Scalable Techniques for Anomaly Detection

Computer networks are constantly being attacked by malicious entities for various reasons. Network based attacks include but are not limited to, Distributed Denial of Service (DDoS), DNS based attacks, Cross-site Scripting (XSS) etc. Such attacks have exploited either the network protocol or the end-host software vulnerabilities for perpetration. Current network traffic analysis techniques employed for detection and/or prevention of these anomalies suffer from significant delay or have only limited scalability because of their huge resource requirements. This dissertation proposes more scalable techniques for network anomaly detection. We propose using DNS analysis for detecting a wide variety of network anomalies. The use of DNS is motivated by the fact that DNS traffic comprises only 2-3% of total network traffic reducing the burden on anomaly detection resources. Our motivation additionally follows from the observation that almost any Internet activity (legitimate or otherwise) is marked by the use of DNS. We propose several techniques for DNS traffic analysis to distinguish anomalous DNS traffic patterns which in turn identify different categories of network attacks. First, we present MiND, a system to detect misdirected DNS packets arising due to poisoned name server records or due to local infections such as caused by worms like DNSChanger. MiND validates misdirected DNS packets using an externally collected database of authoritative name servers for second or third-level domains. We deploy this tool at the edge of a university campus network for evaluation. Secondly, we focus on domain-fluxing botnet detection by exploiting the high entropy inherent in the set of domains used for locating the Command and Control (C&C) server. We apply three metrics namely the Kullback-Leibler divergence, the Jaccard Index, and the Edit distance, to different groups of domain names present in Tier-1 ISP DNS traces obtained from South Asia and South America. Our evaluation successfully detects existing domain-fluxing botnets such as Conficker and also recognizes new botnets. We extend this approach by utilizing DNS failures to improve the latency of detection. Alternatively, we propose a system which uses temporal and entropy-based correlation between successful and failed DNS queries, for fluxing botnet detection. We also present an approach which computes the reputation of domains in a bipartite graph of hosts within a network, and the domains accessed by them. The inference technique utilizes belief propagation, an approximation algorithm for marginal probability estimation. The computation of reputation scores is seeded through a small fraction of domains found in black and white lists. An application of this technique, on an HTTP-proxy dataset from a large enterprise, shows a high detection rate with low false positive rates

Using Context to Improve Network-based Exploit Kit Detection

Today, our computers are routinely compromised while performing seemingly innocuous activities like reading articles on trusted websites (e.g., the NY Times). These compromises are perpetrated via complex interactions involving the advertising networks that monetize these sites. Web-based compromises such as exploit kits are similar to any other scam -- the attacker wants to lure an unsuspecting client into a trap to steal private information, or resources -- generating 10s of millions of dollars annually. Exploit kits are web-based services specifically designed to capitalize on vulnerabilities in unsuspecting client computers in order to install malware without a user's knowledge. Sadly, it only takes a single successful infection to ruin a user's financial life, or lead to corporate breaches that result in millions of dollars of expense and loss of customer trust. Exploit kits use a myriad of techniques to obfuscate each attack instance, making current network-based defenses such as signature-based network intrusion detection systems far less effective than in years past. Dynamic analysis or honeyclient analysis on these exploits plays a key role in identifying new attacks for signature generation, but provides no means of inspecting end-user traffic on the network to identify attacks in real time. As a result, defenses designed to stop such malfeasance often arrive too late or not at all resulting in high false positive and false negative (error) rates. In order to deal with these drawbacks, three new detection approaches are presented. To deal with the issue of a high number of errors, a new technique for detecting exploit kit interactions on a network is proposed. The technique capitalizes on the fact that an exploit kit leads its potential victim through a process of exploitation by forcing the browser to download multiple web resources from malicious servers. This process has an inherent structure that can be captured in HTTP traffic and used to significantly reduce error rates. The approach organizes HTTP traffic into tree-like data structures, and, using a scalable index of exploit kit traces as samples, models the detection process as a subtree similarity search problem. The technique is evaluated on 3,800 hours of web traffic on a large enterprise network, and results show that it reduces false positive rates by four orders of magnitude over current state-of-the-art approaches. While utilizing structure can vastly improve detection rates over current approaches, it does not go far enough in helping defenders detect new, previously unseen attacks. As a result, a new framework that applies dynamic honeyclient analysis directly on network traffic at scale is proposed. The framework captures and stores a configurable window of reassembled HTTP objects network wide, uses lightweight content rendering to establish the chain of requests leading up to a suspicious event, then serves the initial response content back to the honeyclient in an isolated network. The framework is evaluated on a diverse collection of exploit kits as they evolve over a 1 year period. The empirical evaluation suggests that the approach offers significant operational value, and a single honeyclient can support a campus deployment of thousands of users. While the above approaches attempt to detect exploit kits before they have a chance to infect the client, they cannot protect a client that has already been infected. The final technique detects signs of post infection behavior by intrusions that abuses the domain name system (DNS) to make contact with an attacker. Contemporary detection approaches utilize the structure of a domain name and require hundreds of DNS messages to detect such malware. As a result, these detection mechanisms cannot detect malware in a timely manner and are susceptible to high error rates. The final technique, based on sequential hypothesis testing, uses the DNS message patterns of a subset of DNS traffic to detect malware in as little as four DNS messages, and with orders of magnitude reduction in error rates. The results of this work can make a significant operational impact on network security analysis, and open several exciting future directions for network security research.Doctor of Philosoph

