  In order to satisfy diverse quality-of-service (QoS) requirements of complex
real-time video applications, civilian and tactical use cases are employing
software-defined hybrid edge-cloud systems. One of the primary QoS requirements
of such applications is ultra-low end-to-end latency for video applications
that necessitates rapid frame transfer between end-devices and edge servers
using software-defined networking (SDN). Failing to guarantee such strict
requirements leads to quality degradation of video applications and
subsequently mission failure. In this paper, we show how a collaborative group
of attackers can exploit SDN's control communications to launch Denial of
Quality of Service (DQoS) attack that artificially increases end-to-end latency
of video frames and yet evades detection. In particular, we show how Deep
Neural Network (DNN) model training on all or partial network state information
can help predict network packet drop rates with reasonable accuracy. We also
show how such predictions can help design an attack model that can inflict just
the right amount of added latency to the end-to-end video processing that is
enough to cause considerable QoS degradation but not too much to raise
suspicion. We use a realistic edge-cloud testbed on GENI platform for training
data collection and demonstration of high model accuracy and attack success
rate.
