"Security in the Cyber City - Methods of Anomaly Detection for the Prevention and Detection of Cyberattacks"
Researchers at the Thanthai Periyar Government Arts and Science College have proposed using system behavioral modeling as well as unattended or semi-supervised Machine Learning (ML) to help solve the cybersecurity problem in smart cities. According to the team, by training ML models on relevant datasets, security systems can better identify and mitigate cyber threats. An ongoing challenge is ensuring the reliability and completeness of those datasets so that anomalies can be detected confidently. The researchers examined various anomaly detection methods, assessing their benefits and drawbacks. Their study compares and contrasts methods for identifying anomalies in big data-based cybersecurity. They used survival analysis to assess the advantages and disadvantages of current techniques. This article continues to discuss the study of anomaly detection methods for preventing and detecting cyberattacks.
Submitted by grigby1