Phishing URL Detection using Bi-LSTM
Phishing attacks threaten online users, often leading to data breaches, financial losses, and identity theft. Traditional phishing detection systems struggle with high false positive rates and are usually limited by the types of attacks they can identify. This paper proposes a deep learning-based approach using a Bidirectional Long Short-Term Memory (Bi-LSTM) network to classify URLs into four categories: benign, phishing, defacement, and malware. The model leverages sequential URL data and captures contextual information, improving the accuracy of phishing detection. Experimental results on a dataset comprising over 650,000 URLs demonstrate the model's effectiveness, achieving 97% accuracy and significant improvements over traditional techniques.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Federated Phish Bowl: LSTM-Based Decentralized Phishing Email Detection
With increasingly more sophisticated phishing campaigns in recent years, phishing emails lure people using more legitimate-looking personal contexts. To tackle this problem, instead of traditional heuristics-based algori…
Federated LearningPrivacy PreservingThe Performance of Sequential Deep Learning Models in Detecting Phishing Websites Using Contextual Features of URLs
Cyber attacks continue to pose significant threats to individuals and organizations, stealing sensitive data such as personally identifiable information, financial information, and login credentials. Hence, detecting mal…
Deep LearningAntiPhishStack: LSTM-based Stacked Generalization Model for Optimized Phishing URL Detection
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, relia…
Detecting Cloud-Based Phishing Attacks by Combining Deep Learning Models
Web-based phishing attacks nowadays exploit popular cloud web hosting services and apps such as Google Sites and Typeform for hosting their attacks. Since these attacks originate from reputable domains and IP addresses o…
Deep LearningTripletA Deep Learning Model with Hierarchical LSTMs and Supervised Attention for Anti-Phishing
Anti-phishing aims to detect phishing content/documents in a pool of textual data. This is an important problem in cybersecurity that can help to guard users from fraudulent information. Natural language processing (NLP)…
SentenceText Categorization