As the digital age evolves and continues to shape the business landscape, corporate networks have become increasingly complex and distributed. The amount of data a company collects to detect malicious behaviour constantly increases, making it challenging to detect deceptive and unknown attack patterns and the so-called "needle in the haystack". With a growing number of cybersecurity threats, such as data breaches, ransomware attacks, and malicious insiders, organizations are facing significant challenges in successfully monitoring and securing their networks. Furthermore, the talent shortage in the field of cybersecurity makes manual threat hunting and log correlation a cumbersome and difficult task. To address these challenges, organizations are turning to predictive analytics and Machine Learning (ML) driven network security solutions as essential tools for securing their networks against cyber threats and the unknown bad.

The Role of ML-Driven Network Security Solutions

ML-driven network security solutions in cybersecurity refer to the use of self-learning algorithms and other predictive technologies (statistics, time analysis, correlations etc.) to automate various aspects of threat detection. The use of ML algorithms is becoming increasingly popular for scalable technologies due to the limitations present in traditional rule-based security solutions. This results in the processing of data through advanced algorithms that can identify patterns, anomalies, and other subtle indicators of malicious activity, including new and evolving threats that may not have known bad indicators or existing signatures.

Detecting known threat indicators and blocking established attack patterns is still a crucial part of overall cyber hygiene. However, traditional approaches using threat feeds and static rules can become time-consuming when it comes to maintaining and covering all the different log sources. In addition, Indicators of Attack (IoA) or Indicators of Compromise (IoC) may not be available at the time of an attack or are quickly outdated. Consequently, companies require other approaches to fill this gap in their cybersecurity posture.

In summary, the mentioned drawbacks of rule-based security solutions highlight the significance of taking a more holistic approach to network security, which should nowadays include ML-powered Network Detection and Response (NDR) solutions to complement traditional detection capabilities and preventive security measures.

The Benefits of ML for Network Security

So, how is Machine Learning (ML) shaping the future of network security? The truth is ML-powered security solutions are bringing about a significant transformation in network security by providing security teams with numerous benefits and enhancing the overall threat detection capabilities of organizations:

• Big data analytics:With the ever-increasing amount of data and different log sources, organisations must be able to process vast amounts of information in real-time, including network traffic logs, endpoints, and other sources of information related to cyber threats. In this regard, ML algorithms can aid in the detection of security threats by identifying patterns and anomalies that may otherwise go unnoticed. Consequently, the ability and flexibility of a solution to incorporate different log sources should be a key requirement for threat detection capabilities.
• Automated analysis of anomalous behavior: AI enables a much-required health monitoring of network activity by utilising the analysis of normal network traffic as a baseline. With the help of automated correlation and clustering, outliers and unusual behavior can be detected, reducing the need for manual detection engineering and threat hunting. Key questions to be answered include "what is the activity of other clients in the network?" and "is a client's behavior in line with its own previous activities?" These approaches allow for the detection of unusual behaviors like domain-generated algorithms (DGA) domains, volume-based irregularities in network connections, and unusual communication patterns (e.g., lateral movement) in the network. Therefore, comparing a client's current behavior with that of its peers serves as a suitable baseline for identifying subtle anomalies.
• Detect unknown attacks in real-time: Whileit is relatively easy to directly detect known bad indicators (specific IP addresses, domains etc.), many attacks can go undetected when these indicators are not present. If that is the case, statistics, time and correlation-based detections are of enormous value to detect unknown attack patterns in an automated manner. By incorporating algorithmic approaches, traditional security solutions based on signatures and indicators of compromise (IoC) can be enhanced to become more self-sufficient and less reliant on known malware indicators.
• Self-learning detection capabilities: ML-driven solutions learn from past events in order to continuously improve their threat detection capabilities, threat scoring, clustering and network visualisations. This may involve training the algorithms themselves or adjusting how information is presented based on feedback from analysts.
• Enhance Incident Response:By learning from an analyst's past incident response activities, ML can automate certain aspects of the incident response process, minimizing the time and resources required to address a security breach. This can involve using algorithms to analyze text and evidence, identifying root causes and attack patterns.

Example of an ML-driven Network Security Solution

When it comes to ML-driven Network Detection & Response (NDR) solutions that incorporate the outlined benefits, ExeonTrace stands out as a leading network security solution in Europe. Based on award-winning ML algorithms, which incorporate a decade of academic research, ExeonTrace provides organizations with advanced ML threat detection capabilities, complete network visibility, flexible log source integration and big data analytics. In addition, the algorithms rely on metadata analysis instead of actual payloads which makes them unaffected by encryption, completely hardware-free and compatible with most cybersecurity infrastructures. As a result, ExeonTrace is able to process raw log data into powerful graph databases, which are then analyzed by supervised and unsupervised ML-models. Through correlation and event fusion, the algorithms can accurately pinpoint high-fidelity anomalies and subtle cues of malicious behavior, even when dealing with novel or emerging cyber threats that may lack established signatures or known malicious indicators.

Security Analytics Pipeline: Detection of network anomalies through ML

Conclusion

As the threat of cyber attacks becomes increasingly complex, organizations must go beyond traditional security measures to protect their networks. As a result, many companies are now turning to Machine Learning (ML) and predictive analytics to strengthen their security defenses. In this regard, ML-driven Network Detection & Response (NDR) solutions, such as ExeonTrace, are designed to help organizations stay ahead of the ever-evolving threat landscape. By utilizing advanced ML algorithms that analyze network traffic and application logs, ExeonTrace offers organizations quick detection and response to even the most sophisticated cyberattacks.

ExeonTrace Platform: Network visibility

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