In any industry, any enterprise that does business online needs to ensure that their network stays online. Keeping a network online is an art form and involves juggling the concerns of managing physical and virtual hardware against performance degradation and external cyber threats. As networks become more complex, the old ways of system monitoring are starting to fall behind.
Outdated network monitoring practices are problematic because cyber threats are becoming more advanced than ever before. In “The State of Endpoint Security Risk Report” from the Poneman Institute 77 percent of malware attacks in 2017 were fileless. No less problematic is the task of monitoring application performance. Amazon has identified that even one second of delay can decrease yearly sales by $1.6 billion.
Anomaly detection: Machine learning platforms for real-time decision making
In response to the complexities of managing cybersecurity concerns and network and performance monitoring organisations are incorporating sophisticated AI tools with machine learning to keep up. Network and performance monitoring platforms using machine learning and anomaly detection have the potential to respond to threats in real-time.
Anomaly detection example
This takes us to anomaly detection examples. One high profile provider turning to anomaly detection is Datadog adding a new anomaly detection platform to help manage information from log data. Similarly, Moogsoft offers an “AIOps” solution that uses machine learning to limit the number of alerts sent to the user to only those that are most relevant. Datadog and Moogsoft are not alone either with a range of providers including AppDynamics, Dynatrace and Auvik using anomaly detection as well.
How companies in the supply chain are using anomaly detection to stay up and running
The growth of complex networks mixing traditional infrastructure with cloud services, fog computing and microservices have made network monitoring much more complicated than they were in the past. These systems are all vulnerable to being breached and need to be monitored continually to stay protected.
Without anomaly detection network administrators are forced to rely on dashboards and configurable thresholds to monitor network performance. This approach catches many performance issues but can be too slow to respond to sophisticated cyber attacks. In comparison, an anomaly detection platform would be able to differentiate between normal usage patterns and a malicious attack.
Catching a cyber attack or performance concerns early on can be the difference between normal operations and costly downtime. Anomaly detection acts as a vigilant eye over datasets that jumps on inconsistencies at a level that threshold based alerts can’t replicate. By incorporating a responsive machine learning solution, CTOs make sure that their network remains operational.
The CTOs guide to anomaly detection