"},{"key":"9332_CR57","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.jnca.2016.01.011","volume":"66","author":"M Masdari","year":"2016","unstructured":"Masdari, M., Nabavi, S. S., & Ahmadi, V. (2016a). An overview of virtual machine placement schemes in cloud computing. Journal of Network and Computer Applications, 66, 106\u2013127.","journal-title":"Journal of Network and Computer Applications"},{"key":"9332_CR58","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.jnca.2016.01.018","volume":"66","author":"M Masdari","year":"2016","unstructured":"Masdari, M., ValiKardan, S., Shahi, Z., & Azar, S. I. (2016b). Towards workflow scheduling in cloud computing: A comprehensive analysis. Journal of Network and Computer Applications, 66, 64\u201382.","journal-title":"Journal of Network and Computer Applications"},{"key":"9332_CR59","doi-asserted-by":"crossref","unstructured":"Masdari, M., & Zangakani, M. (2019). Green cloud computing using proactive virtual machine placement: Challenges and issues. Journal of Grid Computing, 1\u201333.","DOI":"10.1007\/s10723-019-09489-9"},{"key":"9332_CR60","doi-asserted-by":"crossref","unstructured":"Mazarbhuiya, F. A., AlZahrani, M. Y., & Georgieva, L. (2019). Anomaly detection using agglomerative hierarchical clustering algorithm. In International conference on information science and applications, Singapore (pp. 475\u2013484).","DOI":"10.1007\/978-981-13-1056-0_48"},{"key":"9332_CR61","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.jnca.2012.05.003","volume":"36","author":"C Modi","year":"2013","unstructured":"Modi, C., Patel, D., Borisaniya, B., Patel, H., Patel, A., & Rajarajan, M. (2013). A survey of intrusion detection techniques in cloud. Journal of Network and Computer Applications, 36, 42\u201357.","journal-title":"Journal of Network and Computer Applications"},{"key":"9332_CR62","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1109\/TFUZZ.2014.2322385","volume":"23","author":"M Moshtaghi","year":"2015","unstructured":"Moshtaghi, M., Bezdek, J. C., Leckie, C., Karunasekera, S., & Palaniswami, M. (2015). Evolving fuzzy rules for anomaly detection in data streams. IEEE Transactions on Fuzzy Systems, 23, 688\u2013700.","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"9332_CR63","first-page":"1","volume":"5","author":"N Moustafa","year":"2018","unstructured":"Moustafa, N., Slay, J., & Creech, G. (2018). Novel geometric area analysis technique for anomaly detection using trapezoidal area estimation on large-scale networks. IEEE Transactions on Big Data, 5, 1.","journal-title":"IEEE Transactions on Big Data"},{"key":"9332_CR64","first-page":"118","volume":"5","author":"M Mukosera","year":"2014","unstructured":"Mukosera, M., & Reddy, G. V. R. (2014). A clustering and fuzzy logic based intrusion detection system. International Journal of Scientific and Engineering Research, 5, 118\u2013124.","journal-title":"International Journal of Scientific and Engineering Research"},{"key":"9332_CR65","doi-asserted-by":"crossref","unstructured":"Nagaraja, A., Aljawarneh, S., & Prabhakara, H. S. (2018). PAREEKSHA: A machine learning approach for intrusion and anomaly detection. Presented at the proceedings of the first international conference on data science, E-learning and information systems, Madrid, Spain.","DOI":"10.1145\/3279996.3280032"},{"key":"9332_CR66","doi-asserted-by":"crossref","unstructured":"Naik, N. (2015). Fuzzy inference based intrusion detection system: FI-Snort. In 2015 IEEE International conference on computer and information technology; ubiquitous computing and communications; dependable, autonomic and secure computing; pervasive intelligence and computing (CIT\/IUCC\/DASC\/PICOM) (pp. 