Understanding Phishing and Phishing Techniques in Client-Side Web-Based Systems

As auspicious as the technology is, the bane of the internet has always been the constant threats of online identity theft and other forms of fraud prevalent on the information highway. Phishing is a form of internet fraud in which emails and websites that are purportedly from legitimate organisations and agencies are used to deceive users into disclosing personal or financial information. Despite the plethora of anti-spam filters that are readily available today, phishing emails are still able to bypass such measures and find their ways into users’ inboxes. This challenge at the client side of the web-based infrastructure is prevalent as clients are at varying levels of usage and knowledge of internet infrastructure. This paper takes a look at the phishing scenario by examining why it works. We provide extensive insights into extant literature in the subject domain as a basis for the development of tools to mitigate phishing and assisting users understand phishing attacks

Tutorial and Critical Analysis of Phishing Websites Methods

The Internet has become an essential component of our everyday social and financial activities. Internet is not important for individual users only but also for organizations, because organizations that offer online trading can achieve a competitive edge by serving worldwide clients. Internet facilitates reaching customers all over the globe without any market place restrictions and with effective use of e-commerce. As a result, the number of customers who rely on the Internet to perform procurements is increasing dramatically. Hundreds of millions of dollars are transferred through the Internet every day. This amount of money was tempting the fraudsters to carry out their fraudulent operations. Hence, Internet users may be vulnerable to different types of web threats, which may cause financial damages, identity theft, loss of private information, brand reputation damage and loss of customers’ confidence in e-commerce and online banking. Therefore, suitability of the Internet for commercial transactions becomes doubtful. Phishing is considered a form of web threats that is defined as the art of impersonating a website of an honest enterprise aiming to obtain user’s confidential credentials such as usernames, passwords and social security numbers. In this article, the phishing phenomena will be discussed in detail. In addition, we present a survey of the state of the art research on such attack. Moreover, we aim to recognize the up-to-date developments in phishing and its precautionary measures and provide a comprehensive study and evaluation of these researches to realize the gap that is still predominating in this area. This research will mostly focus on the web based phishing detection methods rather than email based detection methods

Real-Time Client-Side Phishing Prevention

In the last decades researchers and companies have been working to deploy effective solutions to steer users away from phishing websites. These solutions are typically based on servers or blacklisting systems. Such approaches have several drawbacks: they compromise user privacy, rely on off-line analysis, are not robust against adaptive attacks and do not provide much guidance to the users in their warnings. To address these limitations, we developed a fast real-time client-side phishing prevention software that implements a phishing detection technique recently developed by Marchal et al. It extracts information from the visited webpage and detects if it is a phish to warn the user. It is also able to detect the website that the phish is trying to mimic and propose a redirection to the legitimate domain. Furthermore, to attest the validity of our solution we performed two user studies to evaluate the usability of the interface and the program's impact on user experience

A taxonomy of phishing research

Phishing is a widespread threat that has attracted a lot of attention from the security community. A significant amount of research has focused on designing automated mitigation techniques. However, these techniques have largely only proven successful at catching previously witnessed phishing campaigns. Characteristics of phishing emails and web pages were thoroughly analyzed, but not enough emphasis was put on exploring alternate attack vectors. Novel education approaches were shown to be effective at teaching users to recognize phishing attacks and are adaptable to other kinds of threats. In this thesis, we explore a large amount of existing literature on phishing and present a comprehensive taxonomy of the current state of phishing research. With our extensive literature review, we will illuminate both areas of phishing research we believe will prove fruitful and areas that seem to be oversaturated

