Automated Cybersecurity Briefing Using Deep Learning

Abstract

The urgency and timely requirements of cybersecurity briefings poses a challenge to a few cybersecurity professionals who have to read and summarize vast amount of cybersecurity reports from several sources (personal communication, October 26, 2021). This paper demonstrates a solution based on Long Short Term Memory that automates the process of generating briefs from various cybersecurity report sources and further assesses the standardly used metric(ROUGE) for summary evaluation. This was achieved through the use of CRISP-DM methodology and application of the natural language processing techniques. After training and testing the model, it outperformed other summarizers such as lexRank. Abstractive technique is considered to be relatively strong and dynamic, because sentences that form summaries are generated based on their semantic meaning. On assessing various ROUGE variants, it was clear that evaluating specific summaries require different ROUGE metrics. For instance, ROUGE-1 and ROUGE-2 may be useful if you're working on extractive summarization. Keywords: Cybersecurity Briefing, Recurrent Neural Network, Long Short Term Memory, Abstractive Summary, Extractive Summary, ROUGE, ROUGE-AR.