2062\u20132067).","DOI":"10.1109\/CIT\/IUCC\/DASC\/PICOM.2015.306"},{"key":"9332_CR67","doi-asserted-by":"crossref","first-page":"1878","DOI":"10.1109\/TFUZZ.2017.2755000","volume":"26","author":"N Naik","year":"2017","unstructured":"Naik, N., Diao, R., & Shen, Q. (2017). Dynamic fuzzy rule interpolation and its application to intrusion detection. IEEE Transactions on Fuzzy Systems, 26, 1878\u20131892.","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"9332_CR68","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/978-3-319-26450-9_7","volume-title":"Recent advances in computational intelligence in defense and security","author":"G N\u00e1poles","year":"2016","unstructured":"N\u00e1poles, G., Grau, I., Falcon, R., Bello, R., & Vanhoof, K. (2016). A granular intrusion detection system using rough cognitive networks. In R. Abielmona, R. Falcon, N. Zincir-Heywood, & H. A. Abbass (Eds.), Recent advances in computational intelligence in defense and security (pp. 169\u2013191). Cham: Springer International Publishing."},{"key":"9332_CR69","unstructured":"Ngamwitthayanon, N., & Wattanapongsakorn, N. (2011). Fuzzy-ART in network anomaly detection with feature-reduction dataset. In 2011 The 7th international conference on networked computing (INC) (pp. 116\u2013121)."},{"key":"9332_CR70","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1007\/s11036-015-0644-x","volume":"21","author":"N Pandeeswari","year":"2016","unstructured":"Pandeeswari, N., & Kumar, G. (2016). Anomaly detection system in cloud environment using fuzzy clustering based ANN. Mobile Networks and Applications, 21, 494\u2013505.","journal-title":"Mobile Networks and Applications"},{"key":"9332_CR71","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1504\/IJITST.2018.093367","volume":"8","author":"KR Prabha","year":"2018","unstructured":"Prabha, K. R., & Jeyanthi, N. (2018). Intelligent intrusion detection system using temporal analysis and type-2 fuzzy neural classification. International Journal of Internet Technology and Secured Transactions, 8, 167\u2013184.","journal-title":"International Journal of Internet Technology and Secured Transactions"},{"key":"9332_CR72","doi-asserted-by":"crossref","unstructured":"Rabatel, J., Bringay, S., & Poncelet, P. (2010). Fuzzy anomaly detection in monitoring sensor data. In 2010 IEEE International conference on fuzzy systems (FUZZ) (pp. 1\u20138).","DOI":"10.1109\/FUZZY.2010.5584253"},{"key":"9332_CR73","first-page":"62","volume":"1","author":"S Raja","year":"2016","unstructured":"Raja, S., & Ramaiah, S. (2016). An efficient fuzzy-based hybrid system to cloud intrusion detection. International Journal of Fuzzy Systems, 1, 62\u201377.","journal-title":"International Journal of Fuzzy Systems"},{"key":"9332_CR74","doi-asserted-by":"crossref","unstructured":"Shalini, S., Shafreen Nihara, A., Sathiya Priya, L., & Vetriselvi, V. (2018). Intrusion detection system for software-defined networks using fuzzy system. In Proceedings of the international conference on computing and communication systems, Singapore (pp. 603\u2013620).","DOI":"10.1007\/978-981-10-6890-4_59"},{"key":"9332_CR75","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.measurement.2014.04.034","volume":"55","author":"S Shamshirband","year":"2014","unstructured":"Shamshirband, S., Amini, A., Anuar, N. B., Mat Kiah, M. L., Teh, Y. W., & Furnell, S. (2014). D-FICCA: A density-based fuzzy imperialist competitive clustering algorithm for intrusion detection in wireless sensor networks. Measurement, 55, 212\u2013226.","journal-title":"Measurement"},{"key":"9332_CR76","doi-asserted-by":"crossref","unstructured":"Sharma, R., & Chaurasia, S. (2018). An enhanced approach to fuzzy C-means clustering for anomaly detection. In Proceedings of first international conference on smart system, innovations and computing (pp. 623\u2013636).","DOI":"10.1007\/978-981-10-5828-8_60"},{"key":"9332_CR77","first-page":"2171","volume":"30","author":"V Sharma","year":"2018","unstructured":"Sharma, V., Kumar, R., Cheng, W., Atiquzzaman, M., Srinivasan, K., & Zomaya, A. Y. (2018). NHAD: Neuro-fuzzy based horizontal anomaly detection in online social networks. IEEE Transactions on Knowledge and Data Engineering, 30, 2171\u20132184.