The Future of Network Security: Predictive Analytics and ML-Driven Solutions

Feb 21, 2023The Hacker NewsNetwork Security / Machine Learning

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time of an attack or are quickly outdated.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)^[1]
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^[2]
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^[3]
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

Book