Protecting your organization's data is more important than ever before, but traditional safety measures are often ineffective for spotting security breaches in large-scale IT environments. As hackers become more sophisticated, detecting external attacks becomes more difficult since attackers are familiar with typical intrusion detection methods and know exactly how to cover their tracks.
In this white paper, SoftServe experts describe their data-driven process for identifying informational security risks by detecting anomalies, i.e. deviations from typical patterns in network activity. They discuss:
- Parameters for anomaly identification
- Three data science models used for evaluation
- Combining the three data sets for detection
Conceived SoftServe's Data Science Group, our anomaly detection solution uses machine learning to uncover network activity that may indicate an IT security breach. SoftServe’s Data Science Group is comprised of a team of certified data scientists, many with PhDs.