AI News, New Anomaly Network Intrusion Detection System in Cloud ... artificial intelligence

Threat Visualization

The application of artificial intelligence to the cyber defense challenge has marked a fundamental shift in our ability to protect critical data systems and digital infrastructures.

Cyber AI can then take the right action, at the right time, to contain a threat in seconds – a unique Autonomous Response capability hailed by Dr Nick Jennings, Chair in Artificial Intelligence at Imperial College London, as a “significant engineering innovation … essential for dealing with the volume, novelty, and speed of modern cyber incidents.” As a new generation of cyber-threats, powered by offensive AI, emerge, Autonomous Response AI will be critical to fight back with the precision and speed necessary.

Security alerts in Azure Security Center

In the past, companies typically only had to worry about web site defacement by individual attackers who were mostly interested in seeing “what they could do".

They are now interested in stealing information, financial accounts, and private data – all of which they can use to generate cash on the open market or to leverage a particular business, political, or military position.

In response, organizations often deploy various point solutions, which focus on defending either the enterprise perimeter or endpoints by looking for known attack signatures.

The wide-reaching and diverse collection of datasets enables the discovering of new attack patterns and trends across its on-premises consumer and enterprise products, as well as its online services.

Breakthroughs in big data and machine learning technologies are leveraged to evaluate events across the entire cloud fabric – detecting threats that would be impossible to identify using manual approaches and predicting the evolution of attacks.

Researchers also receive threat intelligence information that is shared among major cloud service providers and subscribes to threat intelligence feeds from third parties.

Azure Security Center can use behavioral analytics to identify compromised resources based on analysis of virtual machine logs, virtual network device logs, fabric logs, crash dumps and other sources.

In contrast to behavioral analytics (which depends on known patterns derived from large data sets), anomaly detection is more “personalized” and focuses on baselines that are specific to your deployments.

This includes the following initiatives: These combined efforts culminate in new and improved detections, which you can benefit from instantly – there’s no action for you to take.

The following topics guide you through the different alerts, according to resource types: The following topics explain how Security Center uses the different telemetry that it collects from integrating with the Azure infrastructure, in order to apply additional protection layers for resources deployed on Azure: A

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