{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T06:40:29Z","timestamp":1772779229386,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,3,21]],"date-time":"2021-03-21T00:00:00Z","timestamp":1616284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCP"],"abstract":"<jats:p>Machine learning algorithms are becoming very efficient in intrusion detection systems with their real time response and adaptive learning process. A robust machine learning model can be deployed for anomaly detection by using a comprehensive dataset with multiple attack types. Nowadays datasets contain many attributes. Such high dimensionality of datasets poses a significant challenge to information extraction in terms of time and space complexity. Moreover, having so many attributes may be a hindrance towards creation of a decision boundary due to noise in the dataset. Large scale data with redundant or insignificant features increases the computational time and often decreases goodness of fit which is a critical issue in cybersecurity. In this research, we have proposed and implemented an efficient feature selection algorithm to filter insignificant variables. Our proposed Dynamic Feature Selector (DFS) uses statistical analysis and feature importance tests to reduce model complexity and improve prediction accuracy. To evaluate DFS, we conducted experiments on two datasets used for cybersecurity research namely Network Security Laboratory (NSL-KDD) and University of New South Wales (UNSW-NB15). In the meta-learning stage, four algorithms were compared namely Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Units, Random Forest and a proposed Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) for accuracy estimation. For NSL-KDD, experiments revealed an increment in accuracy from 99.54% to 99.64% while reducing feature size of one-hot encoded features from 123 to 50. In UNSW-NB15 we observed an increase in accuracy from 90.98% to 92.46% while reducing feature size from 196 to 47. The proposed approach is thus able to achieve higher accuracy while significantly lowering number of features required for processing.<\/jats:p>","DOI":"10.3390\/jcp1010011","type":"journal-article","created":{"date-parts":[[2021,3,21]],"date-time":"2021-03-21T22:00:37Z","timestamp":1616364037000},"page":"199-218","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":90,"title":["Enhancing Machine Learning Prediction in Cybersecurity Using Dynamic Feature Selector"],"prefix":"10.3390","volume":"1","author":[{"given":"Mostofa","family":"Ahsan","sequence":"first","affiliation":[{"name":"Department of Computer Science, North Dakota State University, Fargo, ND 58102, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5377-8196","authenticated-orcid":false,"given":"Rahul","family":"Gomes","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0145-6838","authenticated-orcid":false,"given":"Md. Minhaz","family":"Chowdhury","sequence":"additional","affiliation":[{"name":"Department of Computer Science, East Stroudsburg University of Pennsylvania, East Stroudsburg, PA 18301, USA"}]},{"given":"Kendall E.","family":"Nygard","sequence":"additional","affiliation":[{"name":"Department of Computer Science, North Dakota State University, Fargo, ND 58102, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,21]]},"reference":[{"key":"ref_1","unstructured":"Chowdhury, M., and Nygard, K. (2018, January 19\u201321). Machine Learning within a Con Resistant Trust Model. Proceedings of the The 33rd International Conference on Computers and their Applications (CATA 2018), Las Vegas, NV, USA."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/S0004-3702(97)00063-5","article-title":"Selection of relevant features and examples in machine learning","volume":"97","author":"Blum","year":"1997","journal-title":"Artif. Intell."},{"key":"ref_3","unstructured":"Yang, Y., and Pedersen, J.O. (1997, January 8\u201312). A comparative study on feature selection in text categorization. Proceedings of the ICML, Nashville, TN, USA."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_5","unstructured":"Hu, H., Li, J., Plank, A., Wang, H., and Daggard, G. (2006, January 29\u201330). A comparative study of classification methods for microarray data analysis. Proceedings of the 5th Australasian Data Mining Conference (AusDM 2006): Data Mining and Analytics 2006, Sydney, NSW, Australia."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Niranjan, A., Prakash, A., Veena, N., Geetha, M., Shenoy, P.D., and Venugopal, K. (2017, January 18\u201319). EBJRV: An Ensemble of Bagging, J48 and Random Committee by Voting for Efficient Classification of Intrusions. Proceedings of the 2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), Dehradun, India.","DOI":"10.1109\/WIECON-ECE.2017.8468876"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Camargo, C.O., Faria, E.R., Zarpel\u00e3o, B.B., and Miani, R.S. (2018, January 4\u20138). Qualitative evaluation of denial of service datasets. Proceedings of the XIV Brazilian Symposium on Information Systems, Caxias do Sul, Brazil.","DOI":"10.1145\/3229345.3229394"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bachl, M., Hartl, A., Fabini, J., and Zseby, T. (2019, January 