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"9332_CR78","doi-asserted-by":"crossref","unstructured":"Shekokar, N., & Devane, S. (2011). Anomaly detection in VoIP system using neural network and fuzzy logic. In Computational intelligence and information technology (pp. 537\u2013542). Springer.","DOI":"10.1007\/978-3-642-25734-6_92"},{"issue":"6","key":"9332_CR79","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1049\/iet-ifs.2017.0500","volume":"12","author":"KJ Singh","year":"2018","unstructured":"Singh, K. J., Thongam, K., & De, T. (2018). Detection and differentiation of application layer DDoS attack from flash events using fuzzy-GA computation. IET Information Security, 12(6), 502\u2013512. https:\/\/doi.org\/10.1049\/iet-ifs.2017.0500.","journal-title":"IET Information Security"},{"key":"9332_CR80","doi-asserted-by":"crossref","unstructured":"Song, J., Zhu, Z., Scully, P., & Price, C. (2013). Selecting features for anomaly intrusion detection: A novel method using fuzzy C means and decision tree classification. In Cyberspace safety and security: 5th international symposium, CSS 2013, Zhangjiajie, China, November 13\u201315, proceedings (p. 299).","DOI":"10.1007\/978-3-319-03584-0_22"},{"key":"9332_CR81","doi-asserted-by":"crossref","unstructured":"Su, M.-Y., Lin, C.-Y., Chien, S.-W., & Hsu, H.-C. (2011). Genetic-fuzzy association rules for network intrusion detection systems. In 2011 IEEE International conference on fuzzy systems (FUZZ) (pp. 2046\u20132052).","DOI":"10.1109\/FUZZY.2011.6007555"},{"key":"9332_CR82","doi-asserted-by":"crossref","unstructured":"Sujata, B., & Varma, P. R. K. (2017). Combining fuzzy C-means and KNN algorithms in performance improvement of intrusion detection system. In Proceedings of international conference on computational intelligence and data engineering: ICCIDE 2017 (p. 359).","DOI":"10.1007\/978-981-10-6319-0_30"},{"key":"9332_CR83","doi-asserted-by":"crossref","first-page":"502","DOI":"10.4218\/etrij.15.0114.0275","volume":"37","author":"R Sujendran","year":"2015","unstructured":"Sujendran, R., & Arunachalam, M. (2015). Hybrid fuzzy adaptive Wiener filtering with optimization for intrusion detection. ETRI Journal, 37, 502\u2013511.","journal-title":"ETRI Journal"},{"key":"9332_CR84","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/j.asoc.2008.06.001","volume":"9","author":"A Tajbakhsh","year":"2009","unstructured":"Tajbakhsh, A., Rahmati, M., & Mirzaei, A. (2009). Intrusion detection using fuzzy association rules. Applied Soft Computing, 9, 462\u2013469.","journal-title":"Applied Soft Computing"},{"key":"9332_CR85","doi-asserted-by":"crossref","first-page":"64801","DOI":"10.1109\/ACCESS.2018.2873291","volume":"6","author":"J Wang","year":"2018","unstructured":"Wang, J., Zhao, H., Xu, J., Li, H., Zhu, H., Chao, S., et al. (2018). Using intuitionistic fuzzy set for anomaly detection of network traffic from flow interaction. IEEE Access, 6, 64801\u201364816.","journal-title":"IEEE Access"},{"key":"9332_CR86","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.asoc.2009.06.019","volume":"10","author":"SX Wu","year":"2010","unstructured":"Wu, S. X., & Banzhaf, W. (2010). The