**Description:** Anomaly detection in networks is the process of identifying unusual patterns in network traffic that may indicate security threats. This approach is based on the premise that malicious or unauthorized activities tend to differ from normal network behaviors. Using advanced artificial intelligence (AI) techniques, large volumes of data can be analyzed in real-time to detect deviations that might go unnoticed by traditional methods. Key features of anomaly detection include the ability to learn from historical data, adapt to changes in network behavior, and minimize false alarms. The relevance of this technique lies in its ability to enhance cybersecurity, enabling organizations to quickly identify and respond to potential intrusions or attacks. As networks become more complex and attacks more sophisticated, anomaly detection becomes an essential tool for protecting the integrity and confidentiality of information.
**History:** Anomaly detection in networks began to gain attention in the 1990s with the rise of the Internet and the increase in cyber threats. Initially, simple statistical methods were used to identify unusual patterns. With advancements in technology and the development of machine learning algorithms in the 2000s, anomaly detection became more sophisticated. The introduction of artificial intelligence techniques has allowed systems to learn and adapt to new traffic patterns, significantly improving the accuracy and effectiveness of detection.
**Uses:** Anomaly detection is primarily used in cybersecurity to identify intrusions, denial-of-service (DDoS) attacks, and malicious behaviors in diverse network environments. It is also applied in network performance monitoring, helping to identify congestion issues or hardware failures. Additionally, it is used in fraud analysis in financial transactions, where unusual patterns that may indicate fraudulent activities are sought.
**Examples:** A practical example of anomaly detection is the use of intrusion detection systems (IDS) that analyze network traffic in real-time. If a user attempts to access sensitive data unusually, the system can generate an alert. Another example is the use of machine learning algorithms to detect anomalous behavior patterns in transactional data across various application domains, which can help prevent fraud before it occurs.
