NDLI: The socialbot network: when bots socialize for fame and money

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Understanding the prevalence and use of alternative plans in malware with network games

Distilling critical attack graph surface iteratively through minimum-cost SAT solving

"Mix-in-Place" anonymous networking using secure function evaluation

The socialbot network: when bots socialize for fame and money

A peel of onion

The science of cyber security experimentation: the DETER project

Facing the facts about image type in recognition-based graphical passwords

Improving robustness of DNS to software vulnerabilities

Exploring the potential benefits of expanded rate limiting in Tor: slow and steady wins the race with Tortoise

SEMAGE: a new image-based two-factor CAPTCHA

ASIDE: IDE support for web application security

An empirical study of visual security cues to prevent the SSLstripping attack

Key escrow from a safe distance: looking back at the Clipper Chip

Reliable telemetry in white spaces using remote attestation

Mitigating code-reuse attacks with control-flow locking

Nexat: a history-based approach to predict attacker actions

BareBox: efficient malware analysis on bare-metal

FORECAST: skimming off the malware cream

RIPE: runtime intrusion prevention evaluator

Security through amnesia: a software-based solution to the cold boot attack on disk encryption

Detecting and resolving privacy conflicts for collaborative data sharing in online social networks

PhorceField: a phish-proof password ceremony

Enabling secure VM-vTPM migration in private clouds

"Super nodes" in Tor: existence and security implication

BLOCK: a black-box approach for detection of state violation attacks towards web applications

Tracking payment card data flow using virtual machine state introspection

AdSentry: comprehensive and flexible confinement of JavaScript-based advertisements

Don't Bump, Shake on It: the exploitation of a popular accelerometer-based smart phone exchange and its secure replacement

deRop: removing return-oriented programming from malware

From prey to hunter: transforming legacy embedded devices into exploitation sensor grids

Automated remote repair for mobile malware

Detecting malware's failover C&C strategies with squeeze

Hit 'em where it hurts: a live security exercise on cyber situational awareness

Private search in the real world

Social snapshots: digital forensics for online social networks

Dynamic sample size detection in continuous authentication using sequential sampling

Exposing invisible timing-based traffic watermarks with BACKLIT

Smart metering de-pseudonymization

A server- and browser-transparent CSRF defense for web 2.0 applications

WebJail: least-privilege integration of third-party components in web mashups

Attacks on WebView in the Android system

Static detection of malicious JavaScript-bearing PDF documents

The socialbot network: when bots socialize for fame and money

Content Provider	ACM Digital Library
Author	Boshmaf, Yazan Muslukhov, Ildar Beznosov, Konstantin Ripeanu, Matei
Abstract	Online Social Networks (OSNs) have become an integral part of today's Web. Politicians, celebrities, revolutionists, and others use OSNs as a podium to deliver their message to millions of active web users. Unfortunately, in the wrong hands, OSNs can be used to run astroturf campaigns to spread misinformation and propaganda. Such campaigns usually start off by infiltrating a targeted OSN on a large scale. In this paper, we evaluate how vulnerable OSNs are to a large-scale infiltration by socialbots: computer programs that control OSN accounts and mimic real users. We adopt a traditional web-based botnet design and built a Socialbot Network (SbN): a group of adaptive socialbots that are orchestrated in a command-and-control fashion. We operated such an SbN on Facebook---a 750 million user OSN---for about 8 weeks. We collected data related to users' behavior in response to a large-scale infiltration where socialbots were used to connect to a large number of Facebook users. Our results show that (1) OSNs, such as Facebook, can be infiltrated with a success rate of up to 80%, (2) depending on users' privacy settings, a successful infiltration can result in privacy breaches where even more users' data are exposed when compared to a purely public access, and (3) in practice, OSN security defenses, such as the Facebook Immune System, are not effective enough in detecting or stopping a large-scale infiltration as it occurs.
Starting Page	93
Ending Page	102
Page Count	10
File Format	PDF
ISBN	9781450306720
DOI	10.1145/2076732.2076746
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-12-05
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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