Predictive Analytics for Ramsomware Attacks: Leveraging AI to Forecast Threats
Keywords:
Ransomware, Predictive Analytics, Artificial Intelligence, Machine Learning, LSTM, Cybersecurity, Threat Forecasting, Anomaly Detection, Behavioral Indicators.Abstract
Ransomware attacks are still a big danger to cybersecurity, causing huge losses of money and data all around the world. This study suggested a predictive analytics architecture that uses artificial intelligence (AI) to identify ransomware threats before they do any damage. We created and tested several machine learning models, such as Random Forest, Support Vector Machine, Gradient Boosted Trees, and Long Short-Term Memory (LSTM) neural networks, using old cybersecurity datasets. The LSTM model did the best job at finding temporal patterns that showed ransomware activity, with the highest accuracy and recall. Key criteria that were shown to be important predictors included failed login attempts, running encryption processes, and unusual file changes. The framework was also tested in a fake ransomware environment, where it showed that it could find problems early enough to take action to stop them. These results show how AI-powered predictive analytics could change how we protect against ransomware from reacting to threats to predicting them before they happen. In the future, we will have to use this in the real world and adapt it to new types of ransomware.
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