(also pattern identification)
Definition of pattern recognition
Pattern recognition identifies and classifies data patterns and regularities. It is a field of study in computer science and artificial intelligence that aims to develop algorithms and systems that can automatically recognize and analyze patterns in data that humans find too large or complex to analyze manually. It can also make predictions and decisions based on patterns. From a cybersecurity perspective, pattern recognition can detect and respond to various threats.
Types of pattern recognition techniques
- Supervised pattern recognition. The system goes through training using labeled data, knowing the correct output. Then, it learns to recognize patterns and makes predictions or decisions based on them. A typical example is image recognition.
- Unsupervised pattern recognition. The system identifies patterns without knowing the correct output and identifies and discovers hidden relationships between the data points. Clustering is a popular unsupervised pattern recognition application.
- Semi-supervised pattern recognition. It combines supervised and unsupervised learning methods. The labeled data teaches the system to recognize patterns, while the unlabeled data helps it to generalize the learned patterns to new, unlabeled data. The Google search algorithm is one of the most popular examples of semi-supervised learning.
Examples of pattern recognition
Intrusion detection. It compares the identified data patterns to known attack signatures. In turn, it helps intrusion detection systems (IDS) detect suspicious activity and respond to cyberattacks before they damage the system.
Malware detection. Pattern recognition techniques analyze the code behavior to check whether it’s malicious.
Anomaly detection. These tools are like detectives for computer networks that search for unusual or abnormal behavior patterns, such as a sudden surge in the network activity, that could indicate a security breach.
Authentication. Biometric authentication systems, such as face or fingerprint recognition, utilize pattern recognition to grant access to authorized users and prevent unauthorized access to the system.
Fraud detection. These techniques can analyze transaction patterns, including the frequency of transactions, the amount of money transferred, and the location of the transactions, thereby detecting fraudulent activities.