The Alcide platform provides a threat detection engine and offers protection against attacks that are either overlooked or undetected by traditional protection layers, including abnormal behaviors and security incidents such as DNS exfiltration, spoofing, poisoning, and lateral movement.
While security features like micro-segmentation and cloud-provider security groups limit the allowed network connections between potentially interacting applications’ workloads, they cannot stop the abuse of the permitted connections by external attackers, internally deployed malware or malicious insiders. For example, a web server should be allowed to connect to the database used by the web application it is exposing to the world, but if there is a vulnerability in this application, an attacker may exploit it to gain access through it to the data in the database.
This is where automated detection of anomalous behavior of workloads comes in. Gathering information about each workload’s behavior and network usage, and processing it with machine learning techniques directed by security expertise, highlights unexpected network usage patterns and unusual data transfers initiated by workloads.
Thus, we can detect and identify many variants of malicious behavior, like: incoming connections from known malicious sources, reconnaissance of services deployed inside the data center from internal or external endpoints, unexpected changes in data transfers between internal databases and internal, possibly compromised, processing workloads, and attempts by infected services to connect to remote command and control servers or exfiltrate sensitive information from the data center.