Phishing attacks are among emerging security issues that recently draws significant attention in the cyber security community. There are numerous existing approaches for phishing URL detection. However, malicious URL detection is still a research hotspot because attackers can bypass newly introduced detection mechanisms by changing their tactics. This paper will introduce a transformer-based malicious URL detection model, which has significant accuracy and outperforms current detection methods. We conduct experiments and compare them with six existing classical detection models. Experiments demonstrate that our transformer-based model is the best performing model from all perspectives among the seven models and achieves 97.3 % of detection accuracy.
The hypothesis here states that neural network algorithms such as Multi-layer Perceptron (MLP) have higher accuracy in differentiating malicious and semi-structured phishing URLs. Compared to classical machine learning algorithms such as Logistic Regression and Multinomial Naive Bayes, the classical algorithms rely heavily on substantial corpus data training and machine learning experts' domain knowledge to perform complex feature engineering. MLP could perform non-linear separable multi-classes classification and focus less on corpus feature training. In addition, backpropagation weight adjus…
Many cyberattacks start with disseminating phishing URLs. When clicking these phishing URLs, the victim's private information is leaked to the attacker. There have been proposed several machine learning methods to detect phishing URLs. However, it still remains under-explored to detect phishing URLs with evasion, i.e., phishing URLs that pretend to be benign by manipulating patterns. In many cases, the attacker i) reuses prepared phishing web pages because making a completely brand-new set costs non-trivial expenses, ii) prefers hosting companies that do not require private information and are…
PhishGuard: A Convolutional Neural Network Based Model for Detecting Phishing URLs with Explainability Analysis
Uttara University（乌拉拉大学）；University of Oslo（奥斯陆大学）；Khulna University of Engineering and Technology (KUET)（库尔纳工程技术大学）；BRAC University（BRAC大学）；North South University（北南大学）；American International University Bangladesh (AIUB)（孟加拉国美国国际大学）
Phishing attacks continue to be a significant threat on the Internet. Prior studies show that it is possible to determine whether a website is phishing or not just by analyzing its URL more carefully. A major advantage of the URL based approach is that it can identify a phishing website even before the web page is rendered in the browser, thus avoiding other potential problems such as cryptojacking and drive-by downloads. However, traditional URL based approaches have their limitations. Blacklist based approaches are prone to zero-hour phishing attacks, advanced machine learning based approach…
Blacklists are a widely-used Internet security mechanism to protect Internet users from financial scams, malicious web pages and other cyber attacks based on blacklisted URLs. In this demo, we introduce PhishChain, a transparent and decentralized system to blacklisting phishing URLs. At present, public/private domain blacklists, such as PhishTank, CryptoScamDB, and APWG, are maintained by a centralized authority, but operate in a crowd sourcing fashion to create a manually verified blacklist periodically. In addition to being a single point of failure, the blacklisting process utilized by such…
A Lightweight Hybrid MLP-Based Framework for Real-Time Phishing URL Detection Using Structural URL Features
Department of Cyber Security Science, School of Information and Communication Technology, Federal University of Technology, Minna, Nigeria（网络安全科学系，信息与通信技术学院，联邦科技大学，米纳，尼日利亚）
Spear Phishing is a type of cyber-attack where the attacker sends hyperlinks through email on well-researched targets. The objective is to obtain sensitive information by imitating oneself as a trustworthy website. In recent times, deep learning has become the standard for defending against such attacks. However, these architectures were designed with only defense in mind. Moreover, the attacker's perspective and motivation are absent while creating such models. To address this, we need a game-theoretic approach to understand the perspective of the attacker (Hacker) and the defender (Phishing …
Phishing is a type of social engineering attack with an intention to steal user data, including login credentials and credit card numbers, leading to financial losses for both organisations and individuals. It occurs when an attacker, pretending as a trusted entity, lure a victim into click on a link or attachment in an email, or in a text message. Phishing is often launched via email messages or text messages over social networks. Previous research has revealed that phishing attacks can be identified just by looking at URLs. Identifying the techniques which are used by phishers to mimic a phi…
VirusTotal (VT) provides aggregated threat intelligence on various entities including URLs, IP addresses, and binaries. It is widely used by researchers and practitioners to collect ground truth and evaluate the maliciousness of entities. In this work, we provide a comprehensive analysis of VT URL scanning reports containing the results of 95 scanners for 1.577 Billion URLs over two years. Individual VT scanners are known to be noisy in terms of their detection and attack type classification. To obtain high quality ground truth of URLs and actively take proper actions to mitigate different typ…
AntiPhishStack: LSTM-based Stacked Generalization Model for Optimized Phishing URL Detection
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences（中国科学院大学深圳先进技术学院）；University of Genoa（热那亚大学）；University of Catania（卡塔尼亚大学）；Shenzhen Polytechnic University（深圳职业技术大学）
