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Register a new userKnow Your Phish: Novel Techniques for Detecting Phishing Sites and their Targets
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Added by
Samuel Marchal
Publication date
2015
fields
Informatics Engineering
and research's language is
English
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Phishing is a major problem on the Web. Despite the significant attention it has received over the years, there has been no definitive solution. While the state-of-the-art solutions have reasonably good performance, they require a large amount of training data and are not adept at detecting phishing attacks against new targets. In this paper, we begin with two core observations: (a) although phishers try to make a phishing webpage look similar to its target, they do not have unlimited freedom in structuring the phishing webpage; and (b) a webpage can be characterized by a small set of key terms; how these key terms are used in different parts of a webpage is different in the case of legitimate and phishing webpages. Based on these observations, we develop a phishing detection system with several notable properties: it is language-independent, can be implemented entirely on client-side, has excellent classification performance and is fast. In addition, we developed a target identification component that can identify the target website that a phishing webpage is attempting to mimic. The target detection component is faster than previously reported systems and can help minimize false positives in our phishing detection system.
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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 techniques for detecting the phishing sites. Performance comparison of 18 different models along with nine different sources of datasets are given. Challenges in phishing detection techniques are also given.
Today, two-factor authentication (2FA) is a widely implemented mechanism to counter phishing attacks. Although much effort has been investigated in 2FA, most 2FA systems are still vulnerable to carefully designed phishing attacks, and some even request special hardware, which limits their wide deployment. Recently, real-time phishing (RTP) has made the situation even worse because an adversary can effortlessly establish a phishing website replicating a target website without any background of the web page design technique. Traditional 2FA can be easily bypassed by such RTP attacks. In this work, we propose a novel 2FA system to counter RTP attacks. The main idea is to request a user to take a photo of the web browser with the domain name in the address bar as the 2nd authentication factor. The web server side extracts the domain name information based on Optical Character Recognition (OCR), and then determines if the user is visiting this website or a fake one, thus defeating the RTP attacks where an adversary must set up a fake website with a different domain. We prototyped our system and evaluated its performance in various environments. The results showed that PhotoAuth is an effective technique with good scalability. We also showed that compared to other 2FA systems, PhotoAuth has several advantages, especially no special hardware or software support is needed on the client side except a phone, making it readily deployable.
We present the first large-scale characterization of lateral phishing attacks, based on a dataset of 113 million employee-sent emails from 92 enterprise organizations. In a lateral phishing attack, adversaries leverage a compromised enterprise account to send phishing emails to other users, benefitting from both the implicit trust and the information in the hijacked users account. We develop a classifier that finds hundreds of real-world lateral phishing emails, while generating under four false positives per every one-million employee-sent emails. Drawing on the attacks we detect, as well as a corpus of user-reported incidents, we quantify the scale of lateral phishing, identify several thematic content and recipient targeting strategies that attackers follow, illuminate two types of sophisticated behaviors that attackers exhibit, and estimate the success rate of these attacks. Collectively, these results expand our mental models of the enterprise attacker and shed light on the current state of enterprise phishing attacks.
Internet memes have become powerful means to transmit political, psychological, and socio-cultural ideas. Although memes are typically humorous, recent days have witnessed an escalation of harmful memes used for trolling, cyberbullying, and abusing social entities. Detecting such harmful memes is challenging as they can be highly satirical and cryptic. Moreover, while previous work has focused on specific aspects of memes such as hate speech and propaganda, there has been little work on harm in general, and only one specialized dataset for it. Here, we focus on bridging this gap. In particular, we aim to solve two novel tasks: detecting harmful memes and identifying the social entities they target. We further extend the recently released HarMeme dataset to generalize on two prevalent topics - COVID-19 and US politics and name the two datasets as Harm-C and Harm-P, respectively. We then propose MOMENTA (MultimOdal framework for detecting harmful MemEs aNd Their tArgets), a novel multimodal (text + image) deep neural model, which uses global and local perspectives to detect harmful memes. MOMENTA identifies the object proposals and attributes and uses a multimodal model to perceive the comprehensive context in which the objects and the entities are portrayed in a given meme. MOMENTA is interpretable and generalizable, and it outperforms numerous baselines.
Adversarial patch attack against image classification deep neural networks (DNNs), in which the attacker can inject arbitrary distortions within a bounded region of an image, is able to generate adversarial perturbations that are robust (i.e., remain adversarial in physical world) and universal (i.e., remain adversarial on any input). It is thus important to detect and mitigate such attack to ensure the security of DNNs. This work proposes Jujutsu, a technique to detect and mitigate robust and universal adversarial patch attack. Jujutsu leverages the universal property of the patch attack for detection. It uses explainable AI technique to identify suspicious features that are potentially malicious, and verify their maliciousness by transplanting the suspicious features to new images. An adversarial patch continues to exhibit the malicious behavior on the new images and thus can be detected based on prediction consistency. Jujutsu leverages the localized nature of the patch attack for mitigation, by randomly masking the suspicious features to remove adversarial perturbations. However, the network might fail to classify the images as some of the contents are removed (masked). Therefore, Jujutsu uses image inpainting for synthesizing alternative contents from the pixels that are masked, which can reconstruct the clean image for correct prediction. We evaluate Jujutsu on five DNNs on two datasets, and show that Jujutsu achieves superior performance and significantly outperforms existing techniques. Jujutsu can further defend against various variants of the basic attack, including 1) physical-world attack; 2) attacks that target diverse classes; 3) attacks that use patches in different shapes and 4) adaptive attacks.
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