Modélisation formelle des systèmes de détection d'intrusions

L’écosystème de la cybersécurité évolue en permanence en termes du nombre, de la diversité, et de la complexité des attaques. De ce fait, les outils de détection deviennent inefficaces face à certaines attaques. On distingue généralement trois types de systèmes de détection d’intrusions : détection par anomalies, détection par signatures et détection hybride. La détection par anomalies est fondée sur la caractérisation du comportement habituel du système, typiquement de manière statistique. Elle permet de détecter des attaques connues ou inconnues, mais génère aussi un très grand nombre de faux positifs. La détection par signatures permet de détecter des attaques connues en définissant des règles qui décrivent le comportement connu d’un attaquant. Cela demande une bonne connaissance du comportement de l’attaquant. La détection hybride repose sur plusieurs méthodes de détection incluant celles sus-citées. Elle présente l’avantage d’être plus précise pendant la détection. Des outils tels que Snort et Zeek offrent des langages de bas niveau pour l’expression de règles de reconnaissance d’attaques. Le nombre d’attaques potentielles étant très grand, ces bases de règles deviennent rapidement difficiles à gérer et à maintenir. De plus, l’expression de règles avec état dit stateful est particulièrement ardue pour reconnaître une séquence d’événements. Dans cette thèse, nous proposons une approche stateful basée sur les diagrammes d’état-transition algébriques (ASTDs) afin d’identifier des attaques complexes. Les ASTDs permettent de représenter de façon graphique et modulaire une spécification, ce qui facilite la maintenance et la compréhension des règles. Nous étendons la notation ASTD avec de nouvelles fonctionnalités pour représenter des attaques complexes. Ensuite, nous spécifions plusieurs attaques avec la notation étendue et exécutons les spécifications obtenues sur des flots d’événements à l’aide d’un interpréteur pour identifier des attaques. Nous évaluons aussi les performances de l’interpréteur avec des outils industriels tels que Snort et Zeek. Puis, nous réalisons un compilateur afin de générer du code exécutable à partir d’une spécification ASTD, capable d’identifier de façon efficiente les séquences d’événements.Abstract : The cybersecurity ecosystem continuously evolves with the number, the diversity, and the complexity of cyber attacks. Generally, we have three types of Intrusion Detection System (IDS) : anomaly-based detection, signature-based detection, and hybrid detection. Anomaly detection is based on the usual behavior description of the system, typically in a static manner. It enables detecting known or unknown attacks but also generating a large number of false positives. Signature based detection enables detecting known attacks by defining rules that describe known attacker’s behavior. It needs a good knowledge of attacker behavior. Hybrid detection relies on several detection methods including the previous ones. It has the advantage of being more precise during detection. Tools like Snort and Zeek offer low level languages to represent rules for detecting attacks. The number of potential attacks being large, these rule bases become quickly hard to manage and maintain. Moreover, the representation of stateful rules to recognize a sequence of events is particularly arduous. In this thesis, we propose a stateful approach based on algebraic state-transition diagrams (ASTDs) to identify complex attacks. ASTDs allow a graphical and modular representation of a specification, that facilitates maintenance and understanding of rules. We extend the ASTD notation with new features to represent complex attacks. Next, we specify several attacks with the extended notation and run the resulting specifications on event streams using an interpreter to identify attacks. We also evaluate the performance of the interpreter with industrial tools such as Snort and Zeek. Then, we build a compiler in order to generate executable code from an ASTD specification, able to efficiently identify sequences of events

