The security frontier: defending against data exfiltration with machine learning

The world is experiencing an exponential escalation in cases of ransomware and data theft, often used to extort funds from companies. In parallel, the industry faces a number of critical vulnerabilities in database software and corporate websites. This situation poses a serious data exposure and exfiltration problem that every security leader and team faces. This article examines that challenge and explores the benefits of Machine Learning algorithms and Network Detection & Response (NDR) approaches.

Manage the challenge of detecting data exfiltration

Data exfiltration is often the last act of a cyberattack, providing the last opportunity to detect the breach before the data is made public or used for other nefarious activities, such as espionage. However, data loss can also be the result of human error. While preventing data exfiltration through security controls is ideal, the growing complexity and dispersion of infrastructure, coupled with the integration of outdated devices, makes prevention a daunting task. In such scenarios, detection acts as our ultimate safety net.

Reverse engineer machine learning

Before the advent of machine learning, thresholds for specific networks or clients were set manually. As a result, an alarm would be triggered when a device sent more than a pre-set amount of data out of the network. However, machine learning algorithms have introduced several benefits for data exfiltration detection, such as learning the network traffic communications and upload/download behavior of clients and servers, establishing suitable thresholds for different clients, servers and networks, recognizing variations in learned volume profiles and detecting outliers and suspicious data exchanges.

ExeonTrace

Network Detection & Response (NDR) solutions offer a comprehensive and insightful method to detect anomalous network activity and unexpected spikes in data transmission. Using machine learning, these solutions establish a baseline of network communication, making it easy to quickly identify outliers. This applies to both volume analysis and hidden channels. Through this advanced and proactive stance, NDRs can detect early signs of intrusion, often well before data exfiltration occurs.

Among these NDR solutions, ExeonTrace stands out for its accuracy in monitoring data volume. This Swiss NDR system, powered by award-winning Machine Learning algorithms, passively inspects and analyzes network traffic in real-time, identifying possible risky or unauthorized data movements. ExeonTrace also integrates seamlessly with your existing infrastructure, eliminating the need for additional hardware agents. The benefits of ExeonTrace go beyond simple security by helping you understand regular and abnormal network behavior, critical to establishing a robust and efficient security posture.

Conclusions

In today's digital landscape, with networks continuing to expand and vulnerabilities increasing, effective detection of data exfiltration becomes imperative. However, given the complexity of modern networks, manually setting thresholds for outlier detection can be not only expensive, but also practically impossible. Through volume-based detection and traffic behavior monitoring, you can identify data exfiltration by identifying abnormal changes in data volume and upload/download traffic patterns. In this context, the power of machine learning in Network Detection & Response (NDR) solutions lies in the ability to automatically identify specific thresholds and outliers for each infrastructure.

Among these NDR solutions, ExeonTrace stands out, offering comprehensive network visibility, effective anomaly detection, and a hardened approach to security. These features ensure that business operations proceed safely and efficiently. Request a demo to learn how you can leverage machine learning-driven NDR to detect data exfiltration and anomalous network behaviors for your organization.