Phishing attacks are a type of cybercrime that has grown in recent years. It is part of social engineering attacks where an attacker deceives users by sending fake messages using social media platforms or emails. Phishing attacks steal users’ information or download and install malicious software. They are hard to detect because attackers can design a phishing message that looks legitimate to a user. This message may contain a phishing URL so that even an expert can be a victim. This URL leads the victim to a fake website that steals information, such as login information, payment information, etc. Researchers and engineers work to develop methods to detect phishing attacks without the need for the eyes of experts. Even though many papers discuss HTML and URL-based phishing detection methods, there is no comprehensive survey to discuss these methods. Therefore, this paper comprehensively surveys HTML and URL phishing attacks and detection methods. We review the current state-of-art deep learning models to detect URL-based and hybrid-based phishing attacks in detail. We compare each model based on its data preprocessing, feature extraction, model design, and performance.

Phishing attacks are a major threat to online security, resulting in millions of dollars in losses. These attacks constantly evolve, forcing the cyber security community to improve detection systems. One major problem with current detection systems is that they cannot detect new phishing attacks, such as Browser in the Browser (BiTB) and malvertising attacks. These attacks hide behind legitimate Uniform Resource Locators (URLs) and can evade detection systems that only analyze a web page URL without exploring the page content. To address this problem, we propose PhishTransformer, a deep-learning model that can detect phishing attacks by analyzing URLs and page content. We propose only using URLs embedded within a webpage, such as hyperlinks and JFrames, to train PhishTransformer. This helps reduce the number of features that need to be extracted from the page content, which makes training the model more efficient. PhishTransformer combines convolutional neural networks and transformer encoders to extract features from website URLs and page content. These features are then used to train a classifier that can distinguish between phishing attacks and legitimate websites. We tested PhishTransformer on a dataset of 10,000 URLs. Our results show that PhishTransformer can achieve an F1-score of 99%, precision of 99%, and recall of 99%. This result suggests that PhishTransformer is a promising new approach to phishing detection.

Ransomware attacks continue to pose a significant challenge to cybersecurity, causing substantial financial and reputational damage to individuals and organizations. These attacks typically encrypt user data and demand a ransom for its release. There is a growing need for more effective and dynamic detection methods, especially for zero-day and unknown ransomware variants. This survey focuses on ransomware-detecting methods published from 2019 to 2025. One hundred thirty-five papers were reviewed and filtered based on their scope and publication venue. For in-depth analysis, this survey selected 45 papers focusing on ransomware detection forWindows or Android operating systems. This paper aims to comprehensively review existing ransomware detection methods, focusing on their effectiveness, limitations, and applicability. The detection methods are categorized into machine-learning-based and non-machine-learning-based methods, discussing the advantages and drawbacks of each. The paper also highlights ransomware-as-a-service, explaining what it is, how it works, and how it affects the increasing number of ransomware attacks in recent years. It also studies the datasets used in the reviewed literature, listing their structures and limitations. This survey identifies gaps in current research and suggests future directions for developing more robust ransomware detection systems.

Ransomware remains one of the most significant cybersecurity threats. Techniques used by attackers have evolved to bypass traditional detection methods. Many existing detection systems rely on outdated datasets or complex behavioral analyses, which are resource-intensive and slow. This paper introduces RansomFormer, a Transformer-based model that is designed to detect ransomware using Portable Executable (PE) byte data combined with Application Programming Interface (API) imports or API sequence calls. The evaluation is conducted to determine whether these static and dynamic features alone can achieve high accuracy. To test this hypothesis, the largest ransomware dataset to date is collected, consisting of more than 150 ransomware families. The limitations of existing datasets, which are outdated, lack family variants, or are too small, are addressed by this dataset. RansomFormer is trained and evaluated on the following two datasets: one using static analysis (PE bytes and API imports) and another combining static and dynamic analysis (PE bytes and API sequence calls). The results demonstrate that the model achieves high accuracy, with 99.25% on the static dataset and 99.50% on the combined dataset, making RansomFormer a promising approach for ransomware detection.

The intelligent road is an important component of the intelligent vehicle infrastructure cooperative system, the latest development of intelligent transportation systems. As an advanced sensor, Light Detection and Ranging (LiDAR) has gradually been used to collect high-resolution micro-traffic data on the roadside of intelligent roads. Furthermore, a fusion of multiple LiDARs has become a current hot spot to extend the data collection range and improve detection accuracy. This paper focuses on point cloud registration in a complex traffic environment and proposes a three-dimensional (3D) registration method based on traffic signs and prior knowledge of traffic scenes. Traffic signs with their reflective films are used as reference targets to register 3D point cloud data from roadside LiDARs. The proposed method consists of a vertical registration and a horizontal registration. For the vertical registration, we propose a panel rotation algorithm to rotate the initial point cloud to register it vertically, converting the 3D point cloud registration into a two-dimensional (2D) rigid body transformation. For the vertical registration, our system registers traffic signs from different LiDARs. Our method has been verified in some actual scenarios. Compared with previous methods, the proposed method is automatic and does not need to search reference targets manually. Furthermore, it is suitable for actual engineering use and can be applied to sparse point cloud data from LiDAR with few beams, realizing point cloud registration of large disparity.