How safe is your online social network? Not very, as it turns out. Your friends may not even be human, but rather bots siphoning off your data and influencing your decisions with convincing yet programmed points of view.
A team of computer researchers at the Department of Electrical and Computer Engineering at the University of British Columbia (UBC) has found that hordes of social bots could not only spell disaster for large online destinations like Facebook and Twitter but also threaten the very fabric of the Web and even have implications for our broader economy and society.
Four UBC scientists designed a "social botnet" -- an army of automatic "friends." A botmaster herds its troop of social bots, each of which mimics a person like you and me. The researchers then unleashed the social botnet on an unsuspecting Facebook and its billion-plus profiles.
These social bots masquerade as online users, adding posts that seem like they came from real people. But they secretly promote products or viewpoints, and some you might friend use their new connections to siphon off your private information. When coordinated by a botmaster, these social bots can wreak havoc and steal information at a massive scale.
Traditional botnets don't pose a threat to social networks such as Facebook, where users can easily discriminate between artificial and real people. But the social bots tested at UBC imitated people well enough to infiltrate social networks.
That's not such a big issue with only one fake profile, but if the programmer can control hundreds or thousands of them, then it becomes possible to saturate large parts of the system, to gain access to massive amounts of private data, and to wreck the security model that makes online social networks safe.
Furthermore, because so many services build on top of social networks, the risk runs deeper. Many technologies, including data sharing and backups, integrate with sites like Facebook. Their authentication schemes rely on the implicit trust network that social bots are designed to break into.
The UBC researchers came up with a program that creates Facebook profiles and friends regular users. With the right techniques, it's easy for a program to add people on Facebook as friends. The results surprised the UBC team: "We saw that the success rate can be up to 80 percent. That was quite impressive," says researcher Kosta Beznosov.
Amazingly, some of the bots even got unsolicited messages and requests for friendship by people. Perhaps unsurprisingly, female social bots got 20 to 30 times the number of friend requests from people as male social bots did: 300 requests versus 10 to 15 on average.
How to fake a person on a social network
To infiltrate a network, the bots follow a sophisticated set of behavioral guidelines that place them in positions from which they can access and disseminate information, adapting their actions to large scales, and evade host defenses.
To imitate people, social bots create profiles that they decorate, then develop connections while posting interesting material from the Web. In theory, they could also apply chat software or intercept human conversations to enhance their believability. The individual bots can make their own decisions and receive commands from the central botmaster.
The bots operate in phases. The first step is to establish a believable network to disguise their artificial nature. Profiles that people consider "attractive," meaning likable, have an average number of friends. To get near this "attractive" network size, social bots start by befriending each other.
Next, the social bots solicit human users. As the bots and humans become friends, the bots drop their original connections with each other, eliminating traces of artificiality.
Finally, the bots explore their newfound social network, progressively extending their tentacles through friends of friends. As the social bots infiltrate the targets, they harvest all available private data.
UBC researcher Beznosov recalls, "We were inspired by the paper where they befriend your friends, but on a different social network. For example, they know who your Facebook friends are. They can take this information and take a public picture of you, then create a profile on a completely different social network," such as LinkedIn.
"At that point, the question we had was whether it's possible to do a targeted type of befriending -- where you want to know information about a specific user -- through an algorithmic way to befriend several accounts on the social network, eventually to become friends with that particular target account that you're interested in."
That targeting of specific users didn't work, so the researchers decided to test how many people they could befriend, with the penetration expanding over waves of friendship circles. The research exploits a principle called "triadic closure," first discovered in traditional sociology a century ago, where two parties connected by a mutual acquaintance will likely connect directly to each other. "We implemented automation on top of that."