Detection of Real-Time Distributed Denial-of-Service (DDoS) Attacks on Internet of Things (IoT) Networks Using Machine Learning Algorithms
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Abstract
The primary concern of modern technology is cyber attacks targeting the Internet of Things. As it is one of the most widely used networks today and vulnerable to attacks. Real-time threats pose with modern cyber attacks that ... | Find, read and cite all the research you need on Tech Science Press
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Research context
OpenAlex topics: Network Security and Intrusion Detection
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References (43)
Show source-provided references
1. Multi-objective optimization algorithms for intrusion detection in IoT networks: A systematic review10.1016/j.iotcps.2024.01.003 ↗
2. Measuring individuals cybersecurity awareness based on demographic features
3. Threat analysis and distributed denial of service (DDoS) attack recognition in the internet of things (IoT)10.3390/electronics11030494 ↗
4. DDoS family: A novel perspective for massive types of DDoS attacks10.1016/j.cose.2023.103663 ↗
5. IDS-EFS: Ensemble feature selection-based method for intrusion detection system10.1007/s11042-023-15977-8 ↗
6. A hybrid intrusion detection system based on scalable K-Means+ random forest and deep learning10.1109/ACCESS.2021.3082147 ↗
7. An explainable machine learning framework for intrusion detection systems10.1109/ACCESS.2020.2988359 ↗
8. Stability analysis of networked control systems under DoS attacks and security controller design with mini-batch machine learning supervision10.1109/TIFS.2023.3347889 ↗
9. Data mining and information retrieval in the 21st century: A bibliographic review10.1016/j.cosrev.2019.100193 ↗
10. IoT-defender: A convolutional approach to detect DDoS attacks in internet of things
11. Enhancing network security in IoT applications through DDoS attack detection using ML10.58491/2735-4202.3181 ↗
12. An efficient DDoS attack detection mechanism in SDN environment
13. Real-time detection of DDoS attacks based on random forest in SDN10.3390/app13137872 ↗
14. A long short-term memory based approach for detecting cyber attacks in IoT using CIC-IoT2023 dataset10.55056/jec.648 ↗
15. Drift adaptive online DDoS attack detection framework for IoT system10.3390/electronics13061004 ↗
16. Evaluating deep learning variants for cyber-attacks detection and multi-class classification in IoT networks10.7717/peerj-cs.1793 ↗
17. Internet of Things intrusion detection systems: A comprehensive review and future directions10.1007/s10586-022-03776-z ↗
18. Deep learning approach for intelligent intrusion detection system10.1109/ACCESS.2019.2895334 ↗
19. A deep learning technique for intrusion detection system using a recurrent neural networks based framework10.1016/j.comcom.2022.12.010 ↗
20. Performance analysis and design of intelligent optimising integral-based event-trigger control for autonomous ground vehicles under DoS attacks10.1109/TIV.2023.3317238 ↗
21. Comparison of network security tools-firewall, intrusion detection system and honeypot
22. 4G network security algorithms: Overview10.3991/ijim.v15i16.24175 ↗
23. Classification and prediction of significant cyber incidents (SCI) using data mining and machine learning (DM-ML)10.1109/ACCESS.2023.3249663 ↗
24. Survey on intrusion detection system using data mining techniques
25. Intrusion detection systems, issues, challenges, and needs10.2991/ijcis.d.210105.001 ↗
26. A survey on swarm intelligence approaches to feature selection in data mining10.1016/j.swevo.2020.100663 ↗
27. A novel multi-biometric technique for verification of secure e-document
28. Benchmark for filter methods for feature selection in high-dimensional classification data10.1016/j.csda.2019.106839 ↗
29. Optimize network intrusion detection system based on PCA feature extraction and three naïve bayes classifiers
30. Feature selection on magelang duck egg candling image using variance threshold method
31. Empowering smart city IoT network intrusion detection with advanced ensemble learning-based feature selection10.37391/IJEER.120206 ↗
32. Effects of distance measure choice on k-nearest neighbor classifier performance: A review10.1089/big.2018.0175 ↗
33. V. Prasatha et al., “Effects of distance measure choice on KNN classifier performance-a review,” arXiv preprint arXiv: 1708.04321, 2017.
34. Logistic regression trust-a trust model for internet-of-things using regression analysis10.32604/csse.2023.024292 ↗
35. Logistic regression and artificial neural network classification models: A methodology review10.1016/S1532-0464(03)00034-0 ↗
36. An overview: Stochastic gradient descent classifier, linear discriminant analysis, deep learning and Naive Bayes classifier approaches to network intrusion detection
37. D. Kalimeris et al., “SGD on neural networks learns functions of increasing complexity,” arXiv preprint arXiv: 1905.11604, 2019.
38. A stacking ensemble model of various machine learning models for daily runoff forecasting10.3390/w15071265 ↗
39. Effect of data scaling methods on machine learning algorithms and model performance10.3390/technologies9030052 ↗
40. An algorithm to compute composition skyline based on standard deviation10.1063/1.5116469 ↗
41. Two-step data clustering for improved intrusion detection system using CICIoT2023 dataset
42. Detection of distributed denial of service attacks using automatic feature selection with enhancement for imbalance dataset
43. D. M. Powers, “Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation,” arXiv preprint arXiv:2020.16061, 2010.