{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T18:28:27Z","timestamp":1771957707277,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T00:00:00Z","timestamp":1725926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Satya Wacana Christian University, Salatiga, Indonesia"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>In detecting Distributed Denial of Service (DDoS), deep learning faces challenges and difficulties such as high computational demands, long training times, and complex model interpretation. This research focuses on overcoming these challenges by proposing an effective strategy for detecting DDoS attacks in imbalanced network environments. This research employed DBSCAN and SMOTE to increase the class distribution of the dataset by allowing models using LSTM to learn time anomalies effectively when DDoS attacks occur. The experiments carried out revealed significant improvement in the performance of the LSTM model when integrated with DBSCAN and SMOTE. These include validation loss results of 0.048 for LSTM DBSCAN and SMOTE and 0.1943 for LSTM without DBSCAN and SMOTE, with accuracy of 99.50 and 97.50. Apart from that, there was an increase in the F1 score from 93.4% to 98.3%. This research proved that DBSCAN and SMOTE can be used as an effective strategy to improve model performance in detecting DDoS attacks on heterogeneous networks, as well as increasing model robustness and reliability.<\/jats:p>","DOI":"10.3390\/bdcc8090118","type":"journal-article","created":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T03:16:51Z","timestamp":1725938211000},"page":"118","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["DBSCAN SMOTE LSTM: Effective Strategies for Distributed Denial of Service Detection in Imbalanced Network Environments"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0318-2451","authenticated-orcid":false,"given":"Rissal","family":"Efendi","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50711, Indonesia"}]},{"given":"Teguh","family":"Wahyono","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50711, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0888-5793","authenticated-orcid":false,"given":"Indrastanti Ratna","family":"Widiasari","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50711, Indonesia"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sambangi, S., and Gondi, L. (2020, January 8\u20139). A Machine Learning Approach for DDoS (Distributed Denial of Service) Attack Detection Using Multiple Linear Regression. Proceedings of the 14th International Conference on Interdisciplinarity in Engineering\u2014INTER-ENG, Mures, Romania.","DOI":"10.3390\/proceedings2020063051"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Shieh, C.-S., Lin, W.-W., Nguyen, T.-T., Chen, C.-H., Horng, M.-F., and Miu, D. (2021). Detection of Unknown DDoS Attacks with Deep Learning and Gaussian Mixture Model. Appl. Sci., 11.","DOI":"10.3390\/app11115213"},{"key":"ref_3","first-page":"1317","article-title":"DDoS Attack Detection via Multi-Scale Convolutional Neural Network","volume":"62","author":"Cheng","year":"2020","journal-title":"Comput. Mater. Contin."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"114520","DOI":"10.1016\/j.eswa.2020.114520","article-title":"Detection of DDoS attacks with feed forward based deep neural network model","volume":"169","author":"Cil","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.1007\/s11042-021-11298-w","article-title":"An efficient deep learning technique for facial emotion recognition","volume":"81","author":"Khattak","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Khattak, A., Khan, A., Ullah, H., Asghar, M.U., Arif, A., Kundi, F.M., and Asghar, M.Z. (2022). An Efficient Supervised Machine Learning Technique for Forecasting Stock Market Trends. Information and Knowledge in Internet of Things, Springer.","DOI":"10.1007\/978-3-030-75123-4_7"},{"key":"ref_7","first-page":"1093","article-title":"Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content","volume":"63","author":"Asghar","year":"2020","journal-title":"Comput. Mater. Contin."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Khan, A., Khattak, A.M., Asghar, M.Z., Naeem, M., and Din, A.U. (2021). Playing First-Person Perspective Games with Deep Reinforcement Learning Using the State-of-the-Art Game-AI Research Platforms. Deep Learning for Unmanned Systems, Springer.","DOI":"10.1007\/978-3-030-77939-9_18"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"73865","DOI":"10.1109\/ACCESS.2020.2987842","article-title":"Classification of Poetry Text Into the Emotional States Using Deep Learning Technique","volume":"8","author":"Ahmad","year":"2020","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Alsaeedi, A., Bamasag, O., and Munshi, A. (2020, January 26\u201327). Real-Time DDoS flood Attack Monitoring and Detection (RT-AMD) Model for Cloud Computing. Proceedings of the 4th International Conference on Future Networks and Distributed Systems (ICFNDS), St. Petersburg, Russia.","DOI":"10.1145\/3440749.3442606"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s40537-019-0192-5","article-title":"Survey on deep learning with class imbalance","volume":"6","author":"Johnson","year":"2019","journal-title":"J. Big Data"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1145\/1147234.1147236","article-title":"Data mining for improved cardiac care","volume":"8","author":"Rao","year":"2006","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s11280-012-0178-0","article-title":"Effective detection of sophisticated online banking fraud on extremely imbalanced data","volume":"16","author":"Wei","year":"2013","journal-title":"World Wide Web"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s40537-018-0138-3","article-title":"Big Data fraud detection using multiple medicare data sources","volume":"5","author":"Herland","year":"2018","journal-title":"J. Big Data"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1023\/A:1007452223027","article-title":"Machine Learning for the Detection of Oil Spills in Satellite Radar Images","volume":"30","author":"Kubat","year":"1998","journal-title":"Mach. Learn."