{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T04:16:30Z","timestamp":1744258590022,"version":"3.40.4"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,10,3]],"date-time":"2021-10-03T00:00:00Z","timestamp":1633219200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,10,3]],"date-time":"2021-10-03T00:00:00Z","timestamp":1633219200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,2]]},"DOI":"10.1007\/s00521-021-06565-8","type":"journal-article","created":{"date-parts":[[2021,10,4]],"date-time":"2021-10-04T21:06:36Z","timestamp":1633381596000},"page":"2869-2882","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Optimization of vector convolutional deep neural network using binary real cumulative incarnation for detection of distributed denial of service attacks"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3660-380X","authenticated-orcid":false,"given":"N. G. Bhuvaneswari","family":"Amma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.","family":"Selvakumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,3]]},"reference":[{"issue":"4","key":"6565_CR1","doi-asserted-by":"publisher","first-page":"2046","DOI":"10.1109\/SURV.2013.031413.00127","volume":"15","author":"ST Zargar","year":"2013","unstructured":"Zargar ST, Joshi J, Tipper D (2013) A survey of defense mechanisms against distributed denial of service (ddos) flooding attacks. IEEE Commun Surv Tutor 15(4):2046","journal-title":"IEEE Commun Surv Tutor"},{"key":"6565_CR2","first-page":"84","volume":"73","author":"PD Bojovic","year":"2019","unstructured":"Bojovic PD, Basicevic I, Ocovaj S (2019) amd M. Popovic, A practical approach to detection of distributed denial of service attacks using a hybrid detection method, Computers & Electrical Engineering 73:84\u201396","journal-title":"Popovic, A practical approach to detection of distributed denial of service attacks using a hybrid detection method, Computers & Electrical Engineering"},{"key":"6565_CR3","unstructured":"Oleg Kupreev AG, Ekaterina Badovskaya (2019) Kaspersky lab ddos threat report. https:\/\/securelist.com\/ddos-attacks-in-q1-2019\/90792\/"},{"key":"6565_CR4","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/j.neucom.2015.04.101","volume":"172","author":"A Saied","year":"2016","unstructured":"Saied A, Overill RE, Radzik T (2016) Detection of known and unknown ddos attacks using artificial neural networks. Neurocomputing 172:385","journal-title":"Neurocomputing"},{"key":"6565_CR5","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85","journal-title":"Neural Netw"},{"issue":"7","key":"6565_CR6","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1093\/comjnl\/bxz064","volume":"63","author":"M Asad","year":"2020","unstructured":"Asad M, Asim M, Javed T, Beg MO, Mujtaba H, Abbas S (2020) DeepDetect: Detection of distributed denial of service attacks using deep learning. Comput J 63(7):983\u2013994","journal-title":"Comput J"},{"key":"6565_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cose.2020.102151","volume":"102","author":"SM Sohi","year":"2021","unstructured":"Sohi SM, Seifert JP, Ganji F (2021) RNNIDS: Enhancing network intrusion detection systems through deep learning. Comput Secur 102:1\u201323","journal-title":"Comput Secur"},{"key":"6565_CR8","doi-asserted-by":"crossref","unstructured":"Amma BN, Selvakumar S, Velusamy RL (2020) SAGRU: A stacked autoencoder-based gated recurrent unit approach to intrusion detection, in Springer Intelligent Data Engineering and Analytics, pp. 41\u201350","DOI":"10.1007\/978-981-15-5679-1_5"},{"key":"6565_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.comnet.2020.107390","volume":"180","author":"O Kasim","year":"2020","unstructured":"Kasim O (2020) An efficient and robust deep learning based network anomaly detection against distributed denial of service attacks. Comput Netw 180:1\u201310","journal-title":"Comput Netw"},{"key":"6565_CR10","doi-asserted-by":"publisher","first-page":"1707","DOI":"10.1109\/LWC.2021.3077946","volume":"10","author":"R Zhao","year":"2021","unstructured":"Zhao R, Yin J, Gui G, Adebisi B, Obtsuki T, Gacanin H, Sari H (2021) An efficient intrusion detection method based on dynamic autoencoder. IEEE Wirel Commun Lett 10:1707","journal-title":"IEEE Wirel Commun Lett"},{"key":"6565_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.comnet.2020.107784","volume":"186","author":"M Ge","year":"2021","unstructured":"Ge M, Syed NF, Fu X, Baig Z, Kelly AR (2021) Towards a deep learning driven intrusion detection approach for Internet of Things. Comput Netw 186:1\u201311","journal-title":"Comput Netw"},{"key":"6565_CR12","doi-asserted-by":"crossref","unstructured":"Vinaykumar R, Soman KP, Poornachandran P (2017) Applying convolutional neural network for network intrusion