Intrusion Detection With Graph in Python

Cyber-physical systems have recently emerged in several practical engineering applications where security and privacy are of paramount importance. This motivated the paper and a recent surge of interest in development of innovative and novel anomaly and intrusion detection technologies. This paper proposes a novel distributed blind intrusion detection framework by modeling sensor measurements as the target graph-signal and utilizing the statistical properties of the graph-signal for intrusion detection. To fully take into account the underlying network structure, the graph similarity matrix is constructed using both the data measured by the sensors and sensors' proximity resulting in a data-adaptive and structure-aware monitoring solution. In the proposed supervised detection framework, the magnitude of the captured data is modeled by Gaussian Markov random field and the corresponding precision matrix is estimated by learning a graph Laplacian matrix from sensor measurements adaptively. The proposed intrusion detection methodology is designed based on a modified Bayesian likelihood ratio test and the closed-form expressions are derived for the test statistic.