  Denial of Service (DoS) attacks are common in on-line and mobile services
such as Twitter, Facebook and banking. As the scale and frequency of
Distributed Denial of Service (DDoS) attacks increase, there is an urgent need
for determining the impact of the attack. Two central challenges of the task
are to get feedback from a large number of users and to get it in a timely
manner. In this paper, we present a weakly-supervised model that does not need
annotated data to measure the impact of DoS issues by applying Latent Dirichlet
Allocation and symmetric Kullback-Leibler divergence on tweets. There is a
limitation to the weakly-supervised module. It assumes that the event detected
in a time window is a DoS attack event. This will become less of a problem,
when more non-attack events twitter got collected and become less likely to be
identified as a new event. Another way to remove that limitation, an optional
classification layer, trained on manually annotated DoS attack tweets, to
filter out non-attack tweets can be used to increase precision at the expense
of recall. Experimental results show that we can learn weakly-supervised models
that can achieve comparable precision to supervised ones and can be generalized
across entities in the same industry.
