Aslam et al., 2024 - Google Patents
AntiPhishStack: LSTM-Based Stacked Generalization Model for Optimized Phishing URL DetectionAslam et al., 2024
View PDF- Document ID
- 15778553871708355679
- Author
- Aslam S
- Aslam H
- Manzoor A
- Chen H
- Rasool A
- Publication year
- Publication venue
- Symmetry
External Links
Snippet
The escalating reliance on revolutionary online web services has introduced heightened security risks, with persistent challenges posed by phishing despite extensive security measures. Traditional phishing systems, reliant on machine learning and manual features …
- 238000001514 detection method 0 title abstract description 58
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Thakur et al. | An intelligent algorithmically generated domain detection system | |
Wang et al. | PDRCNN: Precise phishing detection with recurrent convolutional neural networks | |
Jain et al. | Towards detection of phishing websites on client-side using machine learning based approach | |
Li et al. | A stacking model using URL and HTML features for phishing webpage detection | |
Aljofey et al. | An effective detection approach for phishing websites using URL and HTML features | |
Afzal et al. | Urldeepdetect: A deep learning approach for detecting malicious urls using semantic vector models | |
Liang et al. | Anomaly-based web attack detection: a deep learning approach | |
Moghimi et al. | New rule-based phishing detection method | |
Lansley et al. | SEADer++: social engineering attack detection in online environments using machine learning | |
Indrasiri et al. | Robust ensemble machine learning model for filtering phishing URLs: Expandable random gradient stacked voting classifier (ERG-SVC) | |
Mohan et al. | Spoof net: syntactic patterns for identification of ominous online factors | |
Liu et al. | An efficient multistage phishing website detection model based on the CASE feature framework: Aiming at the real web environment | |
Opara et al. | Look before you leap: Detecting phishing web pages by exploiting raw URL and HTML characteristics | |
US20230126692A1 (en) | System and method for blocking phishing attempts in computer networks | |
Hoang et al. | An improved model for detecting DGA botnets using random forest algorithm | |
Yuan et al. | A novel approach for malicious URL detection based on the joint model | |
Bustio-Martínez et al. | A lightweight data representation for phishing URLs detection in IoT environments | |
Aslam et al. | AntiPhishStack: LSTM-Based Stacked Generalization Model for Optimized Phishing URL Detection | |
Rasheed et al. | Adversarial attacks on featureless deep learning malicious urls detection | |
Asiri et al. | PhishingRTDS: A real-time detection system for phishing attacks using a Deep Learning model | |
Abdulrahman et al. | Phishing attack detection based on random forest with wrapper feature selection method | |
Dangwal et al. | Feature selection for machine learning-based phishing websites detection | |
Li et al. | A novel threat intelligence information extraction system combining multiple models | |
Rashid et al. | Phishing URL detection generalisation using Unsupervised Domain Adaptation | |
Deng et al. | Feature optimization and hybrid classification for malicious web page detection |