{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T11:02:55Z","timestamp":1784026975522,"version":"3.55.0"},"reference-count":65,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T00:00:00Z","timestamp":1676419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Distributed denial of service (DDoS) attacks pose an increasing threat to businesses and government agencies. They harm internet businesses, limit access to information and services, and damage corporate brands. Attackers use application layer DDoS attacks that are not easily detectable because of impersonating authentic users. In this study, we address novel application layer DDoS attacks by analyzing the characteristics of incoming packets, including the size of HTTP frame packets, the number of Internet Protocol (IP) addresses sent, constant mappings of ports, and the number of IP addresses using proxy IP. We analyzed client behavior in public attacks using standard datasets, the CTU-13 dataset, real weblogs (dataset) from our organization, and experimentally created datasets from DDoS attack tools: Slow Lairs, Hulk, Golden Eyes, and Xerex. A multilayer perceptron (MLP), a deep learning algorithm, is used to evaluate the effectiveness of metrics-based attack detection. Simulation results show that the proposed MLP classification algorithm has an efficiency of 98.99% in detecting DDoS attacks. The performance of our proposed technique provided the lowest value of false positives of 2.11% compared to conventional classifiers, i.e., Na\u00efve Bayes, Decision Stump, Logistic Model Tree, Na\u00efve Bayes Updateable, Na\u00efve Bayes Multinomial Text, AdaBoostM1, Attribute Selected Classifier, Iterative Classifier, and OneR.<\/jats:p>","DOI":"10.3390\/fi15020076","type":"journal-article","created":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T06:33:01Z","timestamp":1676442781000},"page":"76","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron"],"prefix":"10.3390","volume":"15","author":[{"given":"Sheeraz","family":"Ahmed","sequence":"first","affiliation":[{"name":"Department of Computer Science, Iqra National University, Peshawar 25000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7101-2979","authenticated-orcid":false,"given":"Zahoor Ali","family":"Khan","sequence":"additional","affiliation":[{"name":"Faculty of Computer Information Science, Higher Colleges of Technology, Fujairah 4114, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0886-9061","authenticated-orcid":false,"given":"Syed Muhammad","family":"Mohsin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad, Islamabad 45550, Pakistan"},{"name":"College of Intellectual Novitiates (COIN), Virtual University of Pakistan, Lahore 55150, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shahid","family":"Latif","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Iqra National University, Peshawar 25000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4305-0908","authenticated-orcid":false,"given":"Sheraz","family":"Aslam","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Computer Engineering, and Informatics, Cyprus University of Technology, Limassol 3036, Cyprus"},{"name":"Department of Computer Science, Ctl Eurocollege, 3077 Limassol, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9645-6827","authenticated-orcid":false,"given":"Hana","family":"Mujlid","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Taif University, Taif 11099, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Adil","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Iqra National University, Peshawar 25000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeeshan","family":"Najam","sequence":"additional","affiliation":[{"name":"CEO, Ultimate Engineering Consultants Private Limited, Peshawar 25000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.cose.2016.10.005","article-title":"Application layer HTTP-GET flood DDoS attacks: Research landscape and challenges","volume":"65","author":"Singh","year":"2017","journal-title":"Comput. Secur."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"99273","DOI":"10.1109\/ACCESS.2020.2995801","article-title":"A lightweight post-quantum lattice-based RSA for secure communications","volume":"8","author":"Mustafa","year":"2020","journal-title":"IEEE Access"},{"key":"ref_3","first-page":"383","article-title":"Characterization and Comparison of DDoS Attack Tools and Traffic Generators: A Review","volume":"19","author":"Behal","year":"2017","journal-title":"IJ Netw. Secur."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Jiang, M., Wang, C., Luo, X., Miu, M., and Chen, T. (2017, January 25\u201330). Characterizing the Impacts of Application Layer DDoS Attacks. Proceedings of the 2017 IEEE International Conference on Web Services (ICWS), Honolulu, HI, USA.","DOI":"10.1109\/ICWS.2017.58"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"410","DOI":"10.7763\/IJET.2017.V9.1008","article-title":"Detection and Defense Algorithms of Different Types of DDoS Attacks","volume":"9","author":"Yusof","year":"2017","journal-title":"Int. J. Eng. