Phishing page detection via learning classifiers from page layout feature

Abstract

The web technology has become the cornerstone of a wide range of platforms, such as mobile services and smart Internet-of-things (IoT) systems. In such platforms, users' data are aggregated to a cloud-based platform, where web applications are used as a key interface to access and configure user data. Securing the web interface requires solutions to deal with threats from both technical vulnerabilities and social factors. Phishing attacks are one of the most commonly exploited vectors in social engineering attacks. The attackers use web pages visually mimicking legitimate web sites, such as banking and government services, to collect users' sensitive information. Existing phishing defense mechanisms based on URLs or page contents are often evaded by attackers. Recent research has demonstrated that visual layout similarity can be used as a robust basis to detect phishing attacks. In particular, features extracted from CSS layout files can be used to measure page similarity. However, it needs human expertise in specifying how to measure page similarity based on such features. In this paper, we aim to enable automated page-layout-based phishing detection techniques using machine learning techniques. We propose a learning-based aggregation analysis mechanism to decide page layout similarity, which is used to detect phishing pages. We prototype our solution and evaluate four popular machine learning classifiers on their accuracy and the factors affecting their results.

A comprehensive survey of AI-enabled phishing attacks detection techniques

“…They had combined their classifiers to acquire the maximum accuracy which they got from a DT. Authors in Mao et al [ 45 ] used different machine learning classifiers that include SVM, DT, AdaBoost, and RF to predict the phishing attack. Authors in Sahingoz et al [ 54 ] created their dataset.…”

“…They used other classifiers as well but their result on RF was better than other classifiers. Similarly, authors in Mao et al [ 45 ] collected the dataset of 49 phishing websites from PhishinTank.com . They used four learning classifiers to detect phishing attacks and concluded that the RF classifiers are much better than others.…”

Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

“…Mao et al. [142] and Yi et al [143] mentioned a web-based phishing problems for mobile and IoT systems where attackers are prone to access authorized users sensitive information using fake websites link. Wu et al [144] analyzed the effect of phishing attack between smart bulb mobile application and Nest server which launched to steal PIN code of the user.…”

A Lightweight Fine-Grained Searchable Encryption Scheme in Fog-Based Healthcare IoT Networks

“…As a new type of IBE proposed by [13], attribute-based encryption (ABE) plays a great role in access control, which is classified into the key-policy attribute-based encryption (KP-ABE) and the ciphertext-policy attributebased encryption (CP-ABE). KP-ABE associates user's private keys with the designated policies and tags ciphertexts with attributes, while CP-ABE is related to ciphertexts with the designated policies and identifies the user's private key with attributes [14,15]. Obviously, CP-ABE is a better choice to execute access control in our model since it is the user's ability to designate an access structure and process the encryption operation under the structure.…”