Abstract:
Online communities are growing at a phenomenal rate. With the large number of users these communities contain, attackers are attracted to try and abuse them. Although there are many different ways to abuse them, one of the hardest to secure against is a Denial of Information (DoI) attack. A Denial of Information attack involves flooding a system with low-quality information, detracting from the usefulness of the online community. In extreme cases, DoI attacks make communities unusable due to the high amounts of low-quality information, leading to an analogue of a Denial of Service (DoS) attack.
In my dissertation, I introduce techniques for detection of evolving DoI attacks in online communities using meta-model classification and information unification approaches. I apply insights gained from analysis of the evolution of an arms-race to measure the resilience of our approaches to adaptations by attackers.
My meta-model classification approach involves classifying the "connected payload" associated with the information and using the classification result for the determination. This approach allows for detection of DoI attacks in emerging domains where the amount of information may be constrained. My information unification approach allows for detection of DoI attacks that previously could not be detected. Unifying information across domains provides multiple sources for a single piece of information as well as additional pieces of information specific to each domain. Using this we then identify previously undetectable DoI attacks in the unified information.