154 related articles for article (PubMed ID: 38172185)
41. Realguard: A Lightweight Network Intrusion Detection System for IoT Gateways.
Nguyen XH; Nguyen XD; Huynh HH; Le KH
Sensors (Basel); 2022 Jan; 22(2):. PubMed ID: 35062393
[TBL] [Abstract][Full Text] [Related]
42. An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets.
Larriva-Novo X; Villagrá VA; Vega-Barbas M; Rivera D; Sanz Rodrigo M
Sensors (Basel); 2021 Jan; 21(2):. PubMed ID: 33477875
[TBL] [Abstract][Full Text] [Related]
43. Logistic Regression Ensemble Classifier for Intrusion Detection System in Internet of Things.
Chalichalamala S; Govindan N; Kasarapu R
Sensors (Basel); 2023 Dec; 23(23):. PubMed ID: 38067959
[TBL] [Abstract][Full Text] [Related]
44. Ensemble technique of intrusion detection for IoT-edge platform.
Aldaej A; Ullah I; Ahanger TA; Atiquzzaman M
Sci Rep; 2024 May; 14(1):11703. PubMed ID: 38778085
[TBL] [Abstract][Full Text] [Related]
45. Top-Down Machine Learning-Based Architecture for Cyberattacks Identification and Classification in IoT Communication Networks.
Abu Al-Haija Q
Front Big Data; 2021; 4():782902. PubMed ID: 35098112
[TBL] [Abstract][Full Text] [Related]
46. A lightweight intrusion detection method for IoT based on deep learning and dynamic quantization.
Wang Z; Chen H; Yang S; Luo X; Li D; Wang J
PeerJ Comput Sci; 2023; 9():e1569. PubMed ID: 37810346
[TBL] [Abstract][Full Text] [Related]
47. The proposed hybrid deep learning intrusion prediction IoT (HDLIP-IoT) framework.
Fadel MM; El-Ghamrawy SM; Ali-Eldin AMT; Hassan MK; El-Desoky AI
PLoS One; 2022; 17(7):e0271436. PubMed ID: 35905101
[TBL] [Abstract][Full Text] [Related]
48. Towards Developing a Robust Intrusion Detection Model Using Hadoop-Spark and Data Augmentation for IoT Networks.
Manzano Sanchez RA; Zaman M; Goel N; Naik K; Joshi R
Sensors (Basel); 2022 Oct; 22(20):. PubMed ID: 36298077
[TBL] [Abstract][Full Text] [Related]
49. Detection of Middlebox-Based Attacks in Healthcare Internet of Things Using Multiple Machine Learning Models.
Al Abdulwahid A
Comput Intell Neurosci; 2022; 2022():2037954. PubMed ID: 36479020
[TBL] [Abstract][Full Text] [Related]
50. Examining the Suitability of NetFlow Features in Detecting IoT Network Intrusions.
Awad M; Fraihat S; Salameh K; Al Redhaei A
Sensors (Basel); 2022 Aug; 22(16):. PubMed ID: 36015924
[TBL] [Abstract][Full Text] [Related]
51. Anomaly Detection in a Smart Industrial Machinery Plant Using IoT and Machine Learning.
Jaramillo-Alcazar A; Govea J; Villegas-Ch W
Sensors (Basel); 2023 Oct; 23(19):. PubMed ID: 37837116
[TBL] [Abstract][Full Text] [Related]
52. Cyber and Physical Security Vulnerability Assessment for IoT-Based Smart Homes.
Ali B; Awad AI
Sensors (Basel); 2018 Mar; 18(3):. PubMed ID: 29518023
[TBL] [Abstract][Full Text] [Related]
53. Detection of Malicious Cloud Bandwidth Consumption in Cloud Computing Using Machine Learning Techniques.
Veeraiah D; Mohanty R; Kundu S; Dhabliya D; Tiwari M; Jamal SS; Halifa A
Comput Intell Neurosci; 2022; 2022():4003403. PubMed ID: 36105640
[TBL] [Abstract][Full Text] [Related]
54. CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment.
Neto ECP; Dadkhah S; Ferreira R; Zohourian A; Lu R; Ghorbani AA
Sensors (Basel); 2023 Jun; 23(13):. PubMed ID: 37447792
[TBL] [Abstract][Full Text] [Related]
55. Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System.
Fatani A; Dahou A; Al-Qaness MAA; Lu S; Abd Elaziz M
Sensors (Basel); 2021 Dec; 22(1):. PubMed ID: 35009682
[TBL] [Abstract][Full Text] [Related]
56. Efficient Approach for Anomaly Detection in IoT Using System Calls.
Shamim N; Asim M; Baker T; Awad AI
Sensors (Basel); 2023 Jan; 23(2):. PubMed ID: 36679447
[TBL] [Abstract][Full Text] [Related]
57. An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection.
Kareem SS; Mostafa RR; Hashim FA; El-Bakry HM
Sensors (Basel); 2022 Feb; 22(4):. PubMed ID: 35214297
[TBL] [Abstract][Full Text] [Related]
58. Towards an Effective Intrusion Detection Model Using Focal Loss Variational Autoencoder for Internet of Things (IoT).
Khanam S; Ahmedy I; Idris MYI; Jaward MH
Sensors (Basel); 2022 Aug; 22(15):. PubMed ID: 35957379
[TBL] [Abstract][Full Text] [Related]
59. Artificial Intelligence-Driven Intrusion Detection in Software-Defined Wireless Sensor Networks: Towards Secure IoT-Enabled Healthcare Systems.
Masengo Wa Umba S; Abu-Mahfouz AM; Ramotsoela D
Int J Environ Res Public Health; 2022 Apr; 19(9):. PubMed ID: 35564763
[TBL] [Abstract][Full Text] [Related]
60. A Hybrid Framework for Intrusion Detection in Healthcare Systems Using Deep Learning.
Akshay Kumaar M; Samiayya D; Vincent PMDR; Srinivasan K; Chang CY; Ganesh H
Front Public Health; 2021; 9():824898. PubMed ID: 35096763
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]