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.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

206 related articles for article (PubMed ID: 33110340)

  • 1. A comprehensive survey of AI-enabled phishing attacks detection techniques.
    Basit A; Zafar M; Liu X; Javed AR; Jalil Z; Kifayat K
    Telecommun Syst; 2021; 76(1):139-154. PubMed ID: 33110340
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A Hybrid Approach for Alluring Ads Phishing Attack Detection Using Machine Learning.
    Shaukat MW; Amin R; Muslam MMA; Alshehri AH; Xie J
    Sensors (Basel); 2023 Sep; 23(19):. PubMed ID: 37836902
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Cloud-based email phishing attack using machine and deep learning algorithm.
    Butt UA; Amin R; Aldabbas H; Mohan S; Alouffi B; Ahmadian A
    Complex Intell Systems; 2023; 9(3):3043-3070. PubMed ID: 35668732
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Datasets for phishing websites detection.
    Vrbančič G; Fister I; Podgorelec V
    Data Brief; 2020 Dec; 33():106438. PubMed ID: 33195768
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Detecting phishing websites using machine learning technique.
    Dutta AK
    PLoS One; 2021; 16(10):e0258361. PubMed ID: 34634081
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Improving the phishing website detection using empirical analysis of Function Tree and its variants.
    Balogun AO; Adewole KS; Raheem MO; Akande ON; Usman-Hamza FE; Mabayoje MA; Akintola AG; Asaju-Gbolagade AW; Jimoh MK; Jimoh RG; Adeyemo VE
    Heliyon; 2021 Jul; 7(7):e07437. PubMed ID: 34278030
    [TBL] [Abstract][Full Text] [Related]  

  • 7. An intelligent cyber security phishing detection system using deep learning techniques.
    Mughaid A; AlZu'bi S; Hnaif A; Taamneh S; Alnajjar A; Elsoud EA
    Cluster Comput; 2022; 25(6):3819-3828. PubMed ID: 35602317
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A hybrid DNN-LSTM model for detecting phishing URLs.
    Ozcan A; Catal C; Donmez E; Senturk B
    Neural Comput Appl; 2023; 35(7):4957-4973. PubMed ID: 34393380
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Binary Hunter-Prey Optimization with Machine Learning-Based Cybersecurity Solution on Internet of Things Environment.
    Khadidos AO; AlKubaisy ZM; Khadidos AO; Alyoubi KH; Alshareef AM; Ragab M
    Sensors (Basel); 2023 Aug; 23(16):. PubMed ID: 37631743
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A Deep Learning-Based Innovative Technique for Phishing Detection in Modern Security with Uniform Resource Locators.
    Aldakheel EA; Zakariah M; Gashgari GA; Almarshad FA; Alzahrani AIA
    Sensors (Basel); 2023 Apr; 23(9):. PubMed ID: 37177607
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Comparative evaluation of machine learning algorithms for phishing site detection.
    Almujahid NF; Haq MA; Alshehri M
    PeerJ Comput Sci; 2024; 10():e2131. PubMed ID: 38983211
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. 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]  

  • 15. 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]  

  • 16. 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]  

  • 17. How Good Are We at Detecting a Phishing Attack? Investigating the Evolving Phishing Attack Email and Why It Continues to Successfully Deceive Society.
    Carroll F; Adejobi JA; Montasari R
    SN Comput Sci; 2022; 3(2):170. PubMed ID: 35224514
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Susceptibility to Spear-Phishing Emails: Effects of Internet User Demographics and Email Content.
    Lin T; Capecci DE; Ellis DM; Rocha HA; Dommaraju S; Oliveira DS; Ebner NC
    ACM Trans Comput Hum Interact; 2019 Sep; 26(5):. PubMed ID: 32508486
    [TBL] [Abstract][Full Text] [Related]  

  • 19. APuML: An Efficient Approach to Detect Mobile Phishing Webpages using Machine Learning.
    Jain AK; Debnath N; Jain AK
    Wirel Pers Commun; 2022; 125(4):3227-3248. PubMed ID: 35529800
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Phishing URLs Detection Using Sequential and Parallel ML Techniques: Comparative Analysis.
    Nagy N; Aljabri M; Shaahid A; Ahmed AA; Alnasser F; Almakramy L; Alhadab M; Alfaddagh S
    Sensors (Basel); 2023 Mar; 23(7):. PubMed ID: 37050527
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.