No abstract

SummaryAutomatic Identification System (AIS) represents an important improvement in the fields of maritime security and vessel tracking. It is used by the signatory countries to the SOLAS Convention and by private and public providers. Its main advantage is that it can be used as an additional navigation aids, especially in avoiding collision at sea and in search and rescue operations. The present work analyses the functioning of the AIS System and the ways of exchanging data among the users. We also study one of the vulnerabilities of the System that can be abused by malicious users. The threat itself is analysed in detail in order to provide insight into the very process from the creation of a program to its implementation.

Predicting future trends in the stock market from time-series data is a challenging task due to its high non-linear nature caused by the complexity involved in the trading process. This paper emphasizes the importance of time-series data filtering when neural network models are used for stock market direction forecasting. Performances of three different neural network models are compared on raw data, processed data with simple moving average, and data filtered with discrete wavelet transformation. Applying wavelet transformation on input financial data as a processing step shows better results than the use of raw financial data or simple moving average. Also, among tested neural network models, the better results are obtained by using long short-term neural network then by using other neural network models.