@phdthesis{Jovanovic2015, author = {Jovanovic, Philipp}, title = {Analysis and Design of Symmetric Cryptographic Algorithms}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-3319}, school = {Universit{\"a}t Passau}, pages = {216}, year = {2015}, abstract = {This doctoral thesis is dedicated to the analysis and the design of symmetric cryptographic algorithms. In the first part of the dissertation, we deal with fault-based attacks on cryptographic circuits which belong to the field of active implementation attacks and aim to retrieve secret keys stored on such chips. Our main focus lies on the cryptanalytic aspects of those attacks. In particular, we target block ciphers with a lightweight and (often) non-bijective key schedule where the derived subkeys are (almost) independent from each other. An attacker who is able to reconstruct one of the subkeys is thus not necessarily able to directly retrieve other subkeys or even the secret master key by simply reversing the key schedule. We introduce a framework based on differential fault analysis that allows to attack block ciphers with an arbitrary number of independent subkeys and which rely on a substitution-permutation network. These methods are then applied to the lightweight block ciphers LED and PRINCE and we show in both cases how to recover the secret master key requiring only a small number of fault injections. Moreover, we investigate approaches that utilize algebraic instead of differential techniques for the fault analysis and discuss advantages and drawbacks. At the end of the first part of the dissertation, we explore fault-based attacks on the block cipher Bel-T which also has a lightweight key schedule but is not based on a substitution-permutation network but instead on the so-called Lai-Massey scheme. The framework mentioned above is thus not usable against Bel-T. Nevertheless, we also present techniques for the case of Bel-T that enable full recovery of the secret key in a very efficient way using differential fault analysis. In the second part of the thesis, we focus on authenticated encryption schemes. While regular ciphers only protect privacy of processed data, authenticated encryption schemes also secure its authenticity and integrity. Many of these ciphers are additionally able to protect authenticity and integrity of so-called associated data. This type of data is transmitted unencrypted but nevertheless must be protected from being tampered with during transmission. Authenticated encryption is nowadays the standard technique to protect in-transit data. However, most of the currently deployed schemes have deficits and there are many leverage points for improvements. With NORX we introduce a novel authenticated encryption scheme supporting associated data. This algorithm was designed with high security, efficiency in both hardware and software, simplicity, and robustness against side-channel attacks in mind. Next to its specification, we present special features, security goals, implementation details, extensive performance measurements and discuss advantages over currently deployed standards. Finally, we describe our preliminary security analysis where we investigate differential and rotational properties of NORX. Noteworthy are in particular the newly developed techniques for differential cryptanalysis of NORX which exploit the power of SAT- and SMT-solvers and have the potential to be easily adaptable to other encryption schemes as well.}, subject = {Kryptologie}, language = {en} } @phdthesis{Petit2017, author = {Petit, Albin}, title = {Introducing Privacy in Current Web Search Engines}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-4652}, school = {Universit{\"a}t Passau}, pages = {XVI, 153 S.}, year = {2017}, abstract = {During the last few years, the technological progress in collecting, storing and processing a large quantity of data for a reasonable cost has raised serious privacy issues. Privacy concerns many areas, but is especially important in frequently used services like search engines (e.g., Google, Bing, Yahoo!). These services allow users to retrieve relevant content on the Internet by exploiting their personal data. In this context, developing solutions to enable users to use these services in a privacy-preserving way is becoming increasingly important. In this thesis, we introduce SimAttack an attack against existing protection mechanism to query search engines in a privacy-preserving way. This attack aims at retrieving the original user query. We show with this attack that three representative state-of-the-art solutions do not protect the user privacy in a satisfactory manner. We therefore develop PEAS a new protection mechanism that better protects the user privacy. This solution leverages two types of protection: hiding the user identity (with a succession of two nodes) and masking users' queries (by combining them with several fake queries). To generate realistic fake queries, PEAS exploits previous queries sent by the users in the system. Finally, we present mechanisms to identify sensitive queries. Our goal is to adapt existing protection mechanisms to protect sensitive queries only, and thus save user resources (e.g., CPU, RAM). We design two modules to identify sensitive queries. By deploying these modules on real protection mechanisms, we establish empirically that they dramatically improve the performance of the protection mechanisms.