University Study Offers Recipe For Stealth Malware Attacks Via Social Networks

Stealing Reality suggests that social net-based attacks could be more dangerous than traditional network attacks

A group of researchers last week published a paper that mathematically shows how a low-and-slow malware attack based on social networking behavior patterns could be more effective than a traditional network attack.
In their paper,
Stealing Reality
(PDF), researchers from MIT, Ben Gurion University, and Deutsche Telekom Laboratories offer formulas that show the potential effectiveness of a stealth attack that uses social networks as its underlying platform.
In this paper we discuss the ability to steal vital pieces of information concerning networks and their users by a nonaggressive -- and hence, harder to detect -- malware agent, the researchers say. We analyze this threat and build a mathematical model capable of predicting the optimal attack strategy against various networks.
Using data from real-world mobile networks, we show that, indeed, in many cases a stealth attack [one that is hard to detect and steals private information at a slow pace] can result in the maximal amount of overall knowledge captured by the operator of this attack. This attack strategy also makes sense when compared to the natural human social interaction and communication patterns.
The paper offers a number of mathematical models conducted on actual mobile network data, showing that malware attacks can be adapted to follow human behavior on social networks.
The rate of human communication and evolution of relationship is very slow compared to traditional malware attack message rates, the paper observes. A Stealing Reality type of attack, which is targeted at learning the social communication patterns, could piggyback on the user-generated messages, or imitate their natural patterns, thus not drawing attention to itself while still achieving its target goals.
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University Study Offers Recipe For Stealth Malware Attacks Via Social Networks