Anomaly detection is a technique used in data analysis and cybersecurity to identify abnormal patterns that do not conform to expected behavior. These anomalies, also known as outliers, can be indicative of a problem or a potential area of interest. For instance, in cybersecurity, anomaly detection algorithms monitor network traffic to flag unusual activity that could signify a security breach. In finance, it may be used to detect fraudulent transactions. In industrial settings, it could monitor machine behavior to predict equipment failure before it occurs. Anomaly detection leverages statistical, machine learning, or deep learning models to sift through large datasets and highlight deviations from the norm. The challenge in implementing an effective anomaly detection system lies in defining what constitutes "normal" behavior, which may evolve over time or vary across different contexts. Consequently, the system often requires continuous training and fine-tuning to adapt to new patterns and minimize the rate of false positives and negatives. Overall, anomaly detection serves as a critical tool for proactive monitoring and timely intervention in various domains.