## 1 Introduce  
Phishing attacks have evolved over the years, employing a variety of tactics to deceive users into divulging sensitive information, such as login credentials and financial data [1; 2; 3; 4; 5; 6]. The harm caused by phishing attacks is substantial, often resulting in considerable financial losses, identity theft, and damage to the targeted organizations' reputations. In recent years, the number and sophistication of phishing attacks have increased, with cybercriminals continually developing new techniques to evade detection and target unsuspecting victims. One notable phishing attack in 2016 involved the Bangladesh Bank, where attackers stole USD 81 million by compromising the bank's systems using a phishing email [7]. Another prominent example is the 2020 Twitter Bitcoin scam, where high-profile accounts were hijacked to promote a cryptocurrency scam, causing reputational damage to the social media platform and resulting in financial losses for some users [8]. These incidents highlight the pervasive nature of phishing attacks and the need for effective countermeasures. Various anti-phishing methods have been proposed to address these threats, ranging from traditional blacklist-based approaches to more sophisticated machine learning techniques. Blacklist-based approaches rely on maintaining a list of known phishing websites,which can quickly become outdated due to the dynamic nature of phishing attacks [9; 10]. Machine-learning-based methods offer the potential to automatically learn and adapt to new phishing tactics by analyzing features such as URLs, website content, and behavioral patterns [11]. URL analysis, in particular, has been extensively researched, and advanced representation learning techniques, such as computer vision neural architectures and natural language processing models, have been employed to extract feature patterns from URLs, enabling rapid and accurate phishing URL detection [12; 13; 14]. However, these methods have their limitations. For instance, they may struggle to detect attacks that employ advanced obfuscation techniques, such as masquerading URL addresses. Additionally, phishing attackers frequently change their website URLs to evade detection, posing challenges for machine learning models that rely on URL analysis. Consequently, identifying and leveraging additional features beyond URLs, such as website content, visual appearance, and behavioral patterns, is crucial for building robust anti-phishing countermeasures. Exploring these alternative features can potentially enhance the performance of machine-learning-based phishing detection models. By combining multiple feature sets, it is possible to develop more comprehensive and resilient anti-phishing strategies that can better adapt to the evolving tactics of phishing attackers, ultimately safeguarding users from a wider range of threats. In this paper, we introduce a lightweight multi-view learning-based approach for detecting phishing websites by considering a diverse and easily obtainable set of website information. Our objective is to provide a highly usable, robust, and accurate solution for phishing detection, aiming to enhance real-world performance, including challenging scenarios such as imbalanced class detection, small-scale datasets used for learning, and long-term model performance. Our contributions are as follows:  
* The proposed method employs adversarial post-training to transform a pretrained language model into a powerful URL feature extractor. This approach allows us to transfer the general knowledge learned from massive data by the pretrained language model to URL representation learning at a low cost, resulting in semantic-aware URL embeddings. * Our research introduces three highly informative website features that are easily obtained yet possess significant discriminative value. These features encompass descriptions of website attributes, content, and behavior, which, in conjunction with the website's URL, form the multi-view information of the website.