Challenges and Open Questions of Machine Learning in Computer Security

This habilitation thesis presents advancements in machine learning for computer security, arising from problems in network intrusion detection and steganography. The thesis put an emphasis on explanation of traits shared by steganalysis, network intrusion detection, and other security domains, which makes these domains different from computer vision, speech recognition, and other fields where machine learning is typically studied. Then, the thesis presents methods developed to at least partially solve the identified problems with an overall goal to make machine learning based intrusion detection system viable. Most of them are general in the sense that they can be used outside intrusion detection and steganalysis on problems with similar constraints. A common feature of all methods is that they are generally simple, yet surprisingly effective. According to large-scale experiments they almost always improve the prior art, which is likely caused by being tailored to security problems and designed for large volumes of data. Specifically, the thesis addresses following problems: anomaly detection with low computational and memory complexity such that efficient processing of large data is possible; multiple-instance anomaly detection improving signal-to-noise ration by classifying larger group of samples; supervised classification of tree-structured data simplifying their encoding in neural networks; clustering of structured data; supervised training with the emphasis on the precision in top p% of returned data; and finally explanation of anomalies to help humans understand the nature of anomaly and speed-up their decision. Many algorithms and method presented in this thesis are deployed in the real intrusion detection system protecting millions of computers around the globe

Identification and Recognition of Remote-Controlled Malware

This thesis encapsulates research on the detection of botnets. First, we design and implement Sandnet, an observation and monitoring infrastructure to study the botnet phenomenon. Using Sandnet, we evaluate detection approaches based on traffic analysis and rogue visual monetization. Therefore, we identify and recognize botnet C&C channels by help of traffic analysis. To a large degree, our clustering and classification leverage the sequence of message lengths per flow. As a result, our implementation, CoCoSpot, proves to reliably detect active C&C communication of a variety of botnet families, even in face of fully encrypted C&C messages. Furthermore, we found a botnet that uses DNS as carrier protocol for its command and control channel. By help of statistical entropy as well as behavioral features, we design and implement a classifier that detects DNS-based C&C, even in mixed network traffic of benign users. Finally, perceptual clustering of Sandnet screenshots enables us to group malware into rogue visual monetization campaigns and study their monetization properties

Effiziente und erklärbare Erkennung von mobiler Schadsoftware mittels maschineller Lernmethoden

In recent years, mobile devices shipped with Google’s Android operating system have become ubiquitous. Due to their popularity and the high concentration of sensitive user data on these devices, however, they have also become a profitable target of malware authors. As a result, thousands of new malware instances targeting Android are found almost every day. Unfortunately, common signature-based methods often fail to detect these applications, as these methods can- not keep pace with the rapid development of new malware. Consequently, there is an urgent need for new malware detection methods to tackle this growing threat. In this thesis, we address the problem by combining concepts of static analysis and machine learning, such that mobile malware can be detected directly on the mobile device with low run-time overhead. To this end, we first discuss our analysis results of a sophisticated malware that uses an ultrasonic side channel to spy on unwitting smartphone users. Based on the insights we gain throughout this thesis, we gradually develop a method that allows detecting Android malware in general. The resulting method performs a broad static analysis, gathering a large number of features associated with an application. These features are embedded in a joint vector space, where typical patterns indicative of malware can be automatically identified and used for explaining the decisions of our method. In addition to an evaluation of its overall detection and run-time performance, we also examine the interpretability of the underlying detection model and strengthen the classifier against realistic evasion attacks. In a large set of experiments, we show that the method clearly outperforms several related approaches, including popular anti-virus scanners. In most experiments, our approach detects more than 90% of all malicious samples in the dataset at a low false positive rate of only 1%. Furthermore, even on older devices, it offers a good run-time performance, and can output a decision along with a proper explanation within a few seconds, despite the use of machine learning techniques directly on the mobile device. Overall, we find that the application of machine learning techniques is a promising research direction to improve the security of mobile devices. While these techniques alone cannot defeat the threat of mobile malware, they at least raise the bar for malicious actors significantly, especially if combined with existing techniques.Die Verbreitung von Smartphones, insbesondere mit dem Android-Betriebssystem, hat in den vergangenen Jahren stark zugenommen. Aufgrund ihrer hohen Popularität haben sich diese Geräte jedoch zugleich auch zu einem lukrativen Ziel für Entwickler von Schadsoftware entwickelt, weshalb mittlerweile täglich neue Schadprogramme für Android gefunden werden. Obwohl verschiedene Lösungen existieren, die Schadprogramme auch auf mobilen Endgeräten identifizieren sollen, bieten diese in der Praxis häufig keinen ausreichenden Schutz. Dies liegt vor allem daran, dass diese Verfahren zumeist signaturbasiert arbeiten und somit schädliche Programme erst zuverlässig identifizieren können, sobald entsprechende Erkennungssignaturen vorhanden sind. Jedoch wird es für Antiviren-Hersteller immer schwieriger, die zur Erkennung notwendigen Signaturen rechtzeitig bereitzustellen. Daher ist die Entwicklung von neuen Verfahren nötig, um der wachsenden Bedrohung durch mobile Schadsoftware besser begegnen zu können. In dieser Dissertation wird ein Verfahren vorgestellt und eingehend untersucht, das Techniken der statischen Code-Analyse mit Methoden des maschinellen Lernens kombiniert, um so eine zuverlässige Erkennung von mobiler Schadsoftware direkt auf dem Mobilgerät zu ermöglichen. Die Methode analysiert hierfür mobile Anwendungen zunächst statisch und extrahiert dabei spezielle Merkmale, die eine Abbildung einer Applikation in einen hochdimensionalen Vektorraum ermöglichen. In diesem Vektorraum sind schließlich maschinelle Lernmethoden in der Lage, automatisch Muster zur Erkennung von Schadprogrammen zu finden. Die gefundenen Muster können dabei nicht nur zur Erkennung, sondern darüber hinaus auch zur Erklärung einer getroffenenen Entscheidung dienen. Im Rahmen einer ausführlichen Evaluation wird nicht nur die Erkennungsleistung und die Laufzeit der vorgestellten Methode untersucht, sondern darüber hinaus das gelernte Erkennungsmodell im Detail analysiert. Hierbei wird auch die Robustheit des Modells gegenüber gezielten Angriffe untersucht und verbessert. In einer Reihe von Experimenten kann gezeigt werden, dass mit dem vorgeschlagenen Verfahren bessere Ergebnisse erzielt werden können als mit vergleichbaren Methoden, sogar einschließlich einiger populärer Antivirenprogramme. In den meisten Experimenten kann die Methode Schadprogramme zuverlässig erkennen und erreicht Erkennungsraten von über 90% bei einer geringen Falsch-Positiv-Rate von 1%

