Identify Data Exfiltration with Machine Learning
Why Is Data Expense Detection So Important?
The world is witnessing an exponential increase in ransomware and data theft that are used to extort companies. At the same time, the industry is facing many critical vulnerabilities in database software and enterprise websites. This evolution paints a harrowing picture of data exposure and exfiltration that every security leader and team is facing. This article highlights these challenges and outlines the benefits that Machine Learning algorithms and Network Detection & Response (NDR) approaches bring.
Data exfiltration often serves as the final act of a cyber attack, making it the last chance to detect a breach before the data is made public or used for other malicious activity, such as espionage. However, data leaks are not only the result of cyberattacks, they can also be a consequence of human error. While preventing data exfiltration through security controls is ideal, the increasing complexity and deployment of infrastructure, coupled with the integration of legacy devices, makes prevention an overwhelming task. In such scenarios, detection serves as our main safety net – indeed, better late than never.
Addressing the Challenge of Detecting Data Exfiltration
Attackers can exploit many security holes to harvest and extract data, using protocols such as DNS, HTTP(S), FTP, and SMB. That MITER ATT&CK Framework explains many such exfiltration attack patterns. However, keeping up with each protocol and infrastructure modification is a daunting task, complicating the integration towards holistic security monitoring. What is required is a device- or network-specific volume-based analysis of the relevant thresholds.
This is where Network Detection & Response (NDR) technology comes in. ML-based NDR enables critical network monitoring by providing two important properties:
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They enable proper monitoring of all associated network communications – the cornerstone of comprehensive data exfiltration monitoring. It includes not only internal-external system interaction but also internal communication. While some attack groups extract data directly to the outside, others use dedicated internal exfiltration hosts.
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Machine learning algorithms enable context-specific learning of multiple thresholds for different devices and networks, which is critical in today’s diverse infrastructure landscape.
Machine Learning Decoding for Data Exfiltration Detection
Prior to Machine Learning, thresholds for specific networks or clients were set manually. As a result, an alert is triggered when a device sends more than a certain threshold of data outside the network. However, Machine Learning algorithms bring several advantages to data exfiltration detection:
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Studying network traffic communications and client and server upload/download behavior, provides an important basis for anomaly detection.
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Set appropriate thresholds for different clients, servers and networks. Defining and maintaining these thresholds for each network or client group would be a tedious task.
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Recognizes changes in the studied volume profile, and detects outliers and suspicious exchanges of data, both internally and between internal and external systems.
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Use scoring mechanisms to measure outliers, relate data to other systems, and generate alerts for identified anomalies.
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| Visualization: When the traffic volume exceeds a certain threshold, as determined by the studied profile, an alert will be triggered. |
ML Based Network Detection & Response for Rescue
Network Detection & Response (NDR) The solution provides a comprehensive and in-depth method for detecting abnormal network activity and unexpected spikes in data transmission. Leveraging Machine Learning (ML), this solution lays the foundation for network communications, facilitating rapid identification of outliers. This applies to volume analysis and secret channels. Through this sophisticated proactive stance, NDR can detect early signs of intrusion, often long before data exfiltration occurs.
One such NDR solution, which is distinguished by its precise monitoring of data volumes, is ExeonTrace. This Swiss NDR system, driven by award-winning ML algorithms, passively examines and analyzes network traffic in real time, identifying potentially risky or unauthorized data movements. Additionally, ExeonTrace integrates seamlessly with existing infrastructure, eliminating the need for additional hardware agents. ExeonTrace’s strengths go beyond security, helping to understand regular and anomalous network behavior – a critical factor in building a strong and efficient security posture.
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| ExeonTrace Platform: Data Volume Outlier Detection |
Key takeaways
In today’s digital landscape, networks are constantly evolving, and vulnerabilities are increasing. As a result, effective data exfiltration detection becomes indispensable. However, with the complexity of modern networks, setting manual thresholds for outlier detection is not only complicated but also nearly impossible. Through volume-based detection and traffic behavior monitoring, one can identify data exfiltration, pinpointing abnormal data volume changes and upload/download traffic patterns. Therein 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 strengthened security stance. These features ensure that business operations run safely and efficiently. Request a demo to find out how to leverage ML-based NDR to detect data exfiltration and anomalous network behavior for your organization.
