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Cited by in F6Publishing
For: Xu H, Przystupa K, Fang C, Marciniak A, Kochan O, Beshley M. A Combination Strategy of Feature Selection Based on an Integrated Optimization Algorithm and Weighted K-Nearest Neighbor to Improve the Performance of Network Intrusion Detection. Electronics 2020;9:1206. [DOI: 10.3390/electronics9081206] [Cited by in Crossref: 9] [Cited by in F6Publishing: 12] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
1 Karimi Z, Torabi Z. An Adaptive k-nearest neighbor Classifier using Differential Evolution with Auto-Enhanced Population Diversity for Intrusion Detection.. [DOI: 10.21203/rs.3.rs-2250216/v1] [Reference Citation Analysis]
2 Hussain A, Alam S, Ghauri SA, Ali M, Sherazi HR, Akhunzada A, Bibi I, Gani A. Automatic Modulation Recognition Based on the Optimized Linear Combination of Higher-Order Cumulants. Sensors (Basel) 2022;22:7488. [PMID: 36236583 DOI: 10.3390/s22197488] [Reference Citation Analysis]
3 Yong O, Dou W, Rong G, Zhiwei Y, Kochan O. Single-Phase Fault Detection Based on GCN-TCN Sparse-Attention Model. 2022 12th International Conference on Advanced Computer Information Technologies (ACIT) 2022. [DOI: 10.1109/acit54803.2022.9913084] [Reference Citation Analysis]
4 Bharati S, Podder P, Xiong J. Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions. Security and Communication Networks 2022;2022:1-41. [DOI: 10.1155/2022/8951961] [Reference Citation Analysis]
5 Zhou C, Petryshyn H, Liubytskyi R, Kochan O. Optimization of On-Street Parking in the Historical Heritage Part of Lviv (Ukraine) as a Prerequisite for Designing the IoT Smart Parking System. Buildings 2022;12:865. [DOI: 10.3390/buildings12060865] [Reference Citation Analysis]
6 Rehman E, Haseeb-ud-din M, Malik AJ, Khan TK, Abbasi AA, Kadry S, Khan MA, Rho S. Intrusion detection based on machine learning in the internet of things, attacks and counter measures. J Supercomput. [DOI: 10.1007/s11227-021-04188-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
7 Zhang J, Shi Z, Wu H, Xing M. A Novel Self-supervised Few-shot Network Intrusion Detection Method. Wireless Algorithms, Systems, and Applications 2022. [DOI: 10.1007/978-3-031-19208-1_42] [Reference Citation Analysis]
8 Nti IK, Nyarko-boateng O, Adekoya AF, Arjun R. Network Intrusion Detection with StackNet: A phi coefficient Based Weak Learner Selection Approach. 2021 22nd International Arab Conference on Information Technology (ACIT) 2021. [DOI: 10.1109/acit53391.2021.9677338] [Reference Citation Analysis]
9 Liu W, Li P, Ye Z, Yang S. A Node Deployment Optimization Method of Wireless Sensor Network Based on Firefly Algorithm. 2021 IEEE 4th International Conference on Advanced Information and Communication Technologies (AICT) 2021. [DOI: 10.1109/aict52120.2021.9628937] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
10 Khan MA. HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System. Processes 2021;9:834. [DOI: 10.3390/pr9050834] [Cited by in Crossref: 36] [Cited by in F6Publishing: 37] [Article Influence: 18.0] [Reference Citation Analysis]
11 Przystupa K, Pyrih J, Beshley M, Klymash M, Branytskyy A, Beshley H, Pieniak D, Gauda K. Improving the Efficiency of Information Flow Routing in Wireless Self-Organizing Networks Based on Natural Computing. Energies 2021;14:2255. [DOI: 10.3390/en14082255] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
12 Fang M, Chen Z, Przystupa K, Li T, Majka M, Kochan O. Examination of Abnormal Behavior Detection Based on Improved YOLOv3. Electronics 2021;10:197. [DOI: 10.3390/electronics10020197] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]