}, subject = {Suchmaschine}, language = {en} } @phdthesis{Braun2015, author = {Braun, Bastian}, title = {Web-based Secure Application Control}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-3048}, school = {Universit{\"a}t Passau}, year = {2015}, abstract = {The world wide web today serves as a distributed application platform. Its origins, however, go back to a simple delivery network for static hypertexts. The legacy from these days can still be observed in the communication protocol used by increasingly sophisticated clients and applications. This thesis identifies the actual security requirements of modern web applications and shows that HTTP does not fit them: user and application authentication, message integrity and confidentiality, control-flow integrity, and application-to-application authorization. We explore the other protocols in the web stack and work out why they can not fill the gap. Our analysis shows that the underlying problem is the connectionless property of HTTP. However, history shows that a fresh start with web communication is far from realistic. As a consequence, we come up with approaches that contribute to meet the identified requirements. We first present impersonation attack vectors that begin before the actual user authentication, i.e. when secure web interaction and authentication seem to be unnecessary. Session fixation attacks exploit a responsibility mismatch between the web developer and the used web application framework. We describe and compare three countermeasures on different implementation levels: on the source code level, on the framework level, and on the network level as a reverse proxy. Then, we explain how the authentication credentials that are transmitted for the user login, i.e. the password, and for session tracking, i.e. the session cookie, can be complemented by browser-stored and user-based secrets respectively. This way, an attacker can not hijack user accounts only by phishing the user's password because an additional browser-based secret is required for login. Also, the class of well-known session hijacking attacks is mitigated because a secret only known by the user must be provided in order to perform critical actions. In the next step, we explore alternative approaches to static authentication credentials. Our approach implements a trusted UI and a mutually authenticated session using signatures as a means to authenticate requests. This way, it establishes a trusted path between the user and the web application without exchanging reusable authentication credentials. As a downside, this approach requires support on the client side and on the server side in order to provide maximum protection. Another approach avoids client-side support but can not implement a trusted UI and is thus susceptible to phishing and clickjacking attacks. Our approaches described so far increase the security level of all web communication at all time. This is why we investigate adaptive security policies that fit the actual risk instead of permanently restricting all kinds of communication including non-critical requests. We develop a smart browser extension that detects when the user is authenticated on a website meaning that she can be impersonated because all requests carry her identity proof. Uncritical communication, however, is released from restrictions to enable all intended web features. Finally, we focus on attacks targeting a web application's control-flow integrity. We explain them thoroughly, check whether current web application frameworks provide means for protection, and implement two approaches to protect web applications: The first approach is an extension for a web application framework and provides protection based on its configuration by checking all requests for policy conformity. The second approach generates its own policies ad hoc based on the observed web traffic and assuming that regular users only click on links and buttons and fill forms but do not craft requests to protected resources.