Detecting Phishing Websites via Aggregation Analysis of Page Layouts

Mao, Jian; Bian, Jingdong; Tian, Wenqian; Zhu, Shishi; Wei, Tao; Li, Aili; Liang, Zhenkai

doi:10.1016/j.procs.2018.03.053

Cited by 47 publications

(29 citation statements)

References 17 publications

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“…It can be seen that the multidimensional feature algorithm significantly improves the accuracy and reduces FPR, FNR and cost compared with CNN-LSTM and the traditional feature extraction method. Table 5 illustrates the three metrics of MFPD and other approaches (Mao et al [11], CANTINA+ [19], Bahnse et al [32]) based on the evaluation value in the papers. In order to facilitate comparison, we calculate the three metrics based on our experiment results.…”

Section: Experiments On the Multidimensional Feature Algorithmmentioning

confidence: 99%

“…The reason is that malicious URLs or phishing webpages have some characteristics that can be distinguished from legitimate websites, and machine learning can be effective in this regard for processing. Current mainstream machine learning methods of phishing website detection extract statistical features from the URL and the host [10] or extract relevant features of the webpage, such as the layout, CSS, text [11], [12], and then classify these features. However, these methods only analyze the URL or extract features from a single perspective, which makes it difficult to extract the complete attributes of phishing websites.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Phishing Website Detection Based on Multidimensional Features Driven by Deep Learning

Yang¹,

Zhao²,

Zeng³

2019

IEEE Access

217

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As a crime of employing technical means to steal sensitive information of users, phishing is currently a critical threat facing the Internet, and losses due to phishing are growing steadily. Feature engineering is important in phishing website detection solutions, but the accuracy of detection critically depends on prior knowledge of features. Moreover, although features extracted from different dimensions are more comprehensive, a drawback is that extracting these features requires a large amount of time. To address these limitations, we propose a multidimensional feature phishing detection approach based on a fast detection method by using deep learning. In the first step, character sequence features of the given URL are extracted and used for quick classification by deep learning, and this step does not require thirdparty assistance or any prior knowledge about phishing. In the second step, we combine URL statistical features, webpage code features, webpage text features, and the quick classification result of deep learning into multidimensional features. The approach can reduce the detection time for setting a threshold. Testing on a dataset containing millions of phishing URLs and legitimate URLs, the accuracy reaches 98.99%, and the false positive rate is only 0.59%. By reasonably adjusting the threshold, the experimental results show that the detection efficiency can be improved. INDEX TERMS Phishing website detection, convolutional neural network, long short-term memory network, semantic feature, machine learning.

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Section: Experiments On the Multidimensional Feature Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Phishing Website Detection Based on Multidimensional Features Driven by Deep Learning

Yang¹,

Zhao²,

Zeng³

2019

IEEE Access

217

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“…Authors in Mao et al [ 44 ] proposed a learning-based system to choose page design comparability used to distinguish phishing attack pages. for effective page layout features, they characterized the guidelines and build up a phishing page classifier with two conventional learning-algorithms, SVM and DT.…”

Section: Literature Surveymentioning

confidence: 99%

A comprehensive survey of AI-enabled phishing attacks detection techniques

et al. 2020

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In recent times, a phishing attack has become one of the most prominent attacks faced by internet users, governments, and service-providing organizations. In a phishing attack, the attacker(s) collects the client’s sensitive data (i.e., user account login details, credit/debit card numbers, etc.) by using spoofed emails or fake websites. Phishing websites are common entry points of online social engineering attacks, including numerous frauds on the websites. In such types of attacks, the attacker(s) create website pages by copying the behavior of legitimate websites and sends URL(s) to the targeted victims through spam messages, texts, or social networking. To provide a thorough understanding of phishing attack(s), this paper provides a literature review of Artificial Intelligence (AI) techniques: Machine Learning, Deep Learning, Hybrid Learning, and Scenario-based techniques for phishing attack detection. This paper also presents the comparison of different studies detecting the phishing attack for each AI technique and examines the qualities and shortcomings of these methodologies. Furthermore, this paper provides a comprehensive set of current challenges of phishing attacks and future research direction in this domain.

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“…Not only bad email classification, but phishing webpage classification is also a popular research applied with machine learn-ing. Mao et al [8] proposed a learning-based aggregation analysis mechanism to determine the similarity of page layouts and detect phishing pages. Their approach automatically trains classifiers to determine web page similarity from CSS layout features, without requiring a human expert.…”

Section: Related Researchmentioning

confidence: 99%

Zero-day Malicious Email Investigation and Detection Using Features with Deep-learning Approach

Phomkeona

Okamura

2020

Journal of Information Processing

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Cyber hackers use email as a tool to trick, inject or drop malicious software into the recipient's device. Everyday users have to face off against, phishing or malicious emails and it would be a huge problem for whole organizations even if only one user clicked on a single link from this malicious email. The difficult issue is how to classify and detect those malicious emails from ordinary, especially spear phishing emails, which are designed for a particular target, or zero-day malicious emails that no one has ever found until now. In this paper, we introduce a way to classify and detect zero-day malicious emails by using deep-learning with data investigated from the email header and body itself, combined with dynamic analysis information as a group of features. Four different language email datasets can be used to train and test the system to simulate real-world diversity and zero-day malicious email attack situations. We succeeded in obtaining a satisfactory accuracy rate for detection results for both zero-day malicious email types and normal spam.

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Detecting Phishing Websites via Aggregation Analysis of Page Layouts

Cited by 47 publications

References 17 publications

Phishing Website Detection Based on Multidimensional Features Driven by Deep Learning

Phishing Website Detection Based on Multidimensional Features Driven by Deep Learning

A comprehensive survey of AI-enabled phishing attacks detection techniques

Zero-day Malicious Email Investigation and Detection Using Features with Deep-learning Approach

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