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Analysis Study Research Paper | Computer Science & Engineering | India | Volume 10 Issue 1, January 2021 | Rating: 2.8 / 10
Anomaly Detection: Enhancing Systems with Machine Learning
Yogananda Domlur Seetharama [2]
Abstract: Anomaly detection is crucial across various industries for maintaining system integrity and financial health by identifying irregular data patterns. Traditional methods, which are manual and rule-based, are often static and inefficient, especially with large and diverse data sets. Machine learning ML enhances anomaly detection by automating the process, learning from data without frequent updates, and handling large volumes of complex data efficiently. ML techniques, including unsupervised and supervised learning, improve the accuracy and scalability of anomaly detection. As ML technology advances, it promises more sophisticated and adaptable solutions for anomaly detection, significantly reducing potential financial losses and improving security and operational efficiency across industries.
Keywords: anomaly detection, machine learning, data integrity, unsupervised learning, supervised learning
Edition: Volume 10 Issue 1, January 2021,
Pages: 1659 - 1668