Phishing attacks are an increasingly potent web-based threat, with nearly 1.5 million websites created on a monthly basis. In this work, we present the first study towards identifying such attacks through phishing reports shared by users on Twitter. We evaluated over 16.4k such reports posted by 701 Twitter accounts between June to August 2021, which contained 11.1k unique URLs, and analyzed their effectiveness using various quantitative and qualitative measures. Our findings indicate that not only do these users share a high volume of legitimate phishing URLs, but these reports contain more i…
Phishing is considered to be one of the most prevalent cyber-attacks because of its immense flexibility and alarmingly high success rate. Even with adequate training and high situational awareness, it can still be hard for users to continually be aware of the URL of the website they are visiting. Traditional detection methods rely on blocklists and content analysis, both of which require time-consuming human verification. Thus, there have been attempts focusing on the predictive filtering of such URLs. This study aims to develop a machine-learning model to detect fraudulent URLs which can be u…
The University of Adelaide（阿德莱德大学）；The Centre for Research on Engineering Software Technologies（工程软件技术研究中心）
ML-based Phishing URL (MLPU) detectors serve as the first level of defence to protect users and organisations from being victims of phishing attacks. Lately, few studies have launched successful adversarial attacks against specific MLPU detectors raising questions about their practical reliability and usage. Nevertheless, the robustness of these systems has not been extensively investigated. Therefore, the security vulnerabilities of these systems, in general, remain primarily unknown which calls for testing the robustness of these systems. In this article, we have proposed a methodology to in…
EvoMail: Self-Evolving Cognitive Agents for Adaptive Spam and Phishing Email Defense
People’s Liberation Army 77606 Unit（中国人民解放军77606单位）；Field Engineering College, Army Engineering University of PLA（陆军工程大学工程学院）；College Of Software, ZhengZhou University of Light Industry（郑州轻工业大学软件学院）
In the growing world of the internet, the number of ways to obtain crucial data such as passwords and login credentials, as well as sensitive personal information has expanded. Page impersonation, often known as phishing, is one method of obtaining such valuable information. Phishing is one of the most straightforward forms of cyberattack for hackers and one of the simplest for victims to fall for. It can also provide hackers with everything they need to get access to their target's personal and corporate accounts. Such websites do not offer a service, but instead, gather personal information …
Now-a-days, cyberattacks are increasing at an unprecedented rate. Phishing is a social engineering attack which has a massive global impact, destroying the financial and economic value of corporations, government sectors and individuals. In phishing, attackers steal users personal information such as username, passwords, debit card information and so on. In order to detect zero-hour attacks and protect end-users from these attacks, various anti-phishing techniques are developed, but the end-users have to visit the websites to know whether they are safe or not, which may lead to infecting their…
With more than 500 million daily tweets from over 330 million active users, Twitter constantly attracts malicious users aiming to carry out phishing and malware-related attacks against its user base. It therefore becomes of paramount importance to assess the effectiveness of Twitter's use of blacklists in protecting its users from such threats. We collected more than 182 million public tweets containing URLs from Twitter's Stream API over a 2-month period and compared these URLs against 3 popular phishing, social engineering, and malware blacklists, including Google Safe Browsing (GSB). We foc…
University of California, Irvine（加州大学欧文分校）；University of California, Riverside（加州大学河滨分校）
Phishing is an increasingly sophisticated method to steal personal user information using sites that pretend to be legitimate. In this paper, we take the following steps to identify phishing URLs. First, we carefully select lexical features of the URLs that are resistant to obfuscation techniques used by attackers. Second, we evaluate the classification accuracy when using only lexical features, both automatically and hand-selected, vs. when using additional features. We show that lexical features are sufficient for all practical purposes. Third, we thoroughly compare several classification al…
Web phishing remains a serious cyber threat responsible for most data breaches. Machine Learning (ML)-based anti-phishing detectors are seen as an effective countermeasure, and are increasingly adopted by web-browsers and software products. However, with an average of 10K phishing links reported per hour to platforms such as PhishTank and VirusTotal (VT), the deficiencies of such ML-based solutions are laid bare. We first explore how phishing sites bypass ML-based detection with a deep dive into 13K phishing pages targeting major brands such as Facebook. Results show successful evasion is caus…
Commonwealth Science and Industrial Research Organisation (CSIRO) Data61（澳大利亚联邦科学与工业研究组织Data61）；Cyber Security Cooperative Research Centre（网络安全合作研究中心）
URLs are central to a myriad of cyber-security threats, from phishing to the distribution of malware. Their inherent ease of use and familiarity is continuously abused by attackers to evade defences and deceive end-users. Seemingly dissimilar URLs are being used in an organized way to perform phishing attacks and distribute malware. We refer to such behaviours as campaigns, with the hypothesis being that attacks are often coordinated to maximize success rates and develop evasion tactics. The aim is to gain better insights into campaigns, bolster our grasp of their characteristics, and thus aid…
