Causal Inference for Jamming Detection in Adverse Scenarios

The goal of this thesis is the development of an anti-jamming defense mechanism based on causal inference. The current state-of-the-art methods to compute causality, i.e., Granger Causality (GC), Transfer Entropy (TE) and Convergent Cross Mapping (CCM) are presented and they are used to detect the smart jammer into an appropriate simulation environment. The performances of the causality tools are evaluated, pointing out how the TE obtains the best results while the GC fails the detection of the intruder. The innovative CCM algorithm, instead, requires to function a deterministic structure of the communications. In the first part of the work, before that simulation environment is impemented, the three methods are compared to underline their theoretical advantages and disadvantages.