Secure entity authentication

According to Wikipedia, authentication is the act of confirming the truth of an attribute of a single piece of a datum claimed true by an entity. Specifically, entity authentication is the process by which an agent in a distributed system gains confidence in the identity of a communicating partner (Bellare et al.). Legacy password authentication is still the most popular one, however, it suffers from many limitations, such as hacking through social engineering techniques, dictionary attack or database leak. To address the security concerns in legacy password-based authentication, many new authentication factors are introduced, such as PINs (Personal Identification Numbers) delivered through out-of-band channels, human biometrics and hardware tokens. However, each of these authentication factors has its own inherent weaknesses and security limitations. For example, phishing is still effective even when using out-of-band-channels to deliver PINs (Personal Identification Numbers). In this dissertation, three types of secure entity authentication schemes are developed to alleviate the weaknesses and limitations of existing authentication mechanisms: (1) End user authentication scheme based on Network Round-Trip Time (NRTT) to complement location based authentication mechanisms; (2) Apache Hadoop authentication mechanism based on Trusted Platform Module (TPM) technology; and (3) Web server authentication mechanism for phishing detection with a new detection factor NRTT. In the first work, a new authentication factor based on NRTT is presented. Two research challenges (i.e., the secure measurement of NRTT and the network instabilities) are addressed to show that NRTT can be used to uniquely and securely identify login locations and hence can support location-based web authentication mechanisms. The experiments and analysis show that NRTT has superior usability, deploy-ability, security, and performance properties compared to the state-of-the-art web authentication factors. In the second work, departing from the Kerb eros-centric approach, an authentication framework for Hadoop that utilizes Trusted Platform Module (TPM) technology is proposed. It is proven that pushing the security down to the hardware level in conjunction with software techniques provides better protection over software only solutions. The proposed approach provides significant security guarantees against insider threats, which manipulate the execution environment without the consent of legitimate clients. Extensive experiments are conducted to validate the performance and the security properties of the proposed approach. Moreover, the correctness and the security guarantees are formally proved via Burrows-Abadi-Needham (BAN) logic. In the third work, together with a phishing victim identification algorithm, NRTT is used as a new phishing detection feature to improve the detection accuracy of existing phishing detection approaches. The state-of-art phishing detection methods fall into two categories: heuristics and blacklist. The experiments show that the combination of NRTT with existing heuristics can improve the overall detection accuracy while maintaining a low false positive rate. In the future, to develop a more robust and efficient phishing detection scheme, it is paramount for phishing detection approaches to carefully select the features that strike the right balance between detection accuracy and robustness in the face of potential manipulations. In addition, leveraging Deep Learning (DL) algorithms to improve the performance of phishing detection schemes could be a viable alternative to traditional machine learning algorithms (e.g., SVM, LR), especially when handling complex and large scale datasets

Look Before You Leap: Detecting Phishing Web Pages by Exploiting Raw URL And HTML Characteristics

Cybercriminals resort to phishing as a simple and cost-effective medium to perpetrate cyber-attacks on today's Internet. Recent studies in phishing detection are increasingly adopting automated feature selection over traditional manually engineered features. This transition is due to the inability of existing traditional methods to extrapolate their learning to new data. To this end, in this paper, we propose WebPhish, a deep learning technique using automatic feature selection extracted from the raw URL and HTML of a web page. This approach is the first of its kind, which uses the concatenation of URL and HTML embedding feature vectors as input into a Convolutional Neural Network model to detect phishing attacks on web pages. Extensive experiments on a real-world dataset yielded an accuracy of 98 percent, outperforming other state-of-the-art techniques. Also, WebPhish is a client-side strategy that is completely language-independent and can conduct lightweight phishing detection regardless of the web page's textual language

An Android Security Policy Enforcement Tool

The Android operating system (OS) has become the dominant smart phone OS in recent years due to its accessibility, usability and its open-source philosophy. Consequently, this has also made it a popular target for attackers who aim to install malware on Android devices and take advantage of Android’s coarse-grained, non-revoking permission system. This project designs, implements and evaluates a security tool named COMBdroid, which addresses these security concerns in Android by enforcing fine-grained, user-defined policies. COMBdroid modifies an application before installation, allowing it to override points of security vulnerabilities at run-time. As a proof of concept we have implemented three policies in COMBdroid. This paper documents the development process of COMBdroid, deriving design decisions from the literature review, detailing the design and implementation, and proving the program’s effectiveness through evaluation.

