Anomaly Detection Systems for Protecting Genomic Databases from Cyber Attacks

Abstract

Anomaly Detection Systems play a pivotal role in safeguarding genomic databases from cyber-attacks by identifying irregular patterns and potential threats within vast amounts of genomic data. These systems leverage advanced algorithms to detect deviations from normal genomic behavior, enabling early detection of unauthorized access, data breaches, or malicious activities. By analyzing diverse genomic data sets, including DNA sequences, gene expressions, and variations, anomaly detection systems can identify anomalies that might indicate cyber threats such as data tampering, injection attacks, or unauthorized data access. Integrating machine learning and statistical techniques, these systems continually adapt to evolving attack strategies, providing proactive defense measures to protect sensitive genomic information from exploitation and ensuring the integrity and confidentiality of genetic data for research and medical purposes.