}, subject = {Computersicherheit}, language = {en} } @phdthesis{Garchery2020, author = {Garchery, Mathieu}, title = {User-centered intrusion detection using heterogeneous data}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-8704}, school = {Universit{\"a}t Passau}, pages = {vii, 119 Seiten}, year = {2020}, abstract = {With the frequency and impact of data breaches raising, it has become essential for organizations to automate intrusion detection via machine learning solutions. This generally comes with numerous challenges, among others high class imbalance, changing target concepts and difficulties to conduct sound evaluation. In this thesis, we adopt a user-centered anomaly detection perspective to address selected challenges of intrusion detection, through a real-world use case in the identity and access management (IAM) domain. In addition to the previous challenges, salient properties of this particular problem are high relevance of categorical data, limited feature availability and total absence of ground truth. First, we ask how to apply anomaly detection to IAM audit logs containing a restricted set of mixed (i.e. numeric and categorical) attributes. Then, we inquire how anomalous user behavior can be separated from normality, and this separation evaluated without ground truth. Finally, we examine how the lack of audit data can be alleviated in two complementary settings. On the one hand, we ask how to cope with users without relevant activity history ("cold start" problem). On the other hand, we seek how to extend audit data collection with heterogeneous attributes (i.e. categorical, graph and text) to improve insider threat detection. After aggregating IAM audit data into sessions, we introduce and compare general anomaly detection methods for mixed data to a user identification approach, designed to learn the distinction between normal and malicious user behavior. We find that user identification outperforms general anomaly detection and is effective against masquerades. An additional clustering step allows to reduce false positives among similar users. However, user identification is not effective against insider threats. Furthermore, results suggest that the current scope of our audit data collection should be extended. In order to tackle the "cold start" problem, we adopt a zero-shot learning approach. Focusing on the CERT insider threat use case, we extend an intrusion detection system by integrating user relations to organizational entities (like assignments to projects or teams) in order to better estimate user behavior and improve intrusion detection performance. Results show that this approach is effective in two realistic scenarios. Finally, to support additional sources of audit data for insider threat detection, we propose a method representing audit events as graph edges with heterogeneous attributes. By performing detection at fine-grained level, this approach advantageously improves anomaly traceability while reducing the need for aggregation and feature engineering. Our results show that this method is effective to find intrusions in authentication and email logs. Overall, our work suggests that masquerades and insider threats call for different detection methods. For masquerades, user identification is a promising approach. To find malicious insiders, graph features representing user context and relations to other entities can be informative. This opens the door for tighter coupling of intrusion detection with user identities, roles and privileges used in IAM solutions.}, subject = {Anomalie}, language = {en} } @phdthesis{Koop2021, author = {Koop, Martin}, title = {Preventing the Leakage of Privacy Sensitive User Data on the Web}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-8717}, school = {Universit{\"a}t Passau}, pages = {137 Seiten}, year = {2021}, abstract = {Das Aufzeichnen der Internetaktivit{\"a}t ist mit der Verkn{\"u}pfung pers{\"o}nlicher Daten zu einer Schl{\"u}sselressource f{\"u}r viele kostenpflichtige und kostenfreie Dienste im Web geworden. Diese Dienste sind zum einen Webanwendungen, wie beispielsweise die von Google bereitgestellten Karten/Navigation oder Websuche, die t{\"a}glich kostenlos verwendet werden. Zum anderen sind es alle Webseiten, die meist kostenlos Nachrichten oder allgemeine Informationen zu verschiedenen Themen bereitstellen. Durch das Aufrufen und die Nutzung dieser Webdienste werden alle Informationen, die im Webdienst verarbeitet werden, an den Dienstanbieter weitergeben. Dies umfasst nicht nur die im Benutzerkonto des Webdienstes gespeicherte Profildaten wie Name oder Adresse, sondern auch die Aktivit{\"a}t mit dem Webdienst wie das anklicken von Links oder die Verweildauer. Dar{\"u}ber hinaus gibt es jedoch auch unz{\"a}hlige Drittparteien, welche zumeist im Hintergrund in die Webdienste eingebunden sind und das Benutzerverhalten der kompletten Webaktivit{\"a}t - Webseiten {\"u}bergreifend - mitspeichern sowie auswerten. Der Einsatz verschiedener, in der Regel f{\"u}r den Benutzer