Phishcasting: Deep Learning for Time Series Forecasting of Phishing Attacks

Phishing attacks remain pervasive and continue to be a source of significant monetary loss, identity theft, and malware. One of the challenges is that in most organizational settings, the detection paradigm is inherently about identifying and reacting to threats in real-time, as they are unfolding. As a way to complement these efforts with greater foresight, we introduce the idea of phishcasting - forecasting of phishing threat levels weeks or months into the future. Given that phishing attack volume time series data is noisy and devoid of traditional seasonal and cyclical trends, we extend the time series forecasting framework to utilize multiple time series, auxiliary information and alternate representations. We also introduce CoT-Net, a flexible, end-to-end CNN-LSTM based deep learning method for forecasting of complex phishing attack volume time series. CoT-Net uses time series embeddings to uncover correlations between organizational attack patterns within and across industry sectors. Using a publicly available test bed featuring multiple organizations' attack volume over time, we find CoT-Net to outperform most state-of-the-art time series forecasting methods. By showing that phishcasting might be possible and practical, our work has important proactive implications for cybersecurity.