detection, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (IEEE, 2017), pp. 1222\u20131228","DOI":"10.1109\/ICACCI.2017.8126009"},{"key":"6565_CR13","doi-asserted-by":"crossref","unstructured":"Amma NGB, Selvakumar S (2018) VCDeepFL: Vector Convolutional Deep Feature Learning Approach for Identification of Known and Unknown Denial of Service Attacks, in TENCON 2018\u20132018 IEEE Region 10 Conference (IEEE, 2018), pp. 0640\u20130645","DOI":"10.1109\/TENCON.2018.8650225"},{"issue":"5","key":"6565_CR14","doi-asserted-by":"publisher","first-page":"4972","DOI":"10.1016\/j.eswa.2010.09.154","volume":"38","author":"L Zhao","year":"2011","unstructured":"Zhao L, Qian F (2011) Tuning the structure and parameters of a neural network using cooperative binary real particle swarm optimization. Expert Syst Appl 38(5):4972","journal-title":"Expert Syst Appl"},{"key":"6565_CR15","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.eswa.2016.04.036","volume":"60","author":"A Bhardwaj","year":"2016","unstructured":"Bhardwaj A, Tiwari A, Bhardwaj H, Bhardwaj A (2016) A genetically optimized neural network model for multi-class classification. Expert Syst Appl 60:211","journal-title":"Expert Syst Appl"},{"key":"6565_CR16","doi-asserted-by":"crossref","unstructured":"Qolomany B, Maabreh M, Al-Fuqaha A, Gupta A, Benhaddou D (2017) Parameters optimization of deep learning models using Particle swarm optimization, in Wireless Communications and Mobile Computing Conference (IWCMC), 2017 13th International (IEEE, 2017), pp. 1285\u20131290","DOI":"10.1109\/IWCMC.2017.7986470"},{"key":"6565_CR17","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.neucom.2013.03.069","volume":"137","author":"JJ Liang","year":"2014","unstructured":"Liang JJ, Qu BY, Mao X, Niu B, Wang D (2014) Differential evolution based on fitness Euclidean distance ratio for multimodal optimization. Neurocomputing 137:252","journal-title":"Neurocomputing"},{"key":"6565_CR18","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1016\/j.neucom.2019.02.047","volume":"340","author":"BN Amma","year":"2019","unstructured":"Amma BN, Selvakumar S (2019) Deep radial intelligence with cumulative incarnation approach for detecting denial of service attacks. Neurocomputing 340:294","journal-title":"Neurocomputing"},{"issue":"4","key":"6565_CR19","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1145\/382912.382923","volume":"3","author":"J McHugh","year":"2000","unstructured":"McHugh J (2000) Testing intrusion detection systems: a critique of the 1998 and 1999 Darpa intrusion detection system evaluations as performed by Lincoln laboratory. ACM Trans Inform Syst Secur TIS-SEC 3(4):262","journal-title":"ACM Trans Inform Syst Secur TIS-SEC"},{"key":"6565_CR20","doi-asserted-by":"crossref","unstructured":"Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the KDD CUP 99 data set, in Computational Intelligence for Security and Defense Applications, 2009. CISDA 2009. IEEE Symposium on (IEEE, 2009), pp. 1\u20136","DOI":"10.1109\/CISDA.2009.5356528"},{"issue":"1\u20133","key":"6565_CR21","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s10994-014-5473-9","volume":"101","author":"F Iglesias","year":"2015","unstructured":"Iglesias F, Zseby T (2015) Analysis of network traffic features for anomaly detection. Mach Learn 101(1\u20133):59","journal-title":"Mach Learn"},{"issue":"2","key":"6565_CR22","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/COMST.2015.2494502","volume":"18","author":"AL Buczak","year":"2016","unstructured":"Buczak AL, Guven E (2016) A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun Surv Tutor 18(2):1153","journal-title":"IEEE Commun Surv Tutor"},{"issue":"1","key":"6565_CR23","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1109\/COMST.2014.2336610","volume":"17","author":"DJ Weller-Fahy","year":"2015","unstructured":"Weller-Fahy DJ, Borghetti BJ, Sodemann AA (2015) A survey of distance and similarity measures used within network intrusion anomaly detection. IEEE Communications Surveys & Tutorials 17(1):70","journal-title":"IEEE Communications Surveys & Tutorials"},{"key":"6565_CR24","unstructured":"Amma N, Selvakumar S (2020) A statistical class center based triangle area vector method for detection of denial of service attacks, Cluster Computing-The Journal of Networks Software Tools and Applications"},{"key":"6565_CR25","doi-asserted-by":"crossref","unstructured":"Amma NGB, Subramanian S (2019) Feature Correlation Map based Statistical Approach for Denial of Service Attacks Detection, in 2019 5th International Conference on Computing Engineering and Design (ICCED) (IEEE, 2019), pp. 1\u20136","DOI":"10.1109\/ICCED46541.2019.9161080"},{"key":"6565_CR26","doi-asserted-by":"crossref","unstructured":"Velliangiri