These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
22. A Deep-Learning-Driven Light-Weight Phishing Detection Sensor. Wei B; Hamad RA; Yang L; He X; Wang H; Gao B; Woo WL Sensors (Basel); 2019 Sep; 19(19):. PubMed ID: 31575038 [TBL] [Abstract][Full Text] [Related]
23. A Honeybee-Inspired Framework for a Smart City Free of Internet Scams. Ahmed AA; Al-Bayatti A; Saif M; Jabbar WA; Rassem TH Sensors (Basel); 2023 Apr; 23(9):. PubMed ID: 37177488 [TBL] [Abstract][Full Text] [Related]
24. An effective detection approach for phishing websites using URL and HTML features. Aljofey A; Jiang Q; Rasool A; Chen H; Liu W; Qu Q; Wang Y Sci Rep; 2022 May; 12(1):8842. PubMed ID: 35614133 [TBL] [Abstract][Full Text] [Related]
25. Applications of deep learning for phishing detection: a systematic literature review. Catal C; Giray G; Tekinerdogan B; Kumar S; Shukla S Knowl Inf Syst; 2022; 64(6):1457-1500. PubMed ID: 35645443 [TBL] [Abstract][Full Text] [Related]
26. Identifying and Mitigating Phishing Attack Threats in IoT Use Cases Using a Threat Modelling Approach. Abbas SG; Vaccari I; Hussain F; Zahid S; Fayyaz UU; Shah GA; Bakhshi T; Cambiaso E Sensors (Basel); 2021 Jul; 21(14):. PubMed ID: 34300556 [TBL] [Abstract][Full Text] [Related]
27. BoostedEnML: Efficient Technique for Detecting Cyberattacks in IoT Systems Using Boosted Ensemble Machine Learning. Okey OD; Maidin SS; Adasme P; Lopes Rosa R; Saadi M; Carrillo Melgarejo D; Zegarra RodrÃguez D Sensors (Basel); 2022 Sep; 22(19):. PubMed ID: 36236506 [TBL] [Abstract][Full Text] [Related]
28. Thwarting Instant Messaging Phishing Attacks: The Role of Self-Efficacy and the Mediating Effect of Attitude towards Online Sharing of Personal Information. Lee YY; Gan CL; Liew TW Int J Environ Res Public Health; 2023 Feb; 20(4):. PubMed ID: 36834209 [TBL] [Abstract][Full Text] [Related]
29. Prediction of Skin Disease Using Ensemble Data Mining Techniques and Feature Selection Method-a Comparative Study. Verma AK; Pal S; Kumar S Appl Biochem Biotechnol; 2020 Feb; 190(2):341-359. PubMed ID: 31350666 [TBL] [Abstract][Full Text] [Related]
30. Personalized persuasion: Quantifying susceptibility to information exploitation in spear-phishing attacks. Xu T; Singh K; Rajivan P Appl Ergon; 2023 Apr; 108():103908. PubMed ID: 36403509 [TBL] [Abstract][Full Text] [Related]
31. Piracema: a Phishing snapshot database for building dataset features. Gomes de Barros JC; Revoredo da Silva CM; Candeia Teixeira L; Torres Fernandes BJ; Lorenzato de Oliveira JF; Luzeiro Feitosa E; Pinheiro Dos Santos W; Ferraz Arcoverde H; Cardoso Garcia V Sci Rep; 2022 Sep; 12(1):15149. PubMed ID: 36071135 [TBL] [Abstract][Full Text] [Related]
32. Predicting User Susceptibility to Phishing Based on Multidimensional Features. Yang R; Zheng K; Wu B; Li D; Wang Z; Wang X Comput Intell Neurosci; 2022; 2022():7058972. PubMed ID: 35082844 [TBL] [Abstract][Full Text] [Related]
33. HDEC: A Heterogeneous Dynamic Ensemble Classifier for Binary Datasets. Ostvar N; Eftekhari Moghadam AM Comput Intell Neurosci; 2020; 2020():8826914. PubMed ID: 33488690 [TBL] [Abstract][Full Text] [Related]
34. An Assessment of Lexical, Network, and Content-Based Features for Detecting Malicious URLs Using Machine Learning and Deep Learning Models. Aljabri M; Alhaidari F; Mohammad RMA; Samiha Mirza ; Alhamed DH; Altamimi HS; Chrouf SMB Comput Intell Neurosci; 2022; 2022():3241216. PubMed ID: 36059391 [TBL] [Abstract][Full Text] [Related]
35. Is Domain Highlighting Actually Helpful in Identifying Phishing Web Pages? Xiong A; Proctor RW; Yang W; Li N Hum Factors; 2017 Jun; 59(4):640-660. PubMed ID: 28060529 [TBL] [Abstract][Full Text] [Related]
36. IntelliHealth: A medical decision support application using a novel weighted multi-layer classifier ensemble framework. Bashir S; Qamar U; Khan FH J Biomed Inform; 2016 Feb; 59():185-200. PubMed ID: 26703093 [TBL] [Abstract][Full Text] [Related]
37. Machine learning prediction of nanoparticle in vitro toxicity: A comparative study of classifiers and ensemble-classifiers using the Copeland Index. Furxhi I; Murphy F; Mullins M; Poland CA Toxicol Lett; 2019 Sep; 312():157-166. PubMed ID: 31102714 [TBL] [Abstract][Full Text] [Related]
38. A probabilistic classifier ensemble weighting scheme based on cross-validated accuracy estimates. Large J; Lines J; Bagnall A Data Min Knowl Discov; 2019; 33(6):1674-1709. PubMed ID: 31632184 [TBL] [Abstract][Full Text] [Related]
39. A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy. S K S; P A J Med Syst; 2017 Nov; 41(12):201. PubMed ID: 29124453 [TBL] [Abstract][Full Text] [Related]
40. Statistical Models for Predicting Threat Detection From Human Behavior. Kelley T; Amon MJ; Bertenthal BI Front Psychol; 2018; 9():466. PubMed ID: 29713296 [TBL] [Abstract][Full Text] [Related] [Previous] [Next] [New Search]