Studying JavaScript Security Through Static Analysis

Mit dem stetigen Wachstum des Internets wächst auch das Interesse von Angreifern. Ursprünglich sollte das Internet Menschen verbinden; gleichzeitig benutzen aber Angreifer diese Vernetzung, um Schadprogramme wirksam zu verbreiten. Insbesondere JavaScript ist zu einem beliebten Angriffsvektor geworden, da es Angreifer ermöglicht Bugs und weitere Sicherheitslücken auszunutzen, und somit die Sicherheit und Privatsphäre der Internetnutzern zu gefährden. In dieser Dissertation fokussieren wir uns auf die Erkennung solcher Bedrohungen, indem wir JavaScript Code statisch und effizient analysieren. Zunächst beschreiben wir unsere zwei Detektoren, welche Methoden des maschinellen Lernens mit statischen Features aus Syntax, Kontroll- und Datenflüssen kombinieren zur Erkennung bösartiger JavaScript Dateien. Wir evaluieren daraufhin die Verlässlichkeit solcher statischen Systeme, indem wir bösartige JavaScript Dokumente umschreiben, damit sie die syntaktische Struktur von bestehenden gutartigen Skripten reproduzieren. Zuletzt studieren wir die Sicherheit von Browser Extensions. Zu diesem Zweck modellieren wir Extensions mit einem Graph, welcher Kontroll-, Daten-, und Nachrichtenflüsse mit Pointer Analysen kombiniert, wodurch wir externe Flüsse aus und zu kritischen Extension-Funktionen erkennen können. Insgesamt wiesen wir 184 verwundbare Chrome Extensions nach, welche die Angreifer ausnutzen könnten, um beispielsweise beliebigen Code im Browser eines Opfers auszuführen.As the Internet keeps on growing, so does the interest of malicious actors. While the Internet has become widespread and popular to interconnect billions of people, this interconnectivity also simplifies the spread of malicious software. Specifically, JavaScript has become a popular attack vector, as it enables to stealthily exploit bugs and further vulnerabilities to compromise the security and privacy of Internet users. In this thesis, we approach these issues by proposing several systems to statically analyze real-world JavaScript code at scale. First, we focus on the detection of malicious JavaScript samples. To this end, we propose two learning-based pipelines, which leverage syntactic, control and data-flow based features to distinguish benign from malicious inputs. Subsequently, we evaluate the robustness of such static malicious JavaScript detectors in an adversarial setting. For this purpose, we introduce a generic camouflage attack, which consists in rewriting malicious samples to reproduce existing benign syntactic structures. Finally, we consider vulnerable browser extensions. In particular, we abstract an extension source code at a semantic level, including control, data, and message flows, and pointer analysis, to detect suspicious data flows from and toward an extension privileged context. Overall, we report on 184 Chrome extensions that attackers could exploit to, e.g., execute arbitrary code in a victim's browser