Preventing Operational Disruption and Downtime
Keeping People and Packages Moving
The global economy thrives on the seamless movement of people and goods. Individual cargo ships transport 8,000 containers on a single voyage.1 Mass transit systems carry over 53 billion riders each year,2 while over 8.8 billion passengers flow through airports around the world.3
Think about the impact of a system outage or technology glitch on the processing of people and products. Missed connections, delayed baggage, and unsafe environments are just some of the implications.
Unplanned downtime happens for multiple reasons – a device breaks down from operating 24/7, a networking change disturbs an OT system, or a cyber incident halts a critical process.
In transportation and logistics operations where every minute counts, operators can lose valuable time trying to understand and address issues that arise. Imagine the benefits of proactively identifying potential equipment problems and spotting cyber threats before they disrupt operations?
1 “Benefits of Liner Shipping: Efficiency,” World Shipping Council, 2019
Using Anomaly Detection to Identify At-Risk Devices and Processes Before They Fail
The Nozomi Networks solution tackles the risk of transportation and logistics system downtime through passive network monitoring and anomaly detection that alerts you to irregular behavior.
In the initial Dynamic Learning™ phase, the solution uses machine learning and artificial intelligence to observe your network traffic and create asset and operational baselines. It models behavior and correlates multiple types of data, including information about similar assets within the system, to determine what normal activity looks like.
In monitoring phase, the solution automatically detects when a specific device or automated operation is deviating from its baseline, and moving towards a state that could disrupt services. It uses advanced correlation and context to deliver a simple, consolidated view of what’s happening in your networks, and proactively alert you that remediation may be necessary.
Anomaly detection significantly reduces troubleshooting efforts and enables you to take action before a device or automated operation fails.
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