Network traffic analysis tools are software applications that can automate and simplify the tasks of network traffic analysis, providing you with actionable insights and recommendations.

Machine learning or artificial intelligence algorithms can be used to learn from the network traffic data and discover any hidden or emerging anomalies or threats. Anomaly analysis is the process of detecting unknown or novel patterns or behaviors of network traffic that deviate from the baseline or the signatures. By using predefined rules or databases, you can match the network traffic data with the signatures, and alert or block any malicious activity. Signature analysis involves detecting known patterns or signatures of network traffic that correspond to specific types of anomalies or attacks. By comparing the current network traffic data with the baseline, any deviations or outliers that indicate anomalies can be identified. Baseline analysis is the process of establishing a normal or expected profile of network traffic behavior, based on historical or statistical data. The most common approaches and techniques for analyzing network traffic data vary depending on your goals and resources.

Once you have collected network traffic data, it is necessary to analyze it for anomalies.

Network packet analyzer tools