verborgener Techniken, dient dazu das Online-Verhalten der Benutzer genau zu verfolgen und viele sensible Daten zu sammeln. Dieses Verhalten wird als Web-Tracking bezeichnet und wird haupts{\"a}chlich von Werbeunternehmen genutzt. Die gesammelten Daten sind oft personenbezogen und eine wertvolle Ressourcen der Unternehmen, um Beispielsweise passend zum Benutzerprofil personalisierte Werbung schalten zu k{\"o}nnen. Mit der Nutzung dieser personenbezogenen Daten entstehen aber auch weitreichendere Auswirkungen, welche sich unter anderem in Preisanpassungen f{\"u}r Benutzer mit speziellen Profilattributen, wie der Nutzung von teuren Endger{\"a}ten, widerspiegeln. Ziel dieser Arbeit ist es die Privatsph{\"a}re der Nutzer im Internet zu steigern und die Nutzerverfolgung von Web-Tracking signifikant zu reduzieren. Dabei stellen sich vier Herausforderungen, die jeweils einen Forschungsschwerpunkt dieser Arbeit bilden: (1) Systematische Analyse und Einordnung eingesetzter Tracking-Techniken, (2) Untersuchung vorhandener Schutzmechanismen und deren Schwachstellen,(3) Konzeption einer Referenzarchitektur zum Schutz vor Web-Tracking und (4) Entwurf einer automatisierten Testumgebungen unter Realbedingungen, um die Reduzierung von Web-Tracking in den entwickelten Schutzmaßnahmen zu untersuchen. Jeder dieser Forschungsschwerpunkte stellt neue Beitr{\"a}ge bereit, um einheitlich das {\"u}bergeordnete Ziel zu erreichen: der Entwicklung von Schutzmaßnahmen gegen die Preisgabe sensibler Benutzerdaten im Internet. Der erste wissenschaftliche Beitrag dieser Dissertation ist eine umfassende Evaluation eingesetzter Web-Tracking Techniken und Methoden, sowie deren Gefahren, Risiken und Implikationen f{\"u}r die Privatsph{\"a}re der Internetnutzer. Die Evaluation beinhaltet zus{\"a}tzlich die Untersuchung vorhandener Tracking-Schutzmechanismen und deren Schwachstellen. Die gewonnenen Erkenntnisse sind maßgeblich f{\"u}r die in dieser Arbeit neu entwickelten Ans{\"a}tze und verbessern den bisherigen nicht hinreichend gew{\"a}hrleisteten Schutz vor Web-Tracking. Der zweite wissenschaftliche Beitrag ist die Entwicklung einer robusten Klassifizierung von Web-Tracking, der Entwurf einer effizienten Architektur zur Langzeituntersuchung von Web-Tracking sowie einer interaktiven Visualisierung des Auftreten von Web-Tracking im Internet. Dabei basiert der neue Klassifizierungsansatz, um Tracking zu identifizieren, auf der Entropie Messung des Informationsgehalts von Cookies. Die Resultate der Web-Tracking Langzeitstudien sind unter anderem 1.209 identifizierte Tracking-Domains auf den meistbesuchten Webseiten in Deutschland. Hierbei wurden innerhalb der Top 25 Webseiten im Durchschnitt 45 Tracking-Elemente pro Webseite gefunden. Der Tracker mit dem h{\"o}chsten Potenzial zum Erstellen eines Benutzerprofils war doubleclick.com, da er 90\% der Webseiten {\"u}berwacht. Die Auswertung des untersuchten Tracking-Netzwerks ergab weiterhin einen detaillierten Einblick in die Tracking-Technik mithilfe von Weiterleitungslinks. Dabei haben wir 1,2 Millionen HTTP-Traces von monatelangen Crawls der 50.000 international meistbesuchten Webseiten analysiert. Die Ergebnisse zeigen, dass 11,6\% dieser Webseiten HTTP-Redirects, verborgen in Webseiten-Links, zum Tracken verwenden. Dies wird eingesetzt, um den Webseitenverlauf des Benutzers nach dem Klick durch eine Kette von (Tracking-)Servern umzuleiten, welche in der Regel nicht sichtbar sind, bevor das beabsichtigte Link-Ziel geladen wird. In diesem Szenario erfasst der Tracker wertvolle Verbindungs-Metadaten zu Inhalt, Thema oder Benutzerinteressen der Website. Die Visualisierung des Tracking {\"O}kosystem stellen wir in einem interaktiven Open-Source Web-Tool bereit. Der dritte wissenschaftliche Beitrag dieser Dissertation ist die Konzeption von zwei neuartigen Schutzmechanismen gegen Web-Tracking und der Aufbau einer automatisierten Simulationsumgebung unter Realbedingungen, um die Effektivit{\"a}t der Umsetzungen zu verifizieren. Der Fokus liegt auf den beiden meist verwendeten Tracking-Verfahren: Cookies (hierbei wird eine eindeutigen ID auf dem Ger{\"a}t des Benutzers gespeichert), sowie Browser-Fingerprinting. Letzteres beschreibt eine Methode zum Sammeln einer Vielzahl an Ger{\"a}teeigenschaften, um den Benutzer eindeutig zu (re- )identifizieren, ohne eine eindeutige ID auf dem Ger{\"a}t zu speichern. Um die Effektivit{\"a}t der in dieser Arbeit entwickelten Schutzmechanismen vor Web-Tracking zu untersuchen, implementierten und evaluierten wir die Schutzkonzepte direkt im Chromium Browser. Das Ergebnis zeigt eine erfolgreiche Reduzierung von Web-Tracking um 44\%. Zus{\"a}tzlich verbessert das in dieser Arbeit entwickelte Konzept "Site Isolation" den Datenschutz des privaten Browsing-Modus, erm{\"o}glicht das Setzen eines manuellen Speicher-Zeitlimits