use of computational intelligence in intrusion detection systems: A review. Applied Soft Computing, 10, 1\u201335.","journal-title":"Applied Soft Computing"},{"key":"9332_CR87","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.knosys.2018.11.026","volume":"165","author":"R Xiao","year":"2019","unstructured":"Xiao, R., Su, J., Du, X., Jiang, J., Lin, X., & Lin, L. (2019). SFAD: Toward effective anomaly detection based on session feature similarity. Knowledge-Based Systems, 165, 149\u2013156.","journal-title":"Knowledge-Based Systems"},{"key":"9332_CR88","unstructured":"Xie, L., Wang, Y., Chen, L., & Yue, G. (2010). An anomaly detection method based on fuzzy C-means clustering algorithm. In The second international symposium on networking and network security (ISNNS 2010) (p. 89)."},{"key":"9332_CR89","doi-asserted-by":"crossref","unstructured":"Yu, Y., & Wu, H. (2012). Anomaly intrusion detection based upon data mining techniques and fuzzy logic. In 2012 IEEE International conference on systems, man, and cybernetics (SMC) (pp. 514\u2013517).","DOI":"10.1109\/ICSMC.2012.6377776"},{"key":"9332_CR90","doi-asserted-by":"crossref","unstructured":"Zhang, H., & Zhang, X. (2012). Intrusion detection based on improvement of genetic fuzzy C-means algorithm. In Advances in information technology and industry applications (pp. 339\u2013346). Berlin: Springer.","DOI":"10.1007\/978-3-642-26001-8_44"},{"key":"9332_CR91","doi-asserted-by":"crossref","unstructured":"Zhang, L., Bai, Z., Luo, S., Cui, G., & Li, X. (2013). A dynamic artificial immune-based intrusion detection method using rough and fuzzy set. In 2013 International conference on information and network security (ICINS 2013) (pp. 1\u20137).","DOI":"10.1049\/cp.2013.2458"},{"key":"9332_CR92","doi-asserted-by":"crossref","unstructured":"Zhang, Z., & Gu, B. (2016). Intrusion detection network based on fuzzy C-means and particle swarm optimization. In Proceedings of the 6th international Asia conference on industrial engineering and management innovation (pp. 111\u2013119).","DOI":"10.2991\/978-94-6239-145-1_12"},{"key":"9332_CR93","doi-asserted-by":"crossref","unstructured":"Zhong, J., Wu, H., & Lai, Y. (2011). Intrusion detection using evolving fuzzy classifiers. In 2011 6th IEEE Joint international information technology and artificial intelligence conference (ITAIC) (pp. 119\u2013122).","DOI":"10.1109\/ITAIC.2011.6030165"},{"key":"9332_CR94","doi-asserted-by":"crossref","unstructured":"Zolotukhin, M., Kokkonen, T., H\u00e4m\u00e4l\u00e4inen, T., & Siltanen, J. (2016). Weighted fuzzy clustering for online detection of application DDoS attacks in encrypted network traffic. In Internet of things, smart spaces, and next generation networks and systems (pp. 326\u2013338). Cham.","DOI":"10.1007\/978-3-319-46301-8_27"}],"container-title":["Fuzzy Optimization and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10700-020-09332-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10700-020-09332-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10700-020-09332-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T23:19:15Z","timestamp":1626995955000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10700-020-09332-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,23]]},"references-count":94,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["9332"],"URL":"https:\/\/doi.org\/10.1007\/s10700-020-09332-x","relation":{},"ISSN":["1568-4539","1573-2908"],"issn-type":[{"value":"1568-4539","type":"print"},{"value":"1573-2908","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,23]]},"assertion":[{"value":"23 July 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}