Effective network traffic management is crucial in enterprise networks, telecommunications, data centers, and many other industrial enterprises. The primary goal of network traffic analysis and management is to enhance the overall performance, reliability, and security of the network.
Many industrial applications require low-latency data transmission for their real-time operations. Yet industrial systems operate in rigid environments that are prone to downtime, and ensuring high reliability and managing network traffic is a critical challenge. Additionally, industrial networks are increasingly becoming a target for intrusions and other cyberthreats, making it important to enforce safety measures such as firewalls, intrusion detection systems, and security protocols.
The essential role of traffic pattern monitoring
To implement effective network traffic monitoring, network administrators use tools that provide real-time visibility, historical data analysis, and customizable reporting features. In order to maintain a well-performing, secure, and optimized network infrastructure, traffic pattern monitoring is also essential. Analyzing historic traffic data and observing traffic patterns helps optimize application performance, achieve efficient resource allocation, and deliver a secure user experience.
By using a traffic pattern monitoring tool, you can:
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Make informed decisions: Analyze network traffic to understand how different components utilize your network’s bandwidth.
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Oversee user behavior: Observe traffic patterns over time to identify peak usage trends.
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Troubleshoot issues: Gain information about slow performance or any other network problems.
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Enhance security: Monitor unusual patterns in data flow or any sudden spike in data traffic that may indicate a security threat and mitigate these anomalies promptly.
How NetFlow Analyzer benefits your business
NetFlow Analyzer’s Network Traffic Pattern Analysis feature helps to identify trends in network traffic, examine network interface behavior, and identify traffic anomalies, issuing real-time alerts upon their detection. The system continuously scrutinizes network traffic patterns and provides insights into ongoing traffic activities. This proactive approach aids in predicting potential bandwidth bottlenecks that could significantly impact the overall network health of an organization.
Network Traffic Pattern Analysis offers detailed insights into both incoming (IN) and outgoing (OUT) traffic for any interface within a specified timeframe, typically set at every 10 minutes by default. This analysis categorizes and segments data for each 10-minute interval within the past hour, delivering average calculations and identifying network traffic patterns.
NetFlow Analyzer’s Pattern Analysis incorporates the exponential moving average (EMA), an ML algorithm, to offer insights into trends and fluctuations. It serves as a crucial tool for network administrators seeking to enhance network performance optimization.
EMA in Traffic Pattern Analysis
Imagine a scenario where a company’s network traffic exhibits sudden spikes in data transfer during certain hours of the day. Network administrators can apply EMA to smooth the traffic data and identify the underlying pattern. If the EMA shows a consistent increase in traffic during those hours, the company can decide to allocate more bandwidth or optimize network resources during that time to maintain performance standards.
Similarly, in a security context, if EMA values for incoming traffic suddenly deviate from the historical pattern, it could be an indicative of a DDoS attack. Network security tools can use EMA-based anomaly detection to trigger alerts and initiate countermeasures when such deviations are detected.
By incorporating EMA into their analysis toolkit, network administrators can make data-driven decisions to optimize performance and enhance network security. Other benefits include:
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Reduction of false positives: EMA helps to smoothen out frequent fluctuations and spikes, providing a clear view of underlying trends and reducing false positives in anomaly detection.
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Identification of trends: EMA aids in identifying trends in network traffic by responding quickly to changes in traffic patterns, placing more emphasis on recent data.
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Anomaly detection: EMA is effective in detecting anomalies in network traffic. Unusual or unexpected behavior is promptly identified as an anomaly.
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Resource allocation decision support: Network administrators leverage EMA to make informed decisions regarding network resource allocation.
The insights gained from network traffic monitoring empower network administrators to make informed decisions related to network optimization, capacity planning, and policy enforcement. With NetFlow Analyzers’s Traffic Pattern Analysis, diagnose performance issues, enhance security, and improve the efficiency of your corporate network.
Boost user experience and optimize bandwidth: Get NetFlow Analyzer today!
