Biblio

The continuous change of communication frequency brings difficulties to the reconnaissance and prediction of non-cooperative communication. The core of this communication process is the frequency-hopping (FH) sequence with pseudo-random characteristics, which controls carrier frequency hopping. However, FH sequence is always generated by a certain model and is a kind of time sequence with certain regularity. Long Short-Term Memory (LSTM) neural network in deep learning has been proved to have strong ability to solve time series problems. Therefore, in this paper, we establish LSTM model to implement FH sequence prediction. The simulation results show that LSTM-based scheme can effectively predict frequency point by point based on historical HF frequency data. Further, we achieve frequency interval prediction based on frequency point prediction.

In recent years, the spreading of malicious social media messages about financial stocks has threatened the security of financial market. Market Anomaly Attacks is an illegal practice in the stock or commodities markets that induces investors to make purchase or sale decisions based on false information. Identifying these threats from noisy social media datasets remains challenging because of the long time sequence in these social media postings, ambiguous textual context and the difficulties for traditional deep learning approaches to handle both temporal and text dependent data such as financial social media messages. This research developed a temporal recurrent neural network (TRNN) approach to capturing both time and text sequence dependencies for intelligent detection of market anomalies. We tested the approach by using financial social media of U.S. technology companies and their stock returns. Compared with traditional neural network approaches, TRNN was found to more efficiently and effectively classify abnormal returns.