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yadav, S., and Subramanian, S. (2016, January 11\u201313). Detection of Application Layer DDoSattack by feature learning using Stacked AutoEncoder. Proceedings of the 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT), New Delhi, India.","DOI":"10.1109\/ICCTICT.2016.7514608"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Stefanidis, K., and Serpanos, D.N. (2005, January 5\u20137). Countermeasures Against Distributed Denial of Service Attacks. Proceedings of the 2005 IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, Sofia, Bulgaria.","DOI":"10.1109\/IDAACS.2005.283019"},{"key":"ref_8","first-page":"390","article-title":"Preventing DDoSAttack Using Data Mining Algorithms","volume":"6","author":"Bandara","year":"2016","journal-title":"Int. J. Sci. Res. Publ."},{"key":"ref_9","first-page":"474","article-title":"Rank Correlation for Low-Rate DDoS Attack Detection: An Empirical Evaluation","volume":"18","author":"Ain","year":"2016","journal-title":"IJ Netw. Secur."},{"key":"ref_10","first-page":"1917","article-title":"A System for Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis","volume":"3","author":"Devare","year":"2016","journal-title":"Int. Res. J. Eng. Technol. (IRJET)"},{"key":"ref_11","first-page":"1769","article-title":"Development of PCCNN-based network intrusion detection system for EDGE computing","volume":"71","author":"Haq","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_12","first-page":"1729","article-title":"DNNBoT: Deep neural network-based botnet detection and classification","volume":"71","author":"Haq","year":"2022","journal-title":"CMC-Comput. Mater. Contin."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"619","DOI":"10.32604\/iasc.2022.021430","article-title":"Insider Threat Detection Based on NLP Word Embedding and Machine Learning","volume":"33","author":"Haq","year":"2022","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1109\/COMPSAC.2017.207","article-title":"Secure Double-Layered Defense against HTTP-DDoS Attacks","volume":"Volume 2","author":"Eid","year":"2017","journal-title":"Proceedings of the 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)"},{"key":"ref_15","unstructured":"Gonzalez, J.D.T., and Kinsner, W. (2016, January 22\u201323). Zero-crossing analysis of L\u00e9vy walks for real-time feature extraction: Composite signal analysis for strengthening the IoT against DDoS attacks. Proceedings of the 2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC), Palo Alto, CA, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kavisankar, L., Chellappan, C., Venkatesan, S., and Sivasankar, P. (2017, January 3\u20134). Efficient SYN Spoofing Detection and Mitigation Scheme for DDoS Attack. Proceedings of the 2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM), Tindivanam, India.","DOI":"10.1109\/ICRTCCM.2017.55"},{"key":"ref_17","first-page":"145","article-title":"MLP-GA based algorithm to detect application layer DDoS attack","volume":"36","author":"Singh","year":"2017","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_18","unstructured":"Joosten, R., and Nieuwenhuis, L.J. (2017, January 6\u20138). Analysing the Impact of a DDoS Attack Announcement on Victim Stock Prices. Proceedings of the 2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), St. Petersburg, Russia."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ajagekar, S.K., and Jadhav, V. (2016, January 15\u201317). Study on web DDOS attacks detection using multinomial classifier. Proceedings of the 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Chennai, India.","DOI":"10.1109\/ICCIC.2016.7919656"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Akbar, S., and Wibawa, A.D. (2016, January 28\u201330). The impact analysis and mitigation of DDoS attack on local government electronic procurement service (LPSE). Proceedings of the 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA), Lombok, Indonesia.","DOI":"10.1109\/ISITIA.2016.7828694"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ali, S.T., Sultana, A., and Jangra, A. (2016, January 22\u201324). Mitigating DDoS attack using random integer factorization. Proceedings of the 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), Waknaghat, India.","DOI":"10.1109\/PDGC.2016.7913212"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Alparslan, O., Gunes, O., Hanay, Y.S., Arakawa, S.I., and Murata, M. (2017, January 12\u201314). Improving resiliency against DDoS attacks by SDN and multipath orchestration of VNF services. Proceedings of the 2017 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN), Osaka, Japan.","DOI":"10.1109\/LANMAN.2017.7972158"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bhatia, S. (2016, January 6\u20137). Ensemble-based model for DDoS attack detection and flash event separation. Proceedings of the 2016 Future Technologies Conference (FTC), San Francisco, CA, USA.","DOI":"10.1109\/FTC.2016.7821720"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, C., and Chen, H. (2016, January 15\u201317). A resource utilization measurement detection against DDoS attacks. Proceedings of the 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Datong, China.","DOI":"10.1109\/CISP-BMEI.2016.7853035"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.comnet.2017.03.018","article-title":"Ghorbani. Detecting HTTP-based application layer DoS attacks on web servers in the presence of sampling","volume":"121","author":"Jazi","year":"2017","journal-title":"Comput. Netw."