SybilBelief: A Semi-supervised Learning Approach for Structure-based Sybil Detection

Sybil attacks are a fundamental threat to the security of distributed systems. Recently, there has been a growing interest in leveraging social networks to mitigate Sybil attacks. However, the existing approaches suffer from one or more drawbacks, including bootstrapping from either only known benign or known Sybil nodes, failing to tolerate noise in their prior knowledge about known benign or Sybil nodes, and being not scalable. In this work, we aim to overcome these drawbacks. Towards this goal, we introduce SybilBelief, a semi-supervised learning framework, to detect Sybil nodes. SybilBelief takes a social network of the nodes in the system, a small set of known benign nodes, and, optionally, a small set of known Sybils as input. Then SybilBelief propagates the label information from the known benign and/or Sybil nodes to the remaining nodes in the system. We evaluate SybilBelief using both synthetic and real world social network topologies. We show that SybilBelief is able to accurately identify Sybil nodes with low false positive rates and low false negative rates. SybilBelief is resilient to noise in our prior knowledge about known benign and Sybil nodes. Moreover, SybilBelief performs orders of magnitudes better than existing Sybil classification mechanisms and significantly better than existing Sybil ranking mechanisms.Comment: 12 page

Risk assessment in centralized and decentralized online social network.

One of the main concerns in centralized and decentralized OSNs is related to the fact that OSNs users establish new relationships with unknown people with the result of exposing a huge amount of personal data. This can attract the variety of attackers that try to propagate malwares and malicious items in the network to misuse the personal information of users. Therefore, there have been several research studies to detect specific kinds of attacks by focusing on the topology of the graph [159, 158, 32, 148, 157]. On the other hand, there are several solutions to detect specific kinds of attackers based on the behavior of users. But, most of these approaches either focus on just the topology of the graph [159, 158] or the detection of anomalous users by exploiting supervised learning techniques [157, 47, 86, 125]. However, we have to note that the main issue of supervised learning is that they are not able to detect new attacker's behaviors, since the classifier is trained based on the known behavioral patterns. Literature also offers approaches to detect anomalous users in OSNs that use unsupervised learning approaches [150, 153, 36, 146] or a combination of supervised and unsupervised techniques [153]. But, existing attack defenses are designed to cope with just one specific type of attack. Although several solutions to detect specific kinds of attacks have been recently proposed, there is no general solution to cope with the main privacy/security attacks in OSNs. In such a scenario, it would be very beneficial to have a solution that can cope with the main privacy/security attacks that can be perpetrated using the social network graph. Our main contribution is proposing a unique unsupervised approach that helps OSNs providers and users to have a global understanding of risky users and detect them. We believe that the core of such a solution is a mechanism able to assign a risk score to each OSNs account. Over the last three years, we have done significant research efforts in analyzing user's behavior to detect risky users included some kinds of well known attacks in centralized and decentralized online social networks. Our research started by proposing a risk assessment approach based on the idea that the more a user behavior diverges from normal behavior, the more it should be considered risky. In our proposed approach, we monitor and analyze the combination of interaction or activity patterns and friendship patterns of users and build the risk estimation model in order to detect and identify those risky users who follow the behavioral patterns of attackers. Since, users in OSNs follow different behavioral patterns, it is not possible to define a unique standard behavioral model that fits all OSNs users' behaviors. Towards this goal, we propose a two-phase risk assessment approach by grouping users in the first phase to find similar users that share the same behavioral patterns and, then in the second phase, for each identified group, building some normal behavior models and compute for each user the level of divergency from these normal behaviors. Then, we extend this approach for Decentralized Online Social Networks (i.e., DOSNs). In the following of this approach, we propose a solution in defining a risk measure to help users in OSNs to judge their direct contacts so as to avoid friendship with malicious users. Finally, we monitor dynamically the friendship patterns of users in a large social graph over time for any anomalous changes reflecting attacker's behaviors. In this thesis, we will describe all the solutions that we proposed

