Predicting Cyber Security Threats in The Digital Age: A Systematic Review and Analysis

Abstract

Insider threats pose a significant challenge in securing today's digital economy, particularly within the convergence of Critical Infrastructure (CI) and Industry 4.0 applications. This study examines current trends, challenges, and algorithms for detecting and predicting insider threats. We analyse key issues such as data non-stationarity and collusion attacks. The paper proposes a model to address these challenges and evaluates its effectiveness. While the initial results show a 66% accuracy in terms of False Positive Rate (FPR), the study recommends doubling the training data size to further enhance the model's prediction accuracy. This research contributes to improved security solutions for safeguarding critical infrastructure in the era of Industry 4.0. In today's digital world, even simple mistakes when using online systems can lead to cyberattacks with serious consequences. These consequences can include damage to an organization's reputation, financial losses, and disruptions to daily operations. This problem is especially concerning in developing countries. Researchers tackled this issue by building a machine learning model that predicts who might fall victim to cyberattacks. The model analyses social and economic data to identify key factors that make someone more susceptible. This model achieved a high level of accuracy by utilizing a sophisticated machine learning technique combined with an algorithm that discovers hidden patterns in data. To train the model, researchers collected information from both victims and those who haven't been attacked. They then employed a special technique to expand the data available for training, ultimately generating valuable insights that can help predict cyberattacks and the associated risks.