Authors: Yazan Boshmaf, Lldar Muslukhov, Konstantin Beznosov, Matei Ripeanu University of British Columbia Annual Computer Security Applications Conference (ACSAC) 2011 Presented By: Gavin Grant

Abstract OSN Vulnerabilities Socialbot Network The Attack Findings FIS effectiveness

Social Networks have millions of users Illustrate that Online Social Networks (OSN) are vulnerable to infiltrations by socialbots In particular Facebook 80% success rate Socialbots – computer programs that control OSN accounts and mimic real users

Ineffective CAPTCHAs Hiring cheap labor ($1 per 1,000 broken) Reusing session IDs of known CAPTCHAs Fake User Accounts and Profiles and profile Crawlable Social Graphs Traversing linked profiles Exploitable Platforms and APIs Use APIs to automate the execution of activities

Set of socialbots owned and maintained by human controller called the botherder Made up of socialbots, botmaster, and command and control channel Socialbot controls a profile Data collected called botcargo Capable of executing commands Botmaster is software botherder uses to send commands through C & C channel C & C facilitates transfer of botcargo and commands

Read, write, connect, disconnect Set of commands used to mimic a real user Native commands Master commands

Botworker builds and maintains profiles Botupdater pushes new software updates C & C engine maintains a repository of master commands Master commands needed Cluster Rand_connect(k) Decluster Crawl_extneighborhood Mutual_connect Harvest-data

Communication model Works with socialbot-OSN Channel Only OSN-specific API calls and HTTP traffic Helps in non detection

Socialbot has to hide its real identity Botmaster should be able to perform large-scale infiltration C & C channel traffic has to look benign

Facebook Immune System (FIS) 8 week process Exploited Facebook’s Graph API to carry out social- interaction operations Used HTTP request to send friendship request Iheartquotes.com, decaptcher.com, hotornot.com, mail.ru

102 socialbots created and 1 botmaster Users were created manually 49 males 53 females 5053 valid profile IDs 25 request per day per socialbot Harvested data

First 2 weeks 2 days t send 5043 request (2,391 male, female) 976 accepted (381 M, 595 F) Next 6 weeks 3,517 more users added 2,079 infiltrated successfully Generated 250 GB inbound and 3 GB outbound traffic Acceptance rate increase to 80% as mutual friends increased

News feeds Profile info Wall messages 3,055 direct neighborhoods 1,085,785 extended neighborhoods

Real time learning system used to protect its users Only 20 bots were flagged by system Doesn’t consider fake accounts a real threat

OSN vulnerability to a large-scale socialbot network infiltration Defense social networks have against social bots that mimic human behavior Prayed on common user behavior

Only Facebook was attacked Didn’t provide any prevention techniques

Try on other social networking sites Not create socialbots manually