Phishing website detection: How effective are deep <scp>learning‐based</scp> models and hyperparameter optimization?

Almousa, May; Zhang, Tianyang; Sarrafzadeh, Abdolhossein; Anwar, Mohd

doi:10.1002/spy2.256

Cited by 17 publications

(5 citation statements)

References 27 publications

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“…The model is deployed with a simple UI, making it accessible for use in web browsers. Almousa et al [24], proposed a structured approach for constructing phishing detection models, relying on three distinct deep learning algorithm architectures: Long Short-Term Memory (LSTM)based models, Fully Connected Deep Neural Network (FC-DNN)-based models, and convolutional neural network (CNN)-based models. They conducted evaluations on four publicly accessible phishing website datasets and optimized hyperparameters to ensure adaptability across various datasets.…”

Section: Deep Learningmentioning

confidence: 99%

A Permutaion Importance Based feature selection method and Deep Learning Model to Detect Phishing Websites

Zaimi,

Hafidi,

Mahnane

2024

Preprint

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Phishing attacks pose a significant and escalating threat to cybersecurity in recent times. This deceptive scam aims to trick naive users, luring them into visiting harmful websites and sharing sensitive information, including credentials, credit card numbers, and passwords. Consequently, cybercriminals exploit this data for their own gain. As the sophistication and maliciousness of phishing continue to evolve, researchers are earnestly developing multiple anti-phishing solutions in the literature. Among these solutions, those based on deep learning models have gained substantial attention in recent years. This study proposes an intelligent, deep-learning-based mechanism to detect phishing URLs. The proposed system is based on the permutation importance method (PIM) to select the most relevant URL features, and the Smote-Tomek link method to solve the problem of an unbalanced dataset. In addition, four DL models—CNN, LSTM, and two hybrid models (CNN-LSTM and LSTM-CNN)—are tested to identify the more suitable detection model for the phishing field. The experimental results demonstrate the successful functioning of the proposed phishing detection mechanism. It is observed that the proposed mechanism achieved an accuracy ranging from 93.36% to 96.43% without feature selection and data balance across two variants of datasets and different DL classifiers. It also achieved an accuracy ranging from 94.12% to 96.88% with feature selection and data balance. Finally, our phishing detection mechanism is implemented as web application to enhance its usability for web users.

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Section: Deep Learningmentioning

confidence: 99%

A Permutaion Importance Based feature selection method and Deep Learning Model to Detect Phishing Websites

Zaimi,

Hafidi,

Mahnane

2024

Preprint

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“…In their experiments using real IP flows from Internet Service Providers (ISPs), the detection model based on Deep Belief Networks (DBN) algorithm achieved a true positive rate of 90%. In [6], researchers conducted a systematic study of the effectiveness of deep learning algorithm architectures for phishing website detection. They utilized three types of website features (URLbased, content-based, and hybrid (URL and content together).…”

Section: A Phishing Detectionmentioning

confidence: 99%

A URL-Based Social Semantic Attacks Detection With Character-Aware Language Model

Almousa

Anwar

2023

IEEE Access

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Social engineering attacks rely on human errors and behavioral choices. The semantic attack, a subcategory of social engineering attacks, utilizes behavioral or cosmetic deception vectors (e.g., attacker creates a malicious website that looks like and behaves like the legitimate one). The most common types of social semantic attacks include phishing, spamming, defacement, and malware attacks. We investigate the feasibility of developing URL-based social semantic attack detection models utilizing character-aware language models. Specifically, we developed three types of models: long short-term memory (LSTM)-based detection model, convolutional neural network (CNN)-based detection model, and CharacterBERT-based detection model. We benchmarked performances of different models for different attacks. Using the characterBERT-based detection model, the overall evaluation recorded a high detection accuracy of 99.65% by averaging the results of performing a 5-fold cross-validation. Considering the model performance per class, the CharacterBERT model ranked the best model among our 3 models in detecting the social semantic attacks, reaching best accuracy of 99.90% in detecting defacement attack.

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“…Content Analysis: which involves a meticulous examination and scrutiny of various elements within websites, including text, images, and links, has the potential to detect malicious intentions associated with a website. As a result, cybersecurity management's technical approaches must incorporate automated detection mechanisms aimed at thwarting phishing attacks [9]. The primary purpose of content analysis is to identify and analyze potentially suspicious elements that may indicate the presence of phishing attacks.…”

Section: Phishing Website Detectionmentioning

confidence: 99%

Phishing Website Detection Using Machine Learning: A Review

Al saedi,

Abbas Flayh

2023

wjps

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Phishing, a form of cyber attack in which perpetrators employ fraudulent websites or emails to Deceive individuals into divulging sensitive information such as passwords or financial data, can be mitigated through various machine-learning algorithms for website detection. These algorithms, including decision trees, support vector machines, and Random Forest, analyze multiple website features, such as URL structure, website content, and the presence of specific keywords or patterns, to ascertain the likelihood of a website being a phishing site. This comprehensive review elucidates the concept of phishing website detection and the diverse techniques employed while summarizing previous studies, their outcomes, and their contributions. Overall, machine learning algorithms serve as a potent tool in the identification of phishing websites, thereby safeguarding users against falling prey to such malicious attacks.

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Phishing website detection: How effective are deep learning‐based models and hyperparameter optimization?

Cited by 17 publications

References 27 publications

A Permutaion Importance Based feature selection method and Deep Learning Model to Detect Phishing Websites

A Permutaion Importance Based feature selection method and Deep Learning Model to Detect Phishing Websites

A URL-Based Social Semantic Attacks Detection With Character-Aware Language Model

Phishing Website Detection Using Machine Learning: A Review

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