Phishing and spear-phishing are typical examples of masquerade attacks since trust is built up through impersonation for the attack to succeed. Given the prevalence of these attacks, considerable research has been conducted on these problems along multiple dimensions. We reexamine the existing research on phishing and spear-phishing from the perspective of the unique needs of the security domain, which we call security challenges: real-time detection, active attacker, dataset quality and base-rate fallacy. We explain these challenges and then survey the existing phishing/spear phishing solutio…
PhishSnap: Image-Based Phishing Detection Using Perceptual Hashing
Dept. of CSE United International University Dhaka, Bangladesh（计算机科学与工程系，国际联合大学，达卡，孟加拉国）；Professor, Dept. of CSE United International University Dhaka, Bangladesh（计算机科学与工程系，国际联合大学，达卡，孟加拉国）
Phishing is the simplest form of cybercrime with the objective of baiting people into giving away delicate information such as individually recognizable data, banking and credit card details, or even credentials and passwords. This type of simple yet most effective cyber-attack is usually launched through emails, phone calls, or instant messages. The credential or private data stolen are then used to get access to critical records of the victims and can result in extensive fraud and monetary loss. Hence, sending malicious messages to victims is a stepping stone of the phishing procedure. A \te…
Institute for Development and Research in Banking Technology (IDRBT)（银行技术发展研究院）；University of Hyderabad（海得拉巴大学）
Phishing is one of the most severe cyber-attacks where researchers are interested to find a solution. In phishing, attackers lure end-users and steal their personal in-formation. To minimize the damage caused by phishing must be detected as early as possible. There are various phishing attacks like spear phishing, whaling, vishing, smishing, pharming and so on. There are various phishing detection techniques based on white-list, black-list, content-based, URL-based, visual-similarity and machine-learning. In this paper, we discuss various kinds of phishing attacks, attack vectors and detection…
A Graph-Attentive LSTM Model for Malicious URL Detection
Department of Electrical and Electronic Engineering, Dhaka University of Engineering & Technology, Gazipur, Bangladesh（达卡工程与技术大学电子与电气工程系，孟加拉国加兹ipur）；Department of Computer Science and Engineering, Atish Dipankar University of Science and Technology, Dhaka, Bangladesh（阿蒂什迪帕克尔科学技术大学计算机科学与工程系，孟加拉国达卡）；Department of Computer Science, Utah Valley University, 800 W University Pkwy, Orem, UT 84058, USA（犹他山谷大学计算机科学系，美国奥尔姆，UT 84058）
Cyber Technology Institute（网络技术研究院）；Al-Qunfudah College of Computing（古尼富达计算机学院）；Umm Al-Qura University（乌姆阿尔古拉大学）；De Montfort University（德蒙福特大学）
The persistent growth in phishing and the rising volume of phishing websites has led to individuals and organizations worldwide becoming increasingly exposed to various cyber-attacks. Consequently, more effective phishing detection is required for improved cyber defence. Hence, in this paper we present a deep learning-based approach to enable high accuracy detection of phishing sites. The proposed approach utilizes convolutional neural networks (CNN) for high accuracy classification to distinguish genuine sites from phishing sites. We evaluate the models using a dataset obtained from 6,157 gen…
University of Tokyo（东京大学）；United Nations University（联合国大学）
With increasingly more sophisticated phishing campaigns in recent years, phishing emails lure people using more legitimate-looking personal contexts. To tackle this problem, instead of traditional heuristics-based algorithms, more adaptive detection systems such as natural language processing (NLP)-powered approaches are essential to understanding phishing text representations. Nevertheless, concerns surrounding the collection of phishing data that might cover confidential information hinder the effectiveness of model learning. We propose a decentralized phishing email detection framework call…
Browsers often include security features to detect phishing web pages. In the past, some browsers evaluated an unknown URL for inclusion in a list of known phishing pages. However, as the number of URLs and known phishing pages continued to increase at a rapid pace, browsers started to include one or more machine learning classifiers as part of their security services that aim to better protect end users from harm. While additional information could be used, browsers typically evaluate every unknown URL using some classifier in order to quickly detect these phishing pages. Early phishing detec…
Malicious websites are responsible for a majority of the cyber-attacks and scams today. Malicious URLs are delivered to unsuspecting users via email, text messages, pop-ups or advertisements. Clicking on or crawling such URLs can result in compromised email accounts, launching of phishing campaigns, download of malware, spyware and ransomware, as well as severe monetary losses. A machine learning based ensemble classification approach is proposed to detect malicious URLs in emails, which can be extended to other methods of delivery of malicious URLs. The approach uses static lexical features e…
Phishing attacks have evolved and increased over time and, for this reason, the task of distinguishing between a legitimate site and a phishing site is more and more difficult, fooling even the most expert users. The main proposals focused on addressing this problem can be divided into four approaches: List-based, URL based, content-based, and hybrid. In this state of the art, the most recent techniques using web content-based and hybrid approaches for Phishing Detection are reviewed and compared.
The problem of detecting phishing emails through machine learning techniques has been discussed extensively in the literature. Conventional and state-of-the-art machine learning algorithms have demonstrated the possibility of building classifiers with high accuracy. The existing research studies treat phishing and genuine emails through general indicators and thus it is not exactly clear what phishing features are contributing to variations of the classifiers. In this paper, we crafted a set of phishing and legitimate emails with similar indicators in order to investigate whether these cues ar…
