Phishing Website Detection as a Website Comparing Problem

Le-Nguyen, Minh-Khoi; Nguyen, Tri-Chan-Hung; Le, Dinh-Thuan; Nguyen, Van-Hoa; Ton, Long-Phuoc; Nguyen-An, Khuong

doi:10.1007/s42979-022-01544-9

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…Phishing adalah masalah besar dan selalu akan terjadi [16] sebagai salah satu bentuk serangan pada cybercrime. Di masa yang akan datang, serangan phishing akan dilakukan dengan lebih mudah melalui smartphone dibandingkan melalui browser desktop.…”

Section: Celah Sistem Pada Smartphoneunclassified

Desain Arsitektur Aplikasi Qr Code Sebagai Anti Phishing Serangan Qr Code

Slamet

2023

SPIRIT

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QR Codes are very vulnerable to falsification because it is difficult to distinguish the original QR Code from a fake QR Code. Because of this vulnerability, the scanning process on fake QR Codes can direct users to dangerous sites with important information or data from the user. To assess QR Code security vulnerabilities and actions using a secure Application-based QR Code Architecture as Anti Phishing against QR Code attacks using hash functions and digital signatures. In experiments simulated attack types to malicious QR codes that redirect users to phishing sites. The real URL is disguised into the QR Code, where the user does not suspect, the URL is redirected to the fake site. As a result, intruders can easily use QR codes as vectors for phishing attacks targeted at smartphone users, even if they are using a browser that has security features.

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Section: Celah Sistem Pada Smartphoneunclassified

Desain Arsitektur Aplikasi Qr Code Sebagai Anti Phishing Serangan Qr Code

Slamet

2023

SPIRIT

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Detecting Phishing URLs With Word Embedding and Deep Learning

Selamat

Krejcar

2023

Advances in Systems Analysis, Software Engineering, and High Performance Computing

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The past decade has witnessed the rapid development of natural language processing and machine learning in the phishing detection domain. However, there needs to be more research on word embedding and deep learning for malicious URL classification. Inspired to solve this problem, this chapter aims to examine the application of word embedding and deep learning in extracting features from website URLs. To achieve this, several word embedding techniques, such as Keras, Word2Vec, GloVe, and FastText, were used to learn feature representations of webpage URLs. The obtained feature vectors were fed into a deep-learning model based on CNN-BiGRU for extraction and classification. Two different datasets were used to conduct numerous experiments, while various metrics were utilized to evaluate the phishing detection model's performance. The obtained findings indicated that when combined with deep learning, Keras outperformed other text embedding methods and achieved the best results across all evaluation metrics on both datasets.

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Phishing Website Detection as a Website Comparing Problem

Cited by 2 publications

References 24 publications

Desain Arsitektur Aplikasi Qr Code Sebagai Anti Phishing Serangan Qr Code

Desain Arsitektur Aplikasi Qr Code Sebagai Anti Phishing Serangan Qr Code

Detecting Phishing URLs With Word Embedding and Deep Learning

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