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.aci.2017.10.003","article-title":"HTTP flood attack detection in application layer using machine learning metrics and bio inspired bat algorithm","volume":"15","author":"Sreeram","year":"2017","journal-title":"Appl. Comput. Inform."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Diovu, R.C., and Agee, J.T. (2017, January 27\u201330). A cloud-based openflow firewall for mitigation against DDoS attacks in smart grid AMI networks. Proceedings of the 2017 IEEE PES PowerAfrica, Accra, Ghana.","DOI":"10.1109\/PowerAfrica.2017.7991195"},{"key":"ref_28","first-page":"6036","article-title":"An efficient DDoS TCP flood attack detection and prevention system in a cloud environment","volume":"5","author":"Sahi","year":"2017","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kawamura, T., Fukushi, M., Hirano, Y., Fujita, Y., and Hamamoto, Y. (2017, January 12\u201314). An NTP-based detection module for DDoS attacks on IoT. Proceedings of the 2017 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), Taipei, Taiwan.","DOI":"10.1109\/ICCE-China.2017.7990972"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Machaka, P., Bagula, A., and Nelwamondo, F. (December, January 30). Using exponentially weighted moving average algorithm to defend against DDoS attacks. Proceedings of the 2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech), Stellenbosch, South Africa.","DOI":"10.1109\/RoboMech.2016.7813157"},{"key":"ref_31","unstructured":"Kumar, V., and Kumar, K. (October, January 30). Classification of DDoS attack tools and its handling techniques and strategy at application layer. Proceedings of the 2016 2nd International Conference on Advances in Computing, Communication, & Automation (ICACCA) (Fall), Bareilly, India."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Mahale, V.V., Pareek, N.P., and Uttarwar, V.U. (2017, January 21\u201323). Alleviation of DDoS attack using advance technique. Proceedings of the 2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Bengaluru, India.","DOI":"10.1109\/ICIMIA.2017.7975595"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.future.2013.08.002","article-title":"Detection and defense of application-layer DDoS attacks in backbone web traffic","volume":"38","author":"Zhou","year":"2014","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.jnca.2018.03.024","article-title":"D-FACE: An anomaly based distributed approach for early detection of DDoS attacks and flash events","volume":"111","author":"Behal","year":"2018","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_35","first-page":"9469","article-title":"Swarm intelligence based autonomous DDoS attack detection and defense using multi agent system","volume":"4","author":"Kesavamoorthy","year":"2018","journal-title":"Clust. Comput."},{"key":"ref_36","unstructured":"Thomas, A., Kumar, T.G., and Mohan, A.K. (2018). Advanced Computational and Communication Paradigms, Springer."},{"key":"ref_37","unstructured":"Prasad, K.M., Reddy, A.R.M., and Rao, K.V.G. (2018). Artificial Intelligence and Evolutionary Computations in Engineering Systems, Springer."},{"key":"ref_38","unstructured":"Rezaei, H., Farjamib, Y., and Yektae, M.H. (2018). A Novel Framework for DDoS Detectionin Huge Scale Networks, ThankstoQoS Features. arXiv."},{"key":"ref_39","first-page":"245","article-title":"Prevention and detection of DDoS attack on WSN","volume":"2","author":"Hassan","year":"2018","journal-title":"International Journal of Research Culture Society."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1007\/s13369-017-2844-0","article-title":"Distributed Denial-of-Service Attack Detection and Mitigation Using Feature Selection and Intensive Care Request Processing Unit","volume":"43","author":"Bharot","year":"2018","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1007\/s11768-018-8002-8","article-title":"Secure design for cloud control system against distributed denial of service attack","volume":"16","author":"Ali","year":"2018","journal-title":"Control Theory Technol."},{"key":"ref_42","first-page":"15","article-title":"Mitigating the Knock-on-Effect of DDoS Attacks on Application Layer using Deep Learning Multi-Layer Perception","volume":"11","author":"Shah","year":"2020","journal-title":"J. Inf. Commun. Technol. Robot. Appl."},{"key":"ref_43","unstructured":"Girma, A., Garuba, M., and Goel, R. (2018). Information Technology New Generations, Springer."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Koay, A., Chen, A., Welch, I., and Seah, W.K.G. (2018, January 10\u201312). A new multi classifier system using entropy-based features in DDoS attack detection. Proceedings of the 2018 International Conference on Information Networking (ICOIN), Chiang Mai, Thailand.","DOI":"10.1109\/ICOIN.2018.8343104"},{"key":"ref_45","unstructured":"Zare, H., Azadi, M., and Olsen, P. (2018). Information Technology-New Generations, Springer."