Deep Learning Based Cyber Event Detection from Open-Source Re-Emerging Social Data

Abstract

Social media forums have emerged as the most popular form of communication in the modern technology era, allowing people to discuss and express their opinions. This increases the amount of material being shared on social media sites. There is a wealth of information about the threat that may be found in such open data sources. The security of already-deployed software and systems relies heavily on the timely detection of newly-emerging threats to their safety that can be gleaned from such information. Despite the fact that several models for detecting cybersecurity events have been presented, it remains challenging to extract security events from the vast amounts of unstructured text present in public data sources. The majority of the currently available methods concentrate on detecting events that have a high number of dimensions. This is because the unstructured text in open data sources typically contains a large number of dimensions. However, to react to attacks quicker than they can be launched, security analysts and information technology operators need to be aware of critical security events as soon as possible, regardless of how often they are reported. This research provides a unique event detection method that can swiftly identify significant security events from open forums such as Twitter. The proposed work identified new threats and the revival of an attack or related event, independent of the volume of mentions relating to those events on Twitter. In this research work, deep learning has been used to extract predictive features from open-source text. The proposed model is composed of data collection, data transformation, feature extraction using deep learning, Latent Dirichlet Allocation (LDA) based medium-level cyber-event detection and final Google Trends-based high-level cyber-event detection. The proposed technique has been evaluated on numerous datasets. Experiment results show that the proposed method outperforms existing methods in detecting cyber events by giving 95.96% accuracy.