Towards trustworthy social computing systems

The rising popularity of social computing systems has managed to attract rampant forms of service abuse that negatively affects the sustainability of these systems and degrades the quality of service experienced by their users. The main factor that enables service abuse is the weak identity infrastructure used by most sites, where identities are easy to create with no verification by a trusted authority. Attackers are exploiting this infrastructure to launch Sybil attacks, where they create multiple fake (Sybil) identities to take advantage of the combined privileges associated with the identities to abuse the system. In this thesis, we present techniques to mitigate service abuse by designing and building defense schemes that are robust and practical. We use two broad defense strategies: (1) Leveraging the social network: We first analyze existing social network-based Sybil detection schemes and present their practical limitations when applied on real world social networks. Next, we present an approach called Sybil Tolerance that bounds the impact an attacker can gain from using multiple identities; (2) Leveraging activity history of identities: We present two approaches, one that applies anomaly detection on user social behavior to detect individual misbehaving identities, and a second approach called Stamper that focuses on detecting a group of Sybil identities. We show that both approaches in this category raise the bar for defense against adaptive attackers.Die steigende Popularität sozialer Medien führt zu umfangreichen Missbrauch mit negativen Folgen für die nachhaltige Funktionalität und verringerter Qualität des Services. Der Missbrauch wird maßgeblich durch die Nutzung schwacher Identifikationsverfahren, die eine einfache Anmeldung ohne Verifikation durch eine vertrauenswürdige Behörde erlaubt, ermöglicht. Angreifer nutzen diese Umgebung aus und attackieren den Service mit sogenannten Sybil Angriffen, bei denen mehrere gefälschte (Sybil) Identitäten erstellt werden, um einen Vorteil durch die gemeinsamen Privilegien der Identitäten zu erhalten und den Service zu missbrauchen. Diese Doktorarbeit zeigt Techniken zur Verhinderung von Missbrauch sozialer Medien, in dem Verteidigungsmechanismen konstruiert und implementiert werden, die sowohl robust als auch praktikabel sind. Zwei Verteidigungsstrategien werden vorgestellt: (1) Unter Ausnutzung des sozialen Netzwerks: Wir analysieren zuerst existierende soziale Netzwerk-basierende Sybil Erkennungsmechanismen und zeigen deren praktische Anwendungsgrenzen auf bei der Anwendung auf soziale Netzwerke aus der echten Welt. Im Anschluss zeigen wir den Ansatz der sogenannten Sybil Toleranz, welcher die Folgen eines Angriffs mit mehreren Identitäten einschränkt. (2) Unter Ausnutzung des Aktivitätsverlaufs von Identitäten: Wir präsentieren zwei Ansätze, einen anwendbar für die Erkennung von Unregelmäßigkeiten in dem sozialen Verhalten eines Benutzers zur Erkennung unanständiger Benutzer und ein weiterer Ansatz namens Stamper, dessen Fokus die Erkennung von Gruppen bestehend aus Sybil Identitäten ist. Beide gezeigten Ansätze erschweren adaptive Angriffe und verbessern existierende Verteidigungsmechanismen

