Cyber Threat Detection Based on Artificial Neural Networks using Event Profiles

This abstract introduces a novel approach to cyber threat detection leveraging artificial neural networks (ANNs) and event profiles. The proposed system aims to enhance cybersecurity by analyzing intricate patterns within event profiles, utilizing the inherent capability of ANNs to discern subtle anomalies indicative of cyber threats. By training the neural network on historical data, the model develops a comprehensive understanding of normal system behavior, enabling it to identify deviations that may signify potential security breaches. The integration of event profiles ensures a nuanced analysis, capturing diverse data points for a more accurate threat assessment. This research offers a promising paradigm in the ongoing quest for robust cyber threat detection systems, combining the power of Machine Learning and nuanced event profiling to bolster the resilience of digital ecosystems against evolving cyber threats.

Keywords: LSTM, CNN, FCNN, SVM, Decision Tree, Random Forest, KNN and Naive Bayes.