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Recent developments in intelligent transport systems (ITS) based on smartmobility significantly improves safety and security over roads and highways.ITS networks are comprised of the Internet-connected vehicles (mobile nodes),roadside units (RSU), cellular base stations and conventional core networkrouters to create a complete data transmission platform that provides real-timetraffic information and enable prediction of future traffic conditions.However, the heterogeneity and complexity of the underlying ITS networks raisenew challenges in intrusion prevention of mobile network nodes and detection ofsecurity attacks due to such highly vulnerable mobile nodes. In this paper, weconsider a new type of security attack referred to as crossfire attack, whichinvolves a large number of compromised nodes that generate low-intensitytraffic in a temporally coordinated fashion such that target links or hosts(victims) are disconnected from the rest of the network. Detection of suchattacks is challenging since the attacking traffic flows are indistinguishablefrom the legitimate flows. With the support of software-defined networking thatenables dynamic network monitoring and traffic characteristic extraction, wedevelop a machine learning model that can learn the temporal correlation amongtraffic flows traversing in the ITS network, thus differentiating legitimateflows from coordinated attacking flows. We use different deep learningalgorithms to train the model and study the performance using Mininet-WiFiemulation platform. The results show that our approach achieves a detectionaccuracy of at least 80%.

Crossfire Attack Detection using Deep Learning in Software Defined ITS Networks