S, Premalatha J (2017) Intrusion detection of distributed denial of service attack in cloud, Cluster Computing pp. 1\u20139","DOI":"10.1007\/s10586-017-1149-0"},{"key":"6565_CR27","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1016\/j.neucom.2016.06.014","volume":"214","author":"A Prieto","year":"2016","unstructured":"Prieto A, Prieto B, Ortigosa EM, Ros E, Pelayo F, Ortega J, Rojas I (2016) Neural networks: an overview of early research, current frameworks and new challenges. Neurocomputing 214:242","journal-title":"Neurocomputing"},{"key":"6565_CR28","doi-asserted-by":"publisher","first-page":"3193","DOI":"10.1007\/s10489-018-1141-2","volume":"48","author":"M Idhammad","year":"2018","unstructured":"Idhammad M, Afdel K, Belouch M (2018) Semi-supervised machine learning approach for ddos detection. Appl Intell 48:3193","journal-title":"Appl Intell"},{"issue":"1","key":"6565_CR29","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1109\/TNN.2005.860885","volume":"17","author":"JT Tsai","year":"2006","unstructured":"Tsai JT, Chou JH, Liu TK (2006) Tuning the structure and parameters of a neural network by using hybrid taguchi-genetic algorithm. IEEE Trans Neural Netw 17(1):69","journal-title":"IEEE Trans Neural Netw"},{"issue":"6","key":"6565_CR30","doi-asserted-by":"publisher","first-page":"2450","DOI":"10.1109\/TNNLS.2017.2695223","volume":"29","author":"J Liu","year":"2018","unstructured":"Liu J, Gong M, Miao Q, Wang X, Li H (2018) Structure learning for deep neural networks based on multiobjective optimization. IEEE Trans Neural Netw Learn Syst 29(6):2450","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"7553","key":"6565_CR31","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436","journal-title":"Nature"},{"issue":"5","key":"6565_CR32","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","volume":"35","author":"N Tajbakhsh","year":"2016","unstructured":"Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imag 35(5):1299","journal-title":"IEEE Trans Med Imag"},{"issue":"1","key":"6565_CR33","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/TETCI.2017.2772792","volume":"2","author":"N Shone","year":"2018","unstructured":"Shone N, Ngoc TN, Phai VD, Shi Q (2018) A deep learning approach to network intrusion detection. IEEE Trans Emerg Topics Comput Intell 2(1):41","journal-title":"IEEE Trans Emerg Topics Comput Intell"},{"key":"6565_CR34","doi-asserted-by":"crossref","unstructured":"Yuan X, Li C, Li X (2017) DeepDefense: Identifying DDoS Attack via Deep Learning, in 2017 IEEE International Conference on Smart Computing (SMARTCOMP) (IEEE, 2017), pp. 1\u20138","DOI":"10.1109\/SMARTCOMP.2017.7946998"},{"key":"6565_CR35","doi-asserted-by":"crossref","unstructured":"Ghiasi-Shirazi K (2019) Generalizing the convolution operator in convolutional neural networks, Neural Processing Letters pp. 1\u201320","DOI":"10.1007\/s11063-019-10043-7"},{"key":"6565_CR36","doi-asserted-by":"crossref","unstructured":"Keserwani PK, Govil MC, Pilli ES (2021) An effective NIDS framework based on a comprehensive survey of feature optimization and classification techniques, Neural Computing and Applications pp. 1\u201321","DOI":"10.1007\/s00521-021-06093-5"},{"issue":"6","key":"6565_CR37","doi-asserted-by":"publisher","first-page":"1511","DOI":"10.1007\/s00500-014-1334-5","volume":"19","author":"M Mavrovouniotis","year":"2015","unstructured":"Mavrovouniotis M, Yang S (2015) Training neural networks with ant colony optimization algorithms for pattern classification. Soft Comput 19(6):1511","journal-title":"Soft Comput"},{"issue":"1","key":"6565_CR38","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.aei.2005.01.004","volume":"19","author":"E Elbeltagi","year":"2005","unstructured":"Elbeltagi E, Hegazy T, Grierson D (2005) Comparison among five evolutionary-based optimization algorithms. Adv Eng Inform 19(1):43","journal-title":"Adv Eng Inform"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06565-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06565-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06565-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T09:48:48Z","timestamp":1744192128000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06565-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,3]]},"references-count":38,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["6565"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06565-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2021,10,3]]},"assertion":[{"value":"29 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 September 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2021","order":3,"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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}