Why Data Exfiltration Detection is Paramount?

The world is witnessing an exponential rise in ransomware and data theft employed to extort companies. At the same time, the industry faces numerous critical vulnerabilities in database software and company websites. This evolution paints a dire picture of data exposure and exfiltration that every security leader and team is grappling with. This article highlights this challenge and expounds on the benefits that Machine Learning algorithms and Network Detection & Response (NDR) approaches bring to the table.

Data exfiltration often serves as the final act of a cyberattack, making it the last window of opportunity to detect the breach before the data is made public or is used for other sinister activities, such as espionage. However, data leakage isn't only an aftermath of cyberattacks, it can also be a consequence of human error. While prevention of data exfiltration through security controls is ideal, the escalating complexity and dispersion of infrastructures, accompanied by the integration of legacy devices, makes prevention a strenuous task. In such scenarios, detection serves as our ultimate safety net – indeed, better late than never.

Addressing the Challenge of Detecting Data Exfiltration

Attackers can exploit numerous security gaps to harvest and exfiltrate data, employing protocols like DNS, HTTP(S), FTP and SMB. The MITRE ATT&CK framework describes many such exfiltration attack patterns. However, keeping pace with every protocol and infrastructure modification is a daunting task, complicating the integration towards holistic security monitoring. What's needed is device- or network-specific volume-based analysis of relevant thresholds.

This is where Network Detection & Response (NDR) technology steps in. ML-driven NDR allows for essential network monitoring by providing two significant properties:

1. They enable feasible monitoring of all related network communications – the bedrock of comprehensive data exfiltration monitoring. This covers not only internal-external system interactions but also internal communications. While some attack groups exfiltrate data directly to the outside, others employ dedicated internal exfiltration hosts.
2. Machine learning algorithms aid in context-specific learning of diverse thresholds for varying devices and networks, crucial in the current diverse infrastructure landscape.

Decoding Machine Learning for Data Exfiltration Detection

Before Machine Learning, thresholds for specific networks or clients were manually set. Consequently, an alert was triggered when a device sent more than the specific threshold of data outside the network. However, Machine Learning algorithms brought several advantages for data exfiltration detection:

1. Learning the network traffic communications and the upload/download behavior of clients and servers, providing the essential baseline for anomaly detection.
2. Establishing suitable thresholds for different clients, servers, and networks. Defining and maintaining these thresholds for each network or client group would otherwise be a tedious task.
3. Recognizing changes in learned volume profiles, and detecting outliers and suspicious data exchanges, either internally or between internal and external systems.
4. Employing scoring mechanisms to quantify outliers, correlating the data with other systems, and generating alerts for identified anomalies.

Visualization: When the traffic volume surpasses a certain threshold, as determined by the learned profile, an alert will be triggered.

ML-driven Network Detection & Response to the Rescue

Network Detection & Response (NDR) solutions provide a comprehensive and insightful method to detect abnormal network activities and unexpected surges in data transmission. Leveraging Machine Learning (ML), these solutions establish a network communication baseline, facilitating the swift identification of outliers. This applies to volume analysis and covert channels alike. Through this advanced, proactive stance, NDRs can detect the initial signs of intrusion, often well before data exfiltration transpires.

One NDR solution, distinguished by its precise data volume monitoring, is ExeonTrace. This Swiss NDR system, driven by award-winning ML algorithms, passively inspects and analyzes network traffic in real time, identifying potentially risky or unauthorized data movement. Moreover, ExeonTrace integrates seamlessly with existing infrastructure, thereby eliminating the necessity for additional hardware agents. The advantages of ExeonTrace extend beyond mere security, aiding in the comprehension of regular and anomalous network behavior – a critical factor in establishing a robust and efficient security posture.

ExeonTrace Platform: Data Volume Outlier Detection

Key Takeaways

In today's digital landscape, networks are continually expanding, and vulnerabilities are escalating. As a result, effective data exfiltration detection becomes indispensable. However, with the complexity of modern networks, setting manual thresholds for outlier detection can not only be cumbersome but also virtually impossible. Through volume-based detections and traffic behaviour monitoring, one can identify data exfiltration, pinpointing abnormal alterations in data volume and upload/download traffic patterns. Herein lies the power of Machine Learning (ML) in Network Detection & Response (NDR) systems: it automatically identifies infrastructure-specific thresholds and outliers.

Among these NDR solutions, ExeonTrace stands out, offering comprehensive network visibility, effective anomaly detection, and a fortified security stance. These features ensure that business operations proceed with security and efficiency. Request a demo to find out how to leverage ML-driven NDR to detect data exfiltration and anomalous network behaviours for your organisation.