<?xml version="1.0" encoding="UTF-8"?>
<doi_batch version="4.3.0" xmlns="http://www.crossref.org/doi_resources_schema/4.3.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/doi_resources_schema/4.3.0 http://www.crossref.org/schema/deposit/doi_resources4.3.0.xsd">
<head>
<doi_batch_id>87ef4ddc-b891-4ef5-8601-4db9ea79ef53</doi_batch_id>
<depositor>
<name>beie</name>
<email_address>director@blueeyesintelligence.org</email_address>
</depositor>
</head>
<body>
<doi_citations>
<doi>10.35940/ijitee.F9862.0511622</doi>
<citation_list><citation key="ref0"><doi>10.1016/j.icte.2020.12.004</doi><unstructured_citation>V. Kanimozhi and T. P. Jacob, &quot;Artificial Intelligence outflanks all other machine learning classifiers in Network Intrusion Detection System on the realistic cyber dataset CSE-CIC-IDS2018 using cloud computing,&quot; ICT Express, vol. 7, no. 3, pp. 366-370, 2020, doi: 10.1016/j.icte.2020.12.004.</unstructured_citation></citation><citation key="ref1"><doi>10.1016/j.jksuci.2017.05.004</doi><unstructured_citation>G. Kaur, V. Saxena, and J. P. Gupta, &quot;Detection of TCP targeted high bandwidth attacks using self-similarity,&quot; J. King Saud Univ. - Comput. Inf. Sci., vol. 32, no. 1, pp. 35-49, Jan. 2020, doi: 10.1016/j.jksuci.2017.05.004.</unstructured_citation></citation><citation key="ref2"><doi>10.1016/j.cose.2021.102490</doi><unstructured_citation>C. Beaman, A. Barkworth, T. D. Akande, S. Hakak, and M. K. Khan, &quot;Ransomware: Recent advances, analysis, challenges and future research directions,&quot; Comput. Secur., vol. 111, p. 102490, 2021, doi: 10.1016/j.cose.2021.102490.</unstructured_citation></citation><citation key="ref3"><doi>10.1016/j.ijlcj.2016.07.002</doi><unstructured_citation>S. Ibrahim, &quot;Social and contextual taxonomy of cybercrime: Socioeconomic theory of Nigerian cybercriminals,&quot; Int. J. Law, Crime Justice, vol. 47, pp. 44-57, Dec. 2016, doi: 10.1016/j.ijlcj.2016.07.002.</unstructured_citation></citation><citation key="ref4"><doi>10.14569/IJACSA.2016.070159</doi><unstructured_citation>M. Alkasassbeh, G. Al-Naymat, A. B.A, and M. Almseidin, &quot;Detecting Distributed Denial of Service Attacks Using Data Mining Techniques,&quot; Int. J. Adv. Comput. Sci. Appl., vol. 7, no. 1, pp. 436-445, 2016, doi: 10.14569/ijacsa.2016.070159.</unstructured_citation></citation><citation key="ref5"><doi>10.1007/s40033-019-00194-1</doi><unstructured_citation>S. Gupta, J. Sarkar, A. Banerjee, N. R. Bandyopadhyay, and S. Ganguly, &quot;Grain Boundary Detection and Phase Segmentation of SEM Ferrite-Pearlite Microstructure Using SLIC and Skeletonization,&quot; J. Inst. Eng. Ser. D, vol. 100, no. 2, pp. 203-210, Oct. 2019, doi: 10.1007/s40033-019-00194-1.</unstructured_citation></citation><citation key="ref6"><doi>10.1016/j.cirpj.2010.07.005</doi><unstructured_citation>S. K. Singh and A. K. Gupta, &quot;Application of support vector regression in predicting thickness strains in hydro-mechanical deep drawing and comparison with ANN and FEM,&quot; CIRP J. Manuf. Sci. Technol., vol. 3, no. 1, pp. 66-72, 2010, doi: 10.1016/j.cirpj.2010.07.005.</unstructured_citation></citation><citation key="ref7"><doi>10.1109/ICoAC.2011.6165212</doi><unstructured_citation>T. Subbulakshmi, K. Balakrishnan, S. M. Shalinie, D. Anandkumar, V. Ganapathisubramanian, and K. Kannathal, &quot;Detection of DDoS attacks using Enhanced Support Vector Machines with real time generated dataset,&quot; 3rd Int. Conf. Adv. Comput. ICoAC 2011, pp. 17-22, 2011, doi: 10.1109/ICoAC.2011.6165212.</unstructured_citation></citation><citation key="ref8"><doi>10.11591/ij-ai.v2i2.1937</doi><unstructured_citation>H. Waguih, &quot;A Data Mining Approach for the Detection of Denial of Service Attack,&quot; IAES Int. J. Artif. Intell., vol. 2, no. 2, 2013, doi: 10.11591/ij-ai.v2i2.1937.</unstructured_citation></citation><citation key="ref9"><doi>10.5120/11388-6680</doi><unstructured_citation>J. KaurBains, K. Kumar Kaki, and K. Sharma, &quot;Intrusion Detection System with Multi Layer using Bayesian Networks,&quot; Int. J. Comput. Appl., vol. 67, no. 5, pp. 1-4, 2013, doi: 10.5120/11388-6680.