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s13755-018-0051-3","article-title":"The effects of varying class distribution on learner behavior for medicare fraud detection with imbalanced big data","volume":"6","author":"Bauder","year":"2018","journal-title":"Health Inf. Sci. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bauder, R.A., Khoshgoftaar, T.M., and Hasanin, T. (2018, January 17\u201320). An Empirical Study on Class Rarity in Big Data. Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA.","DOI":"10.1109\/ICMLA.2018.00125"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107393","DOI":"10.1016\/j.gexplo.2024.107393","article-title":"Intelligent mapping of geochemical anomalies: Adaptation of DBSCAN and mean-shift clustering approaches","volume":"258","author":"Hajihosseinlou","year":"2024","journal-title":"J. Geochem. Explor."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s13748-016-0094-0","article-title":"Learning from imbalanced data: Open challenges and future directions","volume":"5","author":"Krawczyk","year":"2016","journal-title":"Prog. Artif. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Pouyanfar, S., Tao, Y., Mohan, A., Tian, H., Kaseb, A.S., Gauen, K., Dailey, R., Aghajanzadeh, S., Lu, Y.-H., and Chen, S.-C. (2018, January 10\u201312). Dynamic Sampling in Convolutional Neural Networks for Imbalanced Data Classification. Proceedings of the 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), Miami, FL, USA.","DOI":"10.1109\/MIPR.2018.00027"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","article-title":"A systematic study of the class imbalance problem in convolutional neural networks","volume":"106","author":"Buda","year":"2018","journal-title":"Neural Netw."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6390","DOI":"10.1109\/TNNLS.2021.3136503","article-title":"DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data","volume":"34","author":"Dablain","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.neucom.2022.05.017","article-title":"PF-SMOTE: A novel parameter-free SMOTE for imbalanced datasets","volume":"498","author":"Chen","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"101614","DOI":"10.1016\/j.jocs.2022.101614","article-title":"Weighted Ensemble with one-class Classification and Over-sampling and Instance selection (WECOI): An approach for learning from imbalanced data streams","volume":"61","author":"Czarnowski","year":"2022","journal-title":"J. Comput. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"108217","DOI":"10.1016\/j.knosys.2022.108217","article-title":"Two density-based sampling approaches for imbalanced and overlapping data","volume":"241","author":"Mayabadi","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6473507","DOI":"10.1155\/2022\/6473507","article-title":"Intrusion Detection System for IoT Based on Deep Learning and Modified Reptile Search Algorithm","volume":"2022","author":"Dahou","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yang, L., Moubayed, A., Hamieh, I., and Shami, A. (2019, January 9\u201313). Tree-Based Intelligent Intrusion Detection System in Internet of Vehicles. Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa Village, HI, USA.","DOI":"10.1109\/GLOBECOM38437.2019.9013892"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1109\/JIOT.2021.3084796","article-title":"MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles","volume":"9","author":"Yang","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.procs.2021.05.025","article-title":"Network Intrusion Detection System using Deep Learning","volume":"185","author":"Ashiku","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"100053","DOI":"10.1016\/j.teler.2023.100053","article-title":"DCNNBiLSTM: An Efficient Hybrid Deep Learning-Based Intrusion Detection System","volume":"10","author":"Hnamte","year":"2023","journal-title":"Telemat. Inform. Rep."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Al-Mamory, S.O., and Algelal, Z.M. (2017, January 7\u20139). A modified DBSCAN clustering algorithm for proactive detection of DDoS attacks. Proceedings of the 2017 Annual Conference on New Trends in Information & Communications Technology Applications (NTICT), Baghdad, Iraq.","DOI":"10.1109\/NTICT.2017.7976107"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Girma, A., Garuba, M., and Goel, R. (2018). Advanced Machine Language Approach to Detect DDoS Attack Using DBSCAN Clustering Technology with Entropy. Information Technology\u2014New Generations, Springer.","DOI":"10.1007\/978-3-319-54978-1_17"},{"key":"ref_34","unstructured":"Latha, R., and Thangaraj, S.J.J. (2023, January 18\u201319). Machine Learning Approaches for DDoS Attack Detection: Naive Bayes vs Logistic Regression. Proceedings of the 2023 Second International Conference on Smart Technologies for Smart Nation (SmartTechCon), Singapore."