},{"key":"ref_46","first-page":"29","article-title":"Fuzzy-based User Behavior Characterization to Detect HTTP-GET Flood Attacks","volume":"10","author":"Singh","year":"2018","journal-title":"Int. J. Intell. Syst. Appl."},{"key":"ref_47","first-page":"184","article-title":"Dynamic Ant Colony System with Three Level Update Feature Selection for Intrusion Detection","volume":"20","author":"Rais","year":"2018","journal-title":"Int. J. Netw. Secur."},{"key":"ref_48","first-page":"157","article-title":"Intrusion Detection based on a Novel Hybrid Learning Approach","volume":"6","author":"Keshtgary","year":"2018","journal-title":"J. AI Data Min."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.cose.2017.08.012","article-title":"Record route IP traceback: Combating DoS attacks and the variants","volume":"72","author":"Nur","year":"2018","journal-title":"Comput. Secur."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1109\/TIFS.2017.2758754","article-title":"SkyShield: A Sketch-Based Defense System Against Application Layer DDoS Attacks","volume":"13","author":"Wang","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Iffl\u00e4nder, L., Walter, J., Eismann, S., and Kounev, S. (2018, January 9\u201313). The Vision of Self-aware Reordering of Security Network Function Chains. Proceedings of the Companion of the 2018 ACM\/SPEC International Conference on Performance Engineering, Berlin, Germany.","DOI":"10.1145\/3185768.3186309"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"e2042","DOI":"10.1002\/nem.2042","article-title":"DDoS protection with stateful software-defined networking","volume":"29","author":"Rebecchi","year":"2019","journal-title":"Int. J. Netw. Manag."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1007\/s12083-017-0630-0","article-title":"Survey on SDN based network intrusion detection system using machine learning approaches","volume":"12","author":"Sultana","year":"2019","journal-title":"Peer-Peer Netw. Appl."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.knosys.2018.11.026","article-title":"SFAD: Toward effective anomaly detection based on session feature similarity","volume":"165","author":"Xiao","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"265","DOI":"10.3390\/make1010017","article-title":"Multi-Layer Hidden Markov Model Based Intrusion Detection System","volume":"1","author":"Zegeye","year":"2019","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.future.2019.02.037","article-title":"DDoS detection and defense mechanism based on cognitive-inspired computing in SDN","volume":"97","author":"Cui","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.ijcip.2019.02.003","article-title":"MPTCP-H: A DDoS attack resilient transport protocol to secure wide area measurement systems","volume":"25","author":"Demir","year":"2019","journal-title":"Int. J. Crit. Infrastruct. Prot."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.inffus.2019.01.002","article-title":"A Machine Learning Based Intrusion Detection Scheme for Data Fusion in Mobile Clouds Involving Heterogeneous Client Networks","volume":"49","author":"Dey","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_59","first-page":"436","article-title":"Clustering based Semi-Supervised Machine Learning for DDoS Attack Classification","volume":"33","author":"Aamir","year":"2019","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.jnca.2019.01.019","article-title":"The application of Software Defined Networking on securing computer networks: A survey","volume":"131","author":"Sahay","year":"2019","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.jnca.2019.02.026","article-title":"Intrusion detection in smart cities using Restricted Boltzmann Machines","volume":"135","author":"Elsaeidy","year":"2019","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.jnca.2019.02.030","article-title":"Traffic-flow analysis for source-side DDoS recognition on 5G environments","volume":"136","author":"Monge","year":"2019","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"8469410","DOI":"10.1155\/2019\/8469410","article-title":"ForChaos: Real Time Application DDoS Detection Using Forecasting and Chaos Theory in Smart Home IoT Network","volume":"2019","author":"Procopiou","year":"2019","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.inffus.2018.10.013","article-title":"WitoldPedrycz Network traffic fusion and analysisagainst DDoS flooding attacks with a novel reversible sketch","volume":"51","author":"Jing","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"110992","DOI":"10.1016\/j.rser.2021.110992","article-title":"A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids","volume":"144","author":"Aslam","year":"2021","journal-title":"Renew. Sustain. Energy Rev."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/15\/2\/76\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:36:37Z","timestamp":1760121397000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/15\/2\/76"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,15]]},"references-count":65,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["fi15020076"],"URL":"https:\/\/doi.org\/10.3390\/fi15020076","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,15]]}}}