520 related articles for article (PubMed ID: 35746191)
21. 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]
22. A New Intrusion Detection System for the Internet of Things via Deep Convolutional Neural Network and Feature Engineering.
Ullah S; Ahmad J; Khan MA; Alkhammash EH; Hadjouni M; Ghadi YY; Saeed F; Pitropakis N
Sensors (Basel); 2022 May; 22(10):. PubMed ID: 35632016
[TBL] [Abstract][Full Text] [Related]
23. GLD-Net: Deep Learning to Detect DDoS Attack via Topological and Traffic Feature Fusion.
Guo W; Qiu H; Liu Z; Zhu J; Wang Q
Comput Intell Neurosci; 2022; 2022():4611331. PubMed ID: 36017461
[TBL] [Abstract][Full Text] [Related]
24. Attentive transformer deep learning algorithm for intrusion detection on IoT systems using automatic Xplainable feature selection.
Zegarra Rodríguez D; Daniel Okey O; Maidin SS; Umoren Udo E; Kleinschmidt JH
PLoS One; 2023; 18(10):e0286652. PubMed ID: 37844095
[TBL] [Abstract][Full Text] [Related]
25. Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection.
Ren Z; Li X; Peng J; Chen K; Tan Q; Wu X; Shi C
Sci Rep; 2024 Jan; 14(1):1247. PubMed ID: 38218745
[TBL] [Abstract][Full Text] [Related]
26. Composition of Hybrid Deep Learning Model and Feature Optimization for Intrusion Detection System.
Henry A; Gautam S; Khanna S; Rabie K; Shongwe T; Bhattacharya P; Sharma B; Chowdhury S
Sensors (Basel); 2023 Jan; 23(2):. PubMed ID: 36679684
[TBL] [Abstract][Full Text] [Related]
27. OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection.
Abbasi S; Famouri M; Shafiee MJ; Wong A
Sensors (Basel); 2021 Jul; 21(14):. PubMed ID: 34300545
[TBL] [Abstract][Full Text] [Related]
28. A Continuous Learning Approach for Real-Time Network Intrusion Detection.
Martina MR; Foresti GL
Int J Neural Syst; 2021 Dec; 31(12):2150060. PubMed ID: 34779358
[TBL] [Abstract][Full Text] [Related]
29. SEHIDS: Self Evolving Host-Based Intrusion Detection System for IoT Networks.
Baz M
Sensors (Basel); 2022 Aug; 22(17):. PubMed ID: 36080962
[TBL] [Abstract][Full Text] [Related]
30. A Deep Learning Ensemble for Network Anomaly and Cyber-Attack Detection.
Dutta V; Choraś M; Pawlicki M; Kozik R
Sensors (Basel); 2020 Aug; 20(16):. PubMed ID: 32824187
[TBL] [Abstract][Full Text] [Related]
31. Deep Neural Decision Forest (DNDF): A Novel Approach for Enhancing Intrusion Detection Systems in Network Traffic Analysis.
Alrayes FS; Zakariah M; Driss M; Boulila W
Sensors (Basel); 2023 Oct; 23(20):. PubMed ID: 37896456
[TBL] [Abstract][Full Text] [Related]
32. Data Randomization and Cluster-Based Partitioning for Botnet Intrusion Detection.
Al-Jarrah OY; Alhussein O; Yoo PD; Muhaidat S; Taha K; Kim K
IEEE Trans Cybern; 2016 Aug; 46(8):1796-806. PubMed ID: 26540724
[TBL] [Abstract][Full Text] [Related]
33. Multi-Layered Filtration Framework for Efficient Detection of Network Attacks Using Machine Learning.
Paracha MA; Sadiq M; Liang J; Durad MH; Sheeraz M
Sensors (Basel); 2023 Jun; 23(13):. PubMed ID: 37447678
[TBL] [Abstract][Full Text] [Related]
34. 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]
35. Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks.
Fatani A; Dahou A; Abd Elaziz M; Al-Qaness MAA; Lu S; Alfadhli SA; Alresheedi SS
Sensors (Basel); 2023 Apr; 23(9):. PubMed ID: 37177634
[TBL] [Abstract][Full Text] [Related]
36. Adversarial attacks against supervised machine learning based network intrusion detection systems.
Alshahrani E; Alghazzawi D; Alotaibi R; Rabie O
PLoS One; 2022; 17(10):e0275971. PubMed ID: 36240162
[TBL] [Abstract][Full Text] [Related]
37. Anomaly Detection in Industrial IoT Using Distributional Reinforcement Learning and Generative Adversarial Networks.
Benaddi H; Jouhari M; Ibrahimi K; Ben Othman J; Amhoud EM
Sensors (Basel); 2022 Oct; 22(21):. PubMed ID: 36365782
[TBL] [Abstract][Full Text] [Related]
38. Classification of Normal and Malicious Traffic Based on an Ensemble of Machine Learning for a Vehicle CAN-Network.
Alalwany E; Mahgoub I
Sensors (Basel); 2022 Nov; 22(23):. PubMed ID: 36501896
[TBL] [Abstract][Full Text] [Related]
39. A Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection.
Al-Turaiki I; Altwaijry N
Big Data; 2021 Jun; 9(3):233-252. PubMed ID: 34138657
[TBL] [Abstract][Full Text] [Related]
40. Malicious Network Traffic Detection Based on Deep Neural Networks and Association Analysis.
Gao M; Ma L; Liu H; Zhang Z; Ning Z; Xu J
Sensors (Basel); 2020 Mar; 20(5):. PubMed ID: 32155834
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]