von Cookies und sch{\"u}tzt den Browser gegen verschiedene Bedrohungen wie CSRF (Cross-Site Request Forgery) oder CORS (Cross-Origin Ressource Sharing). Site Isolation speichert dabei den Status der lokalen Website in separaten Containern und kann dadurch diverse Tracking-Methoden wie Cookies, lokalStorage oder redirect tracking verhindern. Bei der Auswertung von 1,6 Millionen Webseiten haben wir gezeigt, dass der Tracker doubleclick.com das h{\"o}chste Potenzial besitzt, den Nutzer zu verfolgen und auf 25\% der 40.000 international meistbesuchten Webseiten vertreten ist. Schließlich demonstrieren wir in unserem erweiterten Chromium-Browser einen robusten Browser-Fingerprinting-Schutz. Der Test unseres Prototyps mittels 70.000 Browsersitzungen zeigt, dass unser Browser den Nutzer vor sogenanntem Browser-Fingerprinting Tracking sch{\"u}tzt. Im Vergleich zu f{\"u}nf anderen Browser-Fingerprint-Tools erzielte unser Prototyp die besten Ergebnisse und ist der erste Schutzmechanismus gegen Flash sowie Canvas Fingerprinting.}, subject = {Datenschutz}, language = {en} } @phdthesis{Johns2009, author = {Johns, Martin}, title = {Code Injection Vulnerabilities in Web Applications - Exemplified at Cross-site Scripting}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus-23626}, school = {Universit{\"a}t Passau}, year = {2009}, abstract = {The majority of all security problems in today's Web applications is caused by string-based code injection, with Cross-site Scripting (XSS)being the dominant representative of this vulnerability class. This thesis discusses XSS and suggests defense mechanisms. We do so in three stages: First, we conduct a thorough analysis of JavaScript's capabilities and explain how these capabilities are utilized in XSS attacks. We subsequently design a systematic, hierarchical classification of XSS payloads. In addition, we present a comprehensive survey of publicly documented XSS payloads which is structured according to our proposed classification scheme. Secondly, we explore defensive mechanisms which dynamically prevent the execution of some payload types without eliminating the actual vulnerability. More specifically, we discuss the design and implementation of countermeasures against the XSS payloads Session Hijacking'', Cross-site Request Forgery'', and attacks that target intranet resources. We build upon this and introduce a general methodology for developing such countermeasures: We determine a necessary set of basic capabilities an adversary needs for successfully executing an attack through an analysis of the targeted payload type. The resulting countermeasure relies on revoking one of these capabilities, which in turn renders the payload infeasible. Finally, we present two language-based approaches that prevent XSS and related vulnerabilities: We identify the implicit mixing of data and code during string-based syntax assembly as the root cause of string-based code injection attacks. Consequently, we explore data/code separation in web applications. For this purpose, we propose a novel methodology for token-level data/code partitioning of a computer language's syntactical elements. This forms the basis for our two distinct techniques: For one, we present an approach to detect data/code confusion on run-time and demonstrate how this can be used for attack prevention. Furthermore, we show how vulnerabilities can be avoided through altering the underlying programming language. We introduce a dedicated datatype for syntax assembly instead of using string datatypes themselves for this purpose. We develop a formal, type-theoretical model of the proposed datatype and proof that it provides reliable separation between data and code hence, preventing code injection vulnerabilities. We verify our approach's applicability utilizing a practical implementation for the J2EE application server.}, subject = {Computersicherheit}, language = {en} } @phdthesis{ParraRodriguez2019, author = {Parra Rodriguez, Juan David}, title = {Computational Resource Abuse in Web Applications}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-7706}, school = {Universit{\"a}t Passau}, pages = {xi, 158 Seiten}, year = {2019}, abstract = {Internet browsers include Application Programming Interfaces (APIs) to support Web applications that require complex functionality, e.g., to let end users watch videos, make phone calls, and play video games. Meanwhile, many Web applications employ the browser APIs to rely on the user's hardware to execute intensive computation, access the Graphics Processing Unit (GPU), use persistent storage, and establish network connections. However, providing access to the system's computational resources, i.e., processing, storage, and networking, through the browser creates an opportunity for attackers to abuse resources. Principally, the