Semantic Text Analysis for Detection of Compromised Accounts on Social Networks
This addresses the issue of malicious account takeovers on social networks, which can exploit user trust, but it is incremental as it builds on existing language modeling techniques.
The paper tackled the problem of detecting compromised accounts on social networks by analyzing text messages for semantic incoherence, using a novel framework based on language model differences, and achieved effectiveness in discovery on a Twitter dataset with a KL-divergence-based feature performing best.
Compromised accounts on social networks are regular user accounts that have been taken over by an entity with malicious intent. Since the adversary exploits the already established trust of a compromised account, it is crucial to detect these accounts to limit the damage they can cause. We propose a novel general framework for semantic analysis of text messages coming out from an account to detect compromised accounts. Our framework is built on the observation that normal users will use language that is measurably different from the language that an adversary would use when the account is compromised. We propose to use the difference of language models of users and adversaries to define novel interpretable semantic features for measuring semantic incoherence in a message stream. We study the effectiveness of the proposed semantic features using a Twitter data set. Evaluation results show that the proposed framework is effective for discovering compromised accounts on social networks and a KL-divergence-based language model feature works best.