Counteracting phishing through HCI

Computer security is a very technical topic that is in many cases hard to grasp for the average user. Especially when using the Internet, the biggest network connecting computers globally together, security and safety are important. In many cases they can be achieved without the user's active participation: securely storing user and customer data on Internet servers is the task of the respective company or service provider, but there are also a lot of cases where the user is involved in the security process, especially when he or she is intentionally attacked. Socially engineered phishing attacks are such a security issue were users are directly attacked to reveal private data and credentials to an unauthorized attacker. These types of attacks are the main focus of the research presented within my thesis. I have a look at how these attacks can be counteracted by detecting them in the first place but also by mediating these detection results to the user. In prior research and development these two areas have most often been regarded separately, and new security measures were developed without taking the final step of interacting with the user into account. This interaction mainly means presenting the detection results and receiving final decisions from the user. As an overarching goal within this thesis I look at these two aspects united, stating the overall protection as the sum of detection and "user intervention". Within nine different research projects about phishing protection this thesis gives answers to ten different research questions in the areas of creating new phishing detectors (phishing detection) and providing usable user feedback for such systems (user intervention): The ten research questions cover five different topics in both areas from the definition of the respective topic over ways how to measure and enhance the areas to finally reasoning about what is making sense. The research questions have been chosen to cover the range of both areas and the interplay between them. They are mostly answered by developing and evaluating different prototypes built within the projects that cover a range of human-centered detection properties and evaluate how well these are suited for phishing detection. I also take a look at different possibilities for user intervention (e.g. how should a warning look like? should it be blocking or non-blocking or perhaps even something else?). As a major contribution I finally present a model that combines phishing detection and user intervention and propose development and evaluation recommendations for similar systems. The research results show that when developing security detectors that yield results being relevant for end users such a detector can only be successful in case the final user feedback already has been taken into account during the development process.Sicherheit rund um den Computer ist ein, für den durchschnittlichen Benutzer schwer zu verstehendes Thema. Besonders, wenn sich die Benutzer im Internet - dem größten Netzwerk unserer Zeit - bewegen, ist die technische und persönliche Sicherheit der Benutzer extrem wichtig. In vielen Fällen kann diese ohne das Zutun des Benutzers erreicht werden. Datensicherheit auf Servern zu garantieren obliegt den Dienstanbietern, ohne dass eine aktive Mithilfe des Benutzers notwendig ist. Es gibt allerdings auch viele Fälle, bei denen der Benutzer Teil des Sicherheitsprozesses ist, besonders dann, wenn er selbst ein Opfer von Attacken wird. Phishing Attacken sind dabei ein besonders wichtiges Beispiel, bei dem Angreifer versuchen durch soziale Manipulation an private Daten des Nutzers zu gelangen. Diese Art der Angriffe stehen im Fokus meiner vorliegenden Arbeit. Dabei werfe ich einen Blick darauf, wie solchen Attacken entgegen gewirkt werden kann, indem man sie nicht nur aufspürt, sondern auch das Ergebnis des Erkennungsprozesses dem Benutzer vermittelt. Die bisherige Forschung und Entwicklung betrachtete diese beiden Bereiche meistens getrennt. Dabei wurden Sicherheitsmechanismen entwickelt, ohne den finalen Schritt der Präsentation zum Benutzer hin einzubeziehen. Dies bezieht sich hauptsächlich auf die Präsentation der Ergebnisse um dann den Benutzer eine ordnungsgemäße Entscheidung treffen zu lassen. Als übergreifendes Ziel dieser Arbeit betrachte ich diese beiden Aspekte zusammen und postuliere, dass Benutzerschutz die Summe aus Problemdetektion und Benutzerintervention' ("user intervention") ist. Mit Hilfe von neun verschiedenen Forschungsprojekten über Phishingschutz beantworte ich in dieser Arbeit zehn Forschungsfragen über die Erstellung von Detektoren ("phishing detection") und das Bereitstellen benutzbaren Feedbacks für solche Systeme ("user intervention"). Die zehn verschiedenen Forschungsfragen decken dabei jeweils fünf verschiedene Bereiche ab. Diese Bereiche erstrecken sich von der Definition des entsprechenden Themas über Messmethoden und Verbesserungsmöglichkeiten bis hin zu Überlegungen über das Kosten-Nutzen-Verhältnis. Dabei wurden die Forschungsfragen so gewählt, dass sie die beiden Bereiche breit abdecken und auf die Abhängigkeiten zwischen beiden Bereichen eingegangen werden kann. Die Forschungsfragen werden hauptsächlich durch das Schaffen verschiedener Prototypen innerhalb der verschiedenen Projekte beantwortet um so einen großen Bereich benutzerzentrierter Erkennungsparameter abzudecken und auszuwerten wie gut diese für die Phishingerkennung geeignet sind. Außerdem habe ich mich mit den verschiedenen Möglichkeiten der Benutzerintervention befasst (z.B. Wie sollte eine Warnung aussehen? Sollte sie Benutzerinteraktion blockieren oder nicht?). Ein weiterer Hauptbeitrag ist schlussendlich die Präsentation eines Modells, dass die Entwicklung von Phishingerkennung und Benutzerinteraktionsmaßnahmen zusammenführt und anhand dessen dann Entwicklungs- und Analyseempfehlungen für ähnliche Systeme gegeben werden. Die Forschungsergebnisse zeigen, dass Detektoren im Rahmen von Computersicherheitsproblemen die eine Rolle für den Endnutzer spielen nur dann erfolgreich entwickelt werden können, wenn das endgültige Benutzerfeedback bereits in den Entwicklungsprozesses des Detektors einfließt