Towards a Content-based Defense against Text DDoS in 9–1-1 Emergency Systems

Text messaging is getting increasingly popular among all generations because it is built into the cellphone carried by people all the time. People, including those with speech and hearing disabilities, are starting to use text messages in place of voice 9-1-1 calls to call for help during emergencies and many 9-1-1 centers are starting to support text messaging. Since text messages take less bandwidth, it is more likely to be available in a major disaster than voice calls. Text messages are also useful in emergency situation such as a child hiding in a closet during a home invasion. On the other hand, text message system is also subject to abuse by hackers. In a recent attack, a teenager was able to send voice calls from a large number of smartphones to 9-1-1 call centers. That same Distributed Denial of Services (DDoS) attack can happen to text messages. While it does not fall under the policy of the call centers to filter out the incoming messages, it would be useful to do some preliminary analyses of the text messages and based on the result of those analyses determine the priority order for processing thereby helping human operators to efficiently manage the messages. In this work, we design and implement several new Natural Language Processing (NLP) techniques to analyze the contents of the incoming text messages to an emergency call center to provide insights about potential spam or DDoS attacks to 9-1-1 centers. Our preliminary results show that the task of automatically analyzing the text to determine if a text is part of an attack can be done with reasonable accuracy.