Blog or block: Detecting blog bots through behavioral biometrics

Chu, Zi; Gianvecchio, Steven; Koehl, Aaron; Wang, Haining; Jajodia, Sushil

doi:10.1016/j.comnet.2012.10.005

Cited by 42 publications

(23 citation statements)

References 16 publications

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“…Table 8 11 yielded a higher TPR value of 99.4%, whereas the TPR value of our approach is 97.9%. However, in our approach, the advantage of detecting a socialbot on an end host is that we can capture the process information and identify any suspicious socialbot process.…”

Section: Detection Performancementioning

confidence: 71%

Understanding socialbot behavior on end hosts

Cao

et al. 2017

International Journal of Distributed Sensor Networks

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Server-side socialbot detection approaches can identify malicious accounts and spams in online social networks. However, they cannot detect socialbot processes, residing on user hosts, which control these accounts. Therefore, new approaches are needed to detect socialbots on hosts. The fundamental to design host-side detecting approaches is to gain an insight into the behaviors of socialbots on host. In this article, we analyzed a series of representative socialbots in depth and summarized the typical features of socialbot behaviors. We proposed a new approach to defense against socialbots on end host. The contributions of this article are threefold: (1) our analysis approach can be used for reference during analyzing new socialbots in the future; (2) we provide several behavior features of socialbots on hosts, including network flow through which socialbots communicate with botmasters through the online social network, system calls via which socialbots conduct an activity, and process information of socialbots running on hosts. These features can be used by someone to design approaches to identifying socialbots on a host; (3) our proposed detection approach can effectively distinguish between a socialbot and a benign application on end hosts.

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Section: Detection Performancementioning

confidence: 71%

Understanding socialbot behavior on end hosts

Cao

et al. 2017

International Journal of Distributed Sensor Networks

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“…To this end, the system should learn the set of HTTP requests generated during mouse trajectories or shortly after them, and then it should generate an alert when those requests occur at time instants which are too far away from the time intervals of the observed mouse trajectories. Indeed, the ability to discriminate bot-generated traffic from human-generated traffic based solely on webpageembedded loggers of keyboard and mouse events with excellent accuracy and negligible overhead, has been proven [20]. We have not addressed this extension in this work, though.…”

Section: Discussionmentioning

confidence: 93%

Continuous and Non-intrusive Reauthentication of Web Sessions Based on Mouse Dynamics

Medvet

Bartoli

Boem

et al. 2014

2014 Ninth International Conference on Availability, Reliability and Security

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We propose a system for continuous reauthentication of web users based on the observed mouse dynamics. Key feature of our proposal is that no specific software needs to be installed on client machines, which allows to easily integrate continuous reauthentication capabilities into the existing infrastructure of large organizations. We assess our proposal with real data from 24 users, collected during normal working activity for several working days. We obtain accuracy in the order of 97%, which is aligned with earlier proposals requiring instrumentation of client workstations for intercepting all mouse activity-quite a strong requirement for large organizations. Our proposal may constitute an effective layer for a defense-in-depth strategy in several key scenarios: web applications hosted in the cloud, where users authenticate with standard mechanisms; organizations which allow local users to access external web applications, and enterprise applications hosted in local servers or private cloud facilities.

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“…Chu et al [9] proposed a technique to automatically detect blog bots -automated programs that fill forms and post comments. Unlike conventional detection methods that require direct user participation, such as CAPTCHA, the authors presented an approach that uses behavioral biometrics, such as the way to use mouse and keyboard, to distinguish between a real person and a blog bot.…”

Section: Related Workmentioning

confidence: 99%

Learning to Block Undesired Comments in the Blogosphere

Almeida

Alberto

2013

2013 12th International Conference on Machine Learning and Applications

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A significant amount of the available information on web sites come from the interaction with users, such as news sites and blogs, where readers can post comments and sometimes develop habits of frequenting them. Some blogs specialize in certain subjects, receive the users credibility and become references in the field. However, the ease of inserting content through text comments makes room for unwanted messages, which affect the user experience, reduce the quality of the information provided by the websites and indirectly causing personal and economic losses. Given this scenario, this paper presents a comprehensive analysis of machine learning techniques applied to automatically detect undesired comments posted on blogs. Experiments carried out with a real and public database indicate that support vector machines and logistic regression, trained with both attributes extracted from the text messages and metadata from the posts, are promising in the task of filtering unwanted comments.

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Blog or block: Detecting blog bots through behavioral biometrics

Cited by 42 publications

References 16 publications

Understanding socialbot behavior on end hosts

Understanding socialbot behavior on end hosts

Continuous and Non-intrusive Reauthentication of Web Sessions Based on Mouse Dynamics

Learning to Block Undesired Comments in the Blogosphere

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