DDoS Event Forecasting using Twitter Data

Distributed Denial of Service (DDoS) attacks have been significant threats to the Internet. Traditional research in cyber security focuses on detecting emerging DDoS attacks by tracing network package flow. A characteristic of DDoS defense is that rescue time is limited since the launch of attack. More resilient detection and defence models are typically more costly. We aim at predicting the likelihood of DDoS attacks by monitoring relevant text streams in social media, so that the level of defense can be adjusted dynamically for maximizing costeffect. To our knowledge, this is a novel yet challenging research question for DDoS rescue. Because the input of this task is a text stream rather than a document, information should be collected both on the textual content of individual posts. We propose a fine-grained hierarchical stream model to capture semantic information over infinitely long history, and reveal burstiness and trends. Empirical evaluation shows that social text streams are indeed informative for DDoS forecasting, and our proposed hierarchical model is more effective compared to strong baseline text stream models and discrete bag-of-words models.