problem occurs when an attacker compromises a Web site and includes malicious code to abuse its visitor's computational resources. For example, an attacker can abuse the user's system networking capabilities to perform a Denial of Service (DoS) attack against third parties. What is more, computational resource abuse has not received widespread attention from the Web security community because most of the current specifications are focused on content and session properties such as isolation, confidentiality, and integrity. Our primary goal is to study computational resource abuse and to advance the state of the art by providing a general attacker model, multiple case studies, a thorough analysis of available security mechanisms, and a new detection mechanism. To this end, we implemented and evaluated three scenarios where attackers use multiple browser APIs to abuse networking, local storage, and computation. Further, depending on the scenario, an attacker can use browsers to perform Denial of Service against third-party Web sites, create a network of browsers to store and distribute arbitrary data, or use browsers to establish anonymous connections similarly to The Onion Router (Tor). Our analysis also includes a real-life resource abuse case found in the wild, i.e., CryptoJacking, where thousands of Web sites forced their visitors to perform crypto-currency mining without their consent. In the general case, attacks presented in this thesis share the attacker model and two key characteristics: 1) the browser's end user remains oblivious to the attack, and 2) an attacker has to invest little resources in comparison to the resources he obtains. In addition to the attack's analysis, we present how existing, and upcoming, security enforcement mechanisms from Web security can hinder an attacker and their drawbacks. Moreover, we propose a novel detection approach based on browser API usage patterns. Finally, we evaluate the accuracy of our detection model, after training it with the real-life crypto-mining scenario, through a large scale analysis of the most popular Web sites.}, subject = {Computersicherheit}, language = {en} } @phdthesis{Jurgovsky2019, author = {Jurgovsky, Johannes}, title = {Context-Aware Credit Card Fraud Detection}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-7622}, school = {Universit{\"a}t Passau}, pages = {xvii, 160 Seiten}, year = {2019}, abstract = {Credit card fraud has emerged as major problem in the electronic payment sector. In this thesis, we study data-driven fraud detection and address several of its intricate challenges by means of machine learning methods with the goal to identify fraudulent transactions that have been issued illegitimately on behalf of the rightful card owner. In particular, we explore several means to leverage contextual information beyond a transaction's basic attributes on the transaction level, sequence level and user level. On the transaction level, we aim to identify fraudulent transactions which, in terms of their attribute values, are globally distinguishable from genuine transactions. We provide an empirical study of the influence of class imbalance and forecasting horizons on the classification performance of a random forest classifier. We augment transactions with additional features extracted from external knowledge sources and show that external information about countries and calendar events improves classification performance most noticeably on card-not-present transactions. On the sequence level, we aim to detect frauds that are inconspicuous in the background of all transactions but peculiar with respect to the short-term sequence they appear in. We use a Long Short-term Memory network (LSTM) for modeling the sequential succession of transactions. Our results suggest that LSTM-based modeling is a promising strategy for characterizing sequences of card-present transactions but it is not adequate for card-not-present transactions. On the user level, we elaborate on feature aggregations and propose a flexible concept allowing us define numerous features by means of a simple syntax. We provide a CUDA-based implementation for the computationally expensive extraction with a speed-up of two orders of magnitude over a single-core implementation. Our feature selection study reveals that aggregates extracted from users' transaction sequences are more useful than those extracted from merchant sequences. Moreover, we discover multiple sets of candidate features with equivalent performance as manually engineered aggregates while being structurally different. Regarding future work, we motivate the usage of simple and transparent machine learning methods for credit card fraud detection and we sketch a simple user-focused modeling approach.}, subject = {Kreditkartenmissbrauch}, language = {en} }