Towards trustworthy social computing systems

The rising popularity of social computing systems has managed to attract rampant forms of service abuse that negatively affects the sustainability of these systems and degrades the quality of service experienced by their users. The main factor that enables service abuse is the weak identity infrastructure used by most sites, where identities are easy to create with no verification by a trusted authority. Attackers are exploiting this infrastructure to launch Sybil attacks, where they create multiple fake (Sybil) identities to take advantage of the combined privileges associated with the identities to abuse the system. In this thesis, we present techniques to mitigate service abuse by designing and building defense schemes that are robust and practical. We use two broad defense strategies: (1) Leveraging the social network: We first analyze existing social network-based Sybil detection schemes and present their practical limitations when applied on real world social networks. Next, we present an approach called Sybil Tolerance that bounds the impact an attacker can gain from using multiple identities; (2) Leveraging activity history of identities: We present two approaches, one that applies anomaly detection on user social behavior to detect individual misbehaving identities, and a second approach called Stamper that focuses on detecting a group of Sybil identities. We show that both approaches in this category raise the bar for defense against adaptive attackers.Die steigende Popularität sozialer Medien führt zu umfangreichen Missbrauch mit negativen Folgen für die nachhaltige Funktionalität und verringerter Qualität des Services. Der Missbrauch wird maßgeblich durch die Nutzung schwacher Identifikationsverfahren, die eine einfache Anmeldung ohne Verifikation durch eine vertrauenswürdige Behörde erlaubt, ermöglicht. Angreifer nutzen diese Umgebung aus und attackieren den Service mit sogenannten Sybil Angriffen, bei denen mehrere gefälschte (Sybil) Identitäten erstellt werden, um einen Vorteil durch die gemeinsamen Privilegien der Identitäten zu erhalten und den Service zu missbrauchen. Diese Doktorarbeit zeigt Techniken zur Verhinderung von Missbrauch sozialer Medien, in dem Verteidigungsmechanismen konstruiert und implementiert werden, die sowohl robust als auch praktikabel sind. Zwei Verteidigungsstrategien werden vorgestellt: (1) Unter Ausnutzung des sozialen Netzwerks: Wir analysieren zuerst existierende soziale Netzwerk-basierende Sybil Erkennungsmechanismen und zeigen deren praktische Anwendungsgrenzen auf bei der Anwendung auf soziale Netzwerke aus der echten Welt. Im Anschluss zeigen wir den Ansatz der sogenannten Sybil Toleranz, welcher die Folgen eines Angriffs mit mehreren Identitäten einschränkt. (2) Unter Ausnutzung des Aktivitätsverlaufs von Identitäten: Wir präsentieren zwei Ansätze, einen anwendbar für die Erkennung von Unregelmäßigkeiten in dem sozialen Verhalten eines Benutzers zur Erkennung unanständiger Benutzer und ein weiterer Ansatz namens Stamper, dessen Fokus die Erkennung von Gruppen bestehend aus Sybil Identitäten ist. Beide gezeigten Ansätze erschweren adaptive Angriffe und verbessern existierende Verteidigungsmechanismen

Risk assessment in centralized and decentralized online social network.

One of the main concerns in centralized and decentralized OSNs is related to the fact that OSNs users establish new relationships with unknown people with the result of exposing a huge amount of personal data. This can attract the variety of attackers that try to propagate malwares and malicious items in the network to misuse the personal information of users. Therefore, there have been several research studies to detect specific kinds of attacks by focusing on the topology of the graph [159, 158, 32, 148, 157]. On the other hand, there are several solutions to detect specific kinds of attackers based on the behavior of users. But, most of these approaches either focus on just the topology of the graph [159, 158] or the detection of anomalous users by exploiting supervised learning techniques [157, 47, 86, 125]. However, we have to note that the main issue of supervised learning is that they are not able to detect new attacker's behaviors, since the classifier is trained based on the known behavioral patterns. Literature also offers approaches to detect anomalous users in OSNs that use unsupervised learning approaches [150, 153, 36, 146] or a combination of supervised and unsupervised techniques [153]. But, existing attack defenses are designed to cope with just one specific type of attack. Although several solutions to detect specific kinds of attacks have been recently proposed, there is no general solution to cope with the main privacy/security attacks in OSNs. In such a scenario, it would be very beneficial to have a solution that can cope with the main privacy/security attacks that can be perpetrated using the social network graph. Our main contribution is proposing a unique unsupervised approach that helps OSNs providers and users to have a global understanding of risky users and detect them. We believe that the core of such a solution is a mechanism able to assign a risk score to each OSNs account. Over the last three years, we have done significant research efforts in analyzing user's behavior to detect risky users included some kinds of well known attacks in centralized and decentralized online social networks. Our research started by proposing a risk assessment approach based on the idea that the more a user behavior diverges from normal behavior, the more it should be considered risky. In our proposed approach, we monitor and analyze the combination of interaction or activity patterns and friendship patterns of users and build the risk estimation model in order to detect and identify those risky users who follow the behavioral patterns of attackers. Since, users in OSNs follow different behavioral patterns, it is not possible to define a unique standard behavioral model that fits all OSNs users' behaviors. Towards this goal, we propose a two-phase risk assessment approach by grouping users in the first phase to find similar users that share the same behavioral patterns and, then in the second phase, for each identified group, building some normal behavior models and compute for each user the level of divergency from these normal behaviors. Then, we extend this approach for Decentralized Online Social Networks (i.e., DOSNs). In the following of this approach, we propose a solution in defining a risk measure to help users in OSNs to judge their direct contacts so as to avoid friendship with malicious users. Finally, we monitor dynamically the friendship patterns of users in a large social graph over time for any anomalous changes reflecting attacker's behaviors. In this thesis, we will describe all the solutions that we proposed