Secure Anomaly Detection: Machine Learning Approach of Detection of Abnormal Patterns with Encrypted Data Protection

In an era of big data, it is necessary to ensure its security along with dependability in data-driven systems. This paper presents a novel approach that supports data security mechanisms based on strong encryption techniques alongside machine-learning-based anomaly detection to enhance the safety of data by detecting unusual behaviors in various datasets. Detection of anomalies is crucial in terms of discovering the activities of fraudsters, network hackers, and operational inefficiencies. However, it remains a major challenge to guard sensitive information during storage as well as transmission. We apply advanced machine learning models, like Isolation Forest, One-Class SVM, and Auto encoders, to identify anomalies, and at the same time we apply a cryptographic algorithm like AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) to ensure the integrity and confidentiality of the data. Our proposed system also allows anonymized data to be securely processed to identify the anomalies without compromising its security. The results of the experiments on the benchmark datasets demonstrate that our method is useful for accurately detecting abnormalities while preserving data privacy. The paper highlights why it is important to integrate machine learning and encryption to offer secure and intelligent types of anomaly detection systems in various sectors like banking, healthcare, and cyber security. Future work will also examine privacy-conserving techniques such as homomorphic encryption in order to enhance security.