9). Walling up Backdoors in Intrusion Detection Systems. Proceedings of the 3rd ACM CoNEXT Workshop on Big Data, Machine Learning and Artificial Intelligence for Data Communication Networks, Orlando, FL, USA.","DOI":"10.1145\/3359992.3366638"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, H., Liu, Z., Liu, Y., and Gao, X. (2019, January 15\u201317). Abnormal Network Traffic Detection based on Leaf Node Density Ratio. Proceedings of the 2019\u20149th International Conference on Communication and Network Security, Chongqing, China.","DOI":"10.1145\/3371676.3371678"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Faker, O., and Dogdu, E. (2019, January 18\u201320). Intrusion detection using big data and deep learning techniques. Proceedings of the 2019 ACM Southeast Conference, Kennesaw, GA, USA.","DOI":"10.1145\/3299815.3314439"},{"key":"ref_11","unstructured":"Thejas, G., Jimenez, D., Iyengar, S.S., Miller, J., Sunitha, N., and Badrinath, P. (2020, January 2\u20134). COMB: A Hybrid Method for Cross-validated Feature Selection. Proceedings of the ACM Southeast Regional Conference, Tampa, FL, USA."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ding, Y., and Zhai, Y. (2018, January 8\u201310). Intrusion detection system for NSL-KDD dataset using convolutional neural networks. Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence, Shenzhen, China.","DOI":"10.1145\/3297156.3297230"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.3233\/IDA-173624","article-title":"A hybrid filter-wrapper feature selection method for DDoS detection in cloud computing","volume":"22","author":"Belouch","year":"2018","journal-title":"Intell. Data Anal."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.cose.2017.06.005","article-title":"A GA-LR wrapper approach for feature selection in network intrusion detection","volume":"70","author":"Khammassi","year":"2017","journal-title":"Comput. Secur."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tun, M.T., Nyaung, D.E., and Phyu, M.P. (2020, January 1\u20133). Network Anomaly Detection using Threshold-based Sparse. Proceedings of the 11th International Conference on Advances in Information Technology, Bangkok, Thailand.","DOI":"10.1145\/3406601.3406626"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Viet, H.N., Van, Q.N., Trang, L.L.T., and Nathan, S. (2018, January 25\u201327). Using deep learning model for network scanning detection. Proceedings of the 4th International Conference on Frontiers of Educational Technologies, Moscow, Russia.","DOI":"10.1145\/3233347.3233379"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Primartha, R., and Tama, B.A. (2017, January 1\u20132). Anomaly detection using random forest: A performance revisited. Proceedings of the 2017 International Conference on Data and Software Engineering (ICoDSE), Palembang, Indonesia.","DOI":"10.1109\/ICODSE.2017.8285847"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Belouch, M., and Hadaj, S.E. (2017, January 22\u201323). Comparison of ensemble learning methods applied to network intrusion detection. Proceedings of the Second International Conference on Internet of things, Data and Cloud Computing, Cambridge, UK.","DOI":"10.1145\/3018896.3065830"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, J., Kantarci, B., and Adams, C. (2020, January 13). Machine learning-driven intrusion detection for Contiki-NG-based IoT networks exposed to NSL-KDD dataset. Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning, Linz, Austria.","DOI":"10.1145\/3395352.3402621"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tran, B., Xue, B., and Zhang, M. (2017). Class dependent multiple feature construction using genetic programming for high-dimensional data. Australasian Joint Conference on Artificial Intelligence, Proceedings of the AI 2017: AI 2017: Advances in Artificial Intelligence, Melbourne, VIC, Australia, 19\u201320 August 2017, Springer.","DOI":"10.1007\/978-3-319-63004-5_15"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Krishna, G.J., and Ravi, V. (2019, January 3\u20135). Feature subset selection using adaptive differential evolution: An application to banking. Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, Kolkata, India.","DOI":"10.1145\/3297001.3297021"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1109\/TNN.2008.2000395","article-title":"A general wrapper approach to selection of class-dependent features","volume":"19","author":"Wang","year":"2008","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tran, B., Zhang, M., and Xue, B. (2016, January 6\u20139). Multiple feature construction in classification on high-dimensional data using GP. Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Athens, Greece.","DOI":"10.1109\/SSCI.2016.7850130"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hariharakrishnan, J., Mohanavalli, S., and Kumar, K.S. (2017, January 10\u201311). Survey of pre-processing techniques for mining big data. Proceedings of the 2017 International Conference on Computer, Communication and Signal Processing (ICCCSP), Chennai, India.","DOI":"10.1109\/ICCCSP.2017.7944072"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Enache, A.C., Sgarciu, V., and Petrescu-Ni\u0163\u0103, A. (2015, January 21\u201323). Intelligent feature selection method rooted in Binary Bat Algorithm for intrusion detection. Proceedings of the 2015 IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics, Timisoara, Romania.","DOI":"10.1109\/SACI.2015.7208259"},{"key":"ref_27","first-page":"80","article-title":"Cyber intrusion detection by combined feature selection algorithm","volume":"44","author":"Mohammadi","year":"2019","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_28","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep Learning, MIT Press."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ahsan, M., Gomes, R., and Denton, A. (2019, January 20\u201322). Application of a Convolutional Neural Network using transfer learning for tuberculosis detection. Proceedings of the 2019 IEEE International Conference on Electro Information Technology (EIT), Brookings, SD, USA.","DOI":"10.1109\/EIT.2019.8833768"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/TKDE.2005.66","article-title":"Toward integrating feature selection algorithms for classification and clustering","volume":"17","author":"Liu","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_31","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"Guyon","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"22","DOI":"10.4097\/kjae.2017.70.1.22","article-title":"Understanding one-way ANOVA using conceptual figures","volume":"70","author":"Kim","year":"2017","journal-title":"Korean J. Anesthesiol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Benesty, J., Chen, J., Huang, Y., and Cohen, I. (2009). Pearson correlation coefficient. Noise Reduction in Speech Processing, Springer.","DOI":"10.1007\/978-3-642-00296-0_5"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1109\/TASL.2008.919072","article-title":"On the importance of the Pearson correlation coefficient in noise reduction","volume":"16","author":"Benesty","year":"2008","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_36","unstructured":"Chen, T., He, T., Benesty, M., Khotilovich, V., and Tang, Y. (2020, July 15). Xgboost: Extreme gradient boosting. R Package Version 0.4-2, Available online: https:\/\/mran.microsoft.com\/web\/packages\/xgboost\/vignettes\/xgboost.pdf."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Gomes, R., Denton, A., and Franzen, D. (2019). Quantifying Efficiency of Sliding-Window Based Aggregation Technique by Using Predictive Modeling on Landform Attributes Derived from DEM and NDVI. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8040196"},{"key":"ref_38","unstructured":"Bennett, K.P. (1992). Decision Tree Construction via Linear Programming, University of Wisconsin-Madison Department of Computer Sciences. Technical Report."},{"key":"ref_39","unstructured":"Harris, E. (2002, January 2\u20134). Information Gain Versus Gain Ratio: A Study of Split Method Biases. Proceedings of the ISAIM, Fort Lauderdale, FL, USA."},{"key":"ref_40","unstructured":"Hall, M.A., and Smith, L.A. (1999, January 1\u20135). Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper. Proceedings of the FLAIRS Conference, Orlando, FL, USA."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Gomes, R., Ahsan, M., and Denton, A. (2018, January 3\u20135). Random forest classifier in SDN framework for user-based indoor localization. Proceedings of the 2018 IEEE International Conference on Electro\/Information Technology (EIT), Rochester, MI, USA.","DOI":"10.1109\/EIT.2018.8500111"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1109\/TSMCB.2002.804363","article-title":"Orthogonal forward selection and backward elimination algorithms for feature subset selection","volume":"34","author":"Mao","year":"2004","journal-title":"IEEE Trans. Syst. Man Cybern. Part B (Cybern.)"},{"key":"ref_44","unstructured":"(2020, July 15). NSL-KDD Dataset. Available online: https:\/\/www.unb.ca\/cic\/datasets\/nsl.html."},{"key":"ref_45","unstructured":"(2020, July 15). KDD Cup 1999 Data. Available online: http:\/\/kdd.ics.uci.edu\/databases\/kddcup99\/."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., and Ghorbani, A.A. (2009, January 8\u201310). A detailed analysis of the KDD CUP 99 data set. Proceedings of the 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, Ottawa, ON, Canada.","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"ref_47","unstructured":"Seger, C. (2020, November 10). An Investigation of Categorical Variable Encoding Techniques in Machine Learning: Binary Versus One-Hot and Feature Hashing. Available online: https:\/\/www.diva-portal.org\/smash\/record.jsf?pid=diva2%3A1259073&dswid=-2157\/."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1007\/s10994-018-5724-2","article-title":"Similarity encoding for learning with dirty categorical variables","volume":"107","author":"Cerda","year":"2018","journal-title":"Mach. Learn."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Choong, A.C.H., and Lee, N.K. (2017, January 9\u201311). Evaluation of convolutionary neural networks modeling of DNA sequences using ordinal versus one-hot encoding method. Proceedings of the 2017 International Conference on Computer and Drone Applications (IConDA), Kuching, Malaysia.","DOI":"10.1109\/ICONDA.2017.8270400"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"280","DOI":"10.4236\/jbise.2016.95021","article-title":"DNA sequence classification by convolutional neural network","volume":"9","author":"Nguyen","year":"2016","journal-title":"J. Biomed. Sci. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Cohen, J., Cohen, P., West, S.G., and Aiken, L.S. (2013). Applied Multiple Regression\/Correlation Analysis for the Behavioral Sciences, Lawrence Erlbaum Associates Publishers.","DOI":"10.4324\/9781410606266"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"29575","DOI":"10.1109\/ACCESS.2020.2972627","article-title":"BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset","volume":"8","author":"Su","year":"2020","journal-title":"IEEE Access"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Moustafa, N., and Slay, J. (2015, January 10\u201312). UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). Proceedings of the 2015 military communications and information systems conference (MilCIS), Canberra, ACT, Australia.","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"ref_54","unstructured":"Ahsan, M., and Nygard, K.E. (2020, January 23\u201325). Convolutional Neural Networks with LSTM for Intrusion Detection. Proceedings of the CATA, San Francisco, CA, USA."},{"key":"ref_55","unstructured":"Nichol, A., Achiam, J., and Schulman, J. (2018). On first-order meta-learning algorithms. arXiv."},{"key":"ref_56","unstructured":"Finn, C., Abbeel, P., and Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of deep networks. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/s10462-013-9406-y","article-title":"Metalearning: A survey of trends and technologies","volume":"44","author":"Lemke","year":"2015","journal-title":"Artif. Intell. Rev."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1925","DOI":"10.1016\/j.patcog.2014.12.003","article-title":"META-DES: A dynamic ensemble selection framework using meta-learning","volume":"48","author":"Cruz","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1016\/j.neucom.2009.06.015","article-title":"Meta-learning for imbalanced data and classification ensemble in binary classification","volume":"73","author":"Lin","year":"2009","journal-title":"Neurocomputing"},{"key":"ref_60","unstructured":"Dvornik, N., Schmid, C., and Mairal, J. (2, January 27). Diversity with cooperation: Ensemble methods for few-shot classification. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Fu, R., Zhang, Z., and Li, L. (2016, January 11\u201313). Using LSTM and GRU neural network methods for traffic flow prediction. Proceedings of the 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), Wuhan, China.","DOI":"10.1109\/YAC.2016.7804912"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Dey, R., and Salemt, F.M. (2017, January 6\u20139). Gate-variants of gated recurrent unit (GRU) neural networks. Proceedings of the 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS), Boston, MA, USA.","DOI":"10.1109\/MWSCAS.2017.8053243"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.jsr.2005.06.013","article-title":"Data mining of tree-based models to analyze freeway accident frequency","volume":"36","author":"Chang","year":"2005","journal-title":"J. Saf. Res."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1007\/BF01054343","article-title":"Tree-based models for random distribution of mass","volume":"73","author":"Aldous","year":"1993","journal-title":"J. Stat. Phys."},{"key":"ref_65","unstructured":"Yang, Y., Morillo, I.G., and Hospedales, T.M. (2018). Deep neural decision trees. arXiv."},{"key":"ref_66","unstructured":"Zhang, J., and Man, K. (1998, January 14). Time series prediction using RNN in multi-dimension embedding phase space. Proceedings of the 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No. 98CH36218), San Diego, CA, USA."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Zhang, L., and Xiang, F. (2018). Relation classification via BiLSTM-CNN. International Conference on Data Mining and Big Data, Proceedings of the DMBD 2018: Data Mining and Big Data, Shanghai, China, 17\u201322 June 2018, Springer.","DOI":"10.1007\/978-3-319-93803-5_35"},{"key":"ref_68","unstructured":"Sharfuddin, A.A., Tihami, M.N., and Islam, M.S. (2018, January 21\u201322). A deep recurrent neural network with bilstm model for sentiment classification. Proceedings of the 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), Sylhet, Bangladesh."},{"key":"ref_69","unstructured":"Powers, D.M. (2011). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv."}],"container-title":["Journal of Cybersecurity and Privacy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2624-800X\/1\/1\/11\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:38:51Z","timestamp":1760161131000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2624-800X\/1\/1\/11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,21]]},"references-count":69,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["jcp1010011"],"URL":"https:\/\/doi.org\/10.3390\/jcp1010011","relation":{},"ISSN":["2624-800X"],"issn-type":[{"value":"2624-800X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,21]]}}}