</unstructured_citation></citation><citation key="ref10"><doi>10.1016/j.jksuci.2020.10.026</doi><unstructured_citation>&quot;Erratum regarding missing Declaration of Competing Interest statements in previously published articles (Journal of King Saud University - Computer and Information Sciences, (S1319157818300545), (10.1016/j.jksuci.2018.04.001)),&quot; Journal of King Saud University - Computer and Information Sciences, vol. 32, no. 10. King Saud bin Abdulaziz University, pp. 1206-1207, Dec. 01, 2020, doi: 10.1016/j.jksuci.2020.10.026.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>A. Bivens, C. Palagiri, R. Smith, B. Szymanski, and M. Embrechts, &quot;Network-based intrusion detection using neural networks,&quot; Intell. Eng. Syst. Through Artif. Neural Networks, vol. 12, pp. 579-584, 2002.</unstructured_citation></citation><citation key="ref12"><doi>10.1109/ICC.2007.206</doi><unstructured_citation>S. Seufert and D. O'brien, &quot;Machine learning for automatic defence against distributed denial of service attacks,&quot; in IEEE International Conference on Communications, 2007, pp. 1217-1222, doi: 10.1109/ICC.2007.206.</unstructured_citation></citation><citation key="ref13"><doi>10.21917/ijct.2013.0105</doi><unstructured_citation>S. T, P. P, P. C, M. M, A. A. J, and M. G, &quot;a Unified Approach for Detection and Prevention of Ddos Attacks Using Enhanced Support Vector Machines and Filtering Mechanisms,&quot; ICTACT J. Commun. Technol., vol. 04, no. 02, pp. 737-743, 2013, doi: 10.21917/ijct.2013.0105.</unstructured_citation></citation><citation key="ref14"><doi>10.1016/j.cogr.2021.04.001</doi><unstructured_citation>J. Wang and M. Wang, &quot;Review of the emotional feature extraction and classification using EEG signals,&quot; Cogn. Robot., vol. 1, no. December 2020, pp. 29-40, 2021, doi: 10.1016/j.cogr.2021.04.001.</unstructured_citation></citation><citation key="ref15"><doi>10.1016/j.engappai.2014.09.019</doi><unstructured_citation>G. G. Sundarkumar and V. Ravi, &quot;A novel hybrid undersampling method for mining unbalanced datasets in banking and insurance,&quot; Eng. Appl. Artif. Intell., vol. 37, pp. 368-377, 2015, doi: 10.1016/j.engappai.2014.09.019.</unstructured_citation></citation><citation key="ref16"><doi>10.1080/10106049.2017.1404141</doi><unstructured_citation>B. T. Pham and I. Prakash, &quot;Evaluation and comparison of LogitBoost Ensemble, Fisher's Linear Discriminant Analysis, logistic regression and support vector machines methods for landslide susceptibility mapping,&quot; Geocarto Int., vol. 34, no. 3, pp. 316-333, 2019, doi: 10.1080/10106049.2017.1404141.</unstructured_citation></citation><citation key="ref17"><doi>10.1109/AEECT.2011.6132529</doi><unstructured_citation>O. S. Al-Kadi, &quot;Supervised texture segmentation: A comparative study,&quot; 2011, doi: 10.1109/AEECT.2011.6132529.</unstructured_citation></citation><citation key="ref18"><doi>10.3390/s20164372</doi><unstructured_citation>Y. N. Soe, Y. Feng, P. I. Santosa, R. Hartanto, and K. Sakurai, &quot;Machine learning-based IoT-botnet attack detection with sequential architecture,&quot; Sensors (Switzerland), vol. 20, no. 16, pp. 1-15, Aug. 2020, doi: 10.3390/s20164372.</unstructured_citation></citation><citation key="ref19"><doi>10.35940/ijitee.A1024.0881019</doi><unstructured_citation>S. Gupta, &quot;Chan-vese segmentation of SEM ferrite-pearlite microstructure and prediction of grain boundary,&quot; Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 10, pp. 1495-1498, 2019, doi: 10.35940/ijitee.A1024.0881019.</unstructured_citation></citation><citation key="ref20"><doi>10.1016/j.matchemphys.2020.123286</doi><unstructured_citation>S. Gupta et al., &quot;Modelling the steel microstructure knowledge for in-silico recognition of phases using machine learning,&quot; Mater. Chem. Phys., vol. 252, no. May, p. 123286, Sep. 2020, doi: 10.1016/j.matchemphys.2020.123286.</unstructured_citation></citation><citation key="ref21"><doi>10.1016/j.iot.2021.100393</doi><unstructured_citation>I. H. Sarker, &quot;CyberLearning: Effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks,&quot; Internet of Things, vol. 14, p. 100393, Jun. 2021, doi: 10.1016/j.iot.2021.100393.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>S. Panda, A. K. Ghosh, A. Das, U. Dey, and S. Gupta, &quot;Machine Learning-based Linear regression way to deal with making data science model for checking the sufficiency of night curfew in Maharashtra , India,&quot; Int. J. Eng. Appl. Phys., vol. 1, no. 2, pp. 168-173, 2021.</unstructured_citation></citation></citation_list>
</doi_citations>
</body>
</doi_batch>