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"124597","DOI":"10.1109\/ACCESS.2023.3328951","article-title":"Enhancing the Efficiency of Gaussian Na\u00efve Bayes Machine Learning Classifier in the Detection of DDOS in Cloud Computing","volume":"11","author":"Naiem","year":"2023","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wabi, A.A., Idris, I., Olaniyi, O.M., Joseph, A., and Adebayo, O.S. (2023). Modeling DDOS attacks in sdn and detection using random forest classifier. J. Cyber Secur. Technol., 1\u201314.","DOI":"10.1080\/23742917.2023.2264435"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ma, R., Wang, Q., Bu, X., and Chen, X. (2023). Real-Time Detection of DDoS Attacks Based on Random Forest in SDN. Appl. Sci., 13.","DOI":"10.3390\/app13137872"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Arunkumar, R., Navanitha, S., Padmavathi, B., and Snekaa, V. (2024, January 15\u201316). Hybrid SVM Approach for Enhanced DDoS Attack Detection Using Machine Learning in Cloud Environment. Proceedings of the 2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), Namakkal, India.","DOI":"10.1109\/AIMLA59606.2024.10531330"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1007\/s12083-023-01460-6","article-title":"An efficient DDoS attack detection and categorization using adolescent identity search-based weighted SVM model","volume":"16","author":"Barona","year":"2023","journal-title":"Peer-to-Peer Netw. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Rizvi, F., Sharma, R., Sharma, N., Rakhra, M., Aledaily, A.N., Viriyasitavat, W., Yadav, K., Dhiman, G., and Kaur, A. (2024). An evolutionary KNN model for DDoS assault detection using genetic algorithm based optimization. Multimed. Tools Appl.","DOI":"10.1007\/s11042-024-18744-5"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.comnet.2004.08.014","article-title":"Real-time detection of distributed denial-of-service attacks using RBF networks and statistical features","volume":"48","author":"Gavrilis","year":"2005","journal-title":"Comput. Netw."},{"key":"ref_42","first-page":"457","article-title":"Mohammad, Anomaly Network Intrusion Detection System based on Distributed Time-Delay Neural Network (DTDNN)","volume":"5","author":"Ibrahim","year":"2010","journal-title":"J. Eng. Sci. Technol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"53015","DOI":"10.1109\/ACCESS.2022.3172304","article-title":"Cyber Threats Detection in Smart Environments Using SDN-Enabled DNN-LSTM Hybrid Framework","volume":"10","author":"Javeed","year":"2022","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Meti, N., Narayan, D.G., and Baligar, V.P. (2017, January 13\u201316). Detection of distributed denial of service attacks using machine learning algorithms in software defined networks. Proceedings of the 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India.","DOI":"10.1109\/ICACCI.2017.8126031"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"8491","DOI":"10.1109\/JIOT.2022.3196942","article-title":"An Efficient Hybrid-DNN for DDoS Detection and Classification in Software-Defined IIoT Networks","volume":"10","author":"Zainudin","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tuan, N.N., Hung, P.H., Nghia, N.D., Van Tho, N., Van Phan, T., and Thanh, N.H. (2020). A DDoS Attack Mitigation Scheme in ISP Networks Using Machine Learning Based on SDN. Electronics, 9.","DOI":"10.3390\/electronics9030413"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Alghazzawi, D., Bamasag, O., Ullah, H., and Asghar, M.Z. (2021). Efficient Detection of DDoS Attacks Using a Hybrid Deep Learning Model with Improved Feature Selection. Appl. Sci., 11.","DOI":"10.3390\/app112411634"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Saini, P.S., Behal, S., and Bhatia, S. (2020, January 12\u201314). Detection of DDoS Attacks using Machine Learning Algorithms. Proceedings of the 2020 7th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India.","DOI":"10.23919\/INDIACom49435.2020.9083716"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"132502","DOI":"10.1109\/ACCESS.2020.3009733","article-title":"An Evolutionary SVM Model for DDOS Attack Detection in Software Defined Networks","volume":"8","author":"Sahoo","year":"2020","journal-title":"IEEE Access"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Polat, H., Polat, O., and Cetin, A. (2020). Detecting DDoS Attacks in Software-Defined Networks through Feature Selection Methods and Machine Learning Models. Sustainability, 12.","DOI":"10.3390\/su12031035"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Becerra-Suarez, F.L., Fern\u00e1ndez-Roman, I., and Forero, M.G. (2024). Improvement of Distributed Denial of Service Attack Detection through Machine Learning and Data Processing. Mathematics, 12.","DOI":"10.3390\/math12091294"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Alahmadi, A.A., Aljabri, M., Alhaidari, F., Alharthi, D.J., Rayani, G.E., Marghalani, L.A., Alotaibi, O.B., and Bajandouh, S.A. (2023). DDoS Attack Detection in IoT-Based Networks Using Machine Learning Models: A Survey and Research Directions. Electronics, 12.","DOI":"10.3390\/electronics12143103"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Mohammed, B.H., SAllehudin, H., Safie, N., Satar, M., Murhg, H.D., and Mohamed, S.A. (2023). Anomaly Detection of Distribted Denial of Service (DDoS) in IoT Network Using Machine Learning. Res. Sq.","DOI":"10.21203\/rs.3.rs-3496063\/v1"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1049\/cps2.12013","article-title":"Network intrusion detection using machine learning approaches: Addressing data imbalance","volume":"7","author":"Ahsan","year":"2021","journal-title":"IET Cyber-Phys. Syst. Theory Appl."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/8\/9\/118\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:52:33Z","timestamp":1760111553000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/8\/9\/118"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,10]]},"references-count":54,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["bdcc8090118"],"URL":"https:\/\/doi.org\/10.3390\/bdcc8090118","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,10]]}}}