Understanding the behavior of malicious applications in social networks

Makridakis, Andreas; Αθανασόπουλος, Ηλίας; Antonatos, Spiros; Antoniades, Demetris; Ioannidis, Sotiris; Markatos, Evangelos P.

doi:10.1109/mnet.2010.5578913

Cited by 24 publications

(12 citation statements)

References 14 publications

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“…Therefore, noncooperative social networks can be modeled well by noncooperative multiagent technologies. Representative studies on noncooperative social networks are the following: [19], [62], [63].…”

Section: Noncooperative Social Networkmentioning

confidence: 99%

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Understanding Social Networks From a Multiagent Perspective

Jiang¹,

Jiang

2014

IEEE Trans. Parallel Distrib. Syst.

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Social networks have recently been widely explored in many fields; these networks are composed of a set of autonomous social actors and the interaction relations among them. Multiagent computing has already been widely envisioned to be a powerful paradigm for modeling autonomous multientity systems; therefore, it is promising to connect the research on social networks and multiagent systems. In general, there are three views for research on social networks: the structure-oriented view, in which only the network structure characteristics among actors are considered, the actor-oriented view, in which only the behavior characteristics of actors are considered, and the actor-structure crossing view, in which both actors and network structures are considered and their crossing effects are explored. This survey paper mainly concerns studies on social networks that have the last two views and discusses the relationship between social networks and multiagent systems. Because coordination is critical for both multiagent systems and social networks, this paper classifies studies on social networks that are based on the coordination mechanisms among the actors in the social networks. By referring to typical types of coordination situations in multiagent systems, social networks in previous studies can be classified into three classes: cooperative social networks, noncooperative social networks, and multiple social networks; for each class, this paper reviews the existing studies and discusses the challenge issues and possible future research directions. From this survey, we find that social networks can be understood well via a multiagent coordination perspective and also that many multiagent coordination techniques can be cogently applied in research on social networks. Moreover, this paper discusses the advantages and disadvantages of the multiagent coordination perspective by comparing with other perspectives on studying social networks.

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Section: Noncooperative Social Networkmentioning

confidence: 99%

“…Currently, social networks are becoming more popular; a side-effect of this growth is that possible exploits can turn social networks into platforms for malicious activities [62]. There are two types of actors in an undependable social network: cooperator and defector.…”

Section: Undependable Actorsmentioning

confidence: 99%

Understanding Social Networks From a Multiagent Perspective

Jiang¹,

Jiang

2014

IEEE Trans. Parallel Distrib. Syst.

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“…Makridakis et al define the term "Antisocial Networks" that refer to distributed systems based on social networking web sites which can be exploited to carry out network attacks [19].…”

Section: Background and Related Workmentioning

confidence: 99%

Correlating a Persona to a Person

Clark

2012

2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing

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Participation in online social networks (OSNs) such as Facebook, LinkedIn, and Twitter has dramatically increased over the years. For example, it is estimated that Facebook has more than eight hundred million registered users. The popularity of OSNs has increased the sheer amount of personal information on the Internet. In this paper, we introduce a profiling and tracking attack that correlates a visitor's online persona that is captured from a seemingly innocuous website with that of the same visitor's real Facebook profile. We compare this with the analytics captured from a custom Facebook Fan Page to show how we can identify the visitor given only their online persona. Furthermore, we create a Facebook application that is linked from the custom website and Fan Page. This application when, accessed and allowed by a visitor (assuming they have a Facebook account), will capture their profile. We tie the analytics captured by the custom website and the Fan Page with that of the profiling and tracking enabled application. Our stated hypothesis is: The majority of visitors can be profiled, tracked, and led to reveal their identity by an adversary who uses website analytic tools. This is because of the actions the visitor performs on the Internet and the information they freely display on their Facebook profiles. We analyze the online behavior of 25 participants and our results show we are able to correctly determine 16 of 25 or 64% of them. We focus on the ramifications of this research and provide defense mechanisms to help protect OSN users.

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“…Makridakis et al [40] use a real application named 'Photo of the Day' to demonstrate how malicious apps on Facebook can launch DDoS attacks using the Facebook platform. King et al [34] conducted a survey to understand users' interaction with Facebook apps.…”

Section: Related Workmentioning

confidence: 99%

FRAppE

Rahman

Huang

Madhyastha

et al. 2012

Proceedings of the 8th International Conference on Emerging Networking Experiments and Technologies

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With 20 million installs a day [1], third-party apps are a major reason for the popularity and addictiveness of Facebook. Unfortunately, hackers have realized the potential of using apps for spreading malware and spam. The problem is already significant, as we find that at least 13% of apps in our dataset are malicious. So far, the research community has focused on detecting malicious posts and campaigns.In this paper, we ask the question: given a Facebook application, can we determine if it is malicious? Our key contribution is in developing FRAppE-Facebook's Rigorous Application Evaluatorarguably the first tool focused on detecting malicious apps on Facebook. To develop FRAppE, we use information gathered by observing the posting behavior of 111K Facebook apps seen across 2.2 million users on Facebook. First, we identify a set of features that help us distinguish malicious apps from benign ones. For example, we find that malicious apps often share names with other apps, and they typically request fewer permissions than benign apps. Second, leveraging these distinguishing features, we show that FRAppE can detect malicious apps with 99.5% accuracy, with no false positives and a low false negative rate (4.1%). Finally, we explore the ecosystem of malicious Facebook apps and identify mechanisms that these apps use to propagate. Interestingly, we find that many apps collude and support each other; in our dataset, we find 1,584 apps enabling the viral propagation of 3,723 other apps through their posts. Long-term, we see FRAppE as a step towards creating an independent watchdog for app assessment and ranking, so as to warn Facebook users before installing apps.

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Understanding the behavior of malicious applications in social networks

Cited by 24 publications

References 14 publications

Understanding Social Networks From a Multiagent Perspective

Understanding Social Networks From a Multiagent Perspective

Correlating a Persona to a Person

FRAppE

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