6. Advanced Techniques for Digital Evidence Preservation: The Power of Blockchain and Machine Learning

Cybercrime is one of the fastest-growing crimes worldwide, and they are increasing in volume, sophistication, and cost. According to numerous reports such as Cybersecurity Ventures and others it is estimated that every seven seconds, cyber attackers penetrated into Cyber Systems. As a result, one of the essential parts of any system for storing and handling all the events is the log system. However, the system is not robust, and detecting an anomaly in logs has been challenging because of the continuous and ever-changing log events and their mutability property. Attackers attempt to modify the logs in order to avoid being discovered, which extends the time between detection and triage. In this work, we propose a novel model using Blockchain to problem of log analysis by suggesting two modules, anomaly detection using different machine learning models and Distributed Immutable storage system for securely storing the logs. We also present descriptive and user-friendly Web Application by integrating all modules using HTML, CSS, and Flask Framework on the Heroku cloud environment. Using proposed Hybrid Machine Learning Model, we are able to achieve 99.7% accuracy for detecting network anomalies.