LED marking systems are currently becoming key elements of every Smart Transport System. Ensuring proper level of security, protection and continuity of failure-free operation seems to be not a completely solved issue. In the article, a system is present allowing to detect different types of anomalies and failures/damage in critical infrastructure of railway transport realized by means of Power Line Communication. There is also described the structure of the examined LED Sign Communications Network. Other discussed topics include significant security problems and maintenance of LED sign system which have direct impact on correct operation of critical communication infrastructure. A two-stage method of anomaly/damage detection is proposed. In the first step, all the outlying observations are detected and eliminated from the analysed network traffic parameters by means of the Cook’s distance. So prepared data is used in stage two to create models on the basis of autoregressive neural network describing variability of the analysed LED Sign Communications Network parameters. Next, relations between the expected network traffic and its real variability are examined in order to detect abnormal behaviour which could indicate an attempt of an attack or failure/damage. There is also proposed a procedure of recurrent learning of the exploited neural networks in case there emerge significant fluctuations in the real PLC traffic. A number of scientific research was realized, which fully confirmed efficiency of the proposed solution and accuracy of autoregressive type of neural network for prediction of the analysed time series.