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Работы, на которые ссылается эта работа
Работ: 72
Работа: Lightweight Intrusion Detection Systems for IoT–Edge Environments: A PRISMA-ScR Systematic Review of Deployability Evidence and a Unified Assessment Framework
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
Matthew J. Page, Joanne E. McKenzie, Patrick M. Bossuyt +23
Статья2021Цитирований: 55ABIA Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks
Chuanlong Yin, Yuefei Zhu, Jinlong Fei +1
Статья2017Цитирований: 9ABIThe Measurement of Observer Agreement for Categorical Data
J. Richard Landis, Gary G. Koch
Статья1977Цитирований: 7ABIPRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation
Andrea C. Tricco, Erin Lillie, Wasifa Zarin +25
Статья2018Цитирований: 7ABIA Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Статья2015Цитирований: 5ABIA detailed analysis of the KDD CUP 99 data set
Mahbod Tavallaee, Ebrahim Bagheri, Wei Lu +1
Статья2009Цитирований: 3ABINetwork Intrusion Detection for IoT Security Based on Learning Techniques
Nadia Chaabouni, Mohamed Mosbah, Akka Zemmari +2
Статья2019Цитирований: 3ABIA Survey on Mobile Edge Computing: The Communication Perspective
Yuyi Mao, Changsheng You, Jun Zhang +2
Статья2017Цитирований: 3ABIDeep Learning Approach for Intelligent Intrusion Detection System
R. Vinayakumar, Mamoun Alazab, K. P. Soman +3
Статья2019Цитирований: 2ABICICIoT2023: A real-time dataset and benchmark for large-scale attacks in IoT environment
Euclides Carlos Pinto Neto, Sajjad Dadkhah, Raphael Ferreira +3
Препринт2023Цитирований: 2ABIA New Ensemble-Based Intrusion Detection System for Internet of Things
Adeel Abbas, Muazzam A. Khan, Shahid Latif +3
Статья2021Цитирований: 2ABIFederated-Learning-Based Anomaly Detection for IoT Security Attacks
Viraaji Mothukuri, Prachi Khare, Reza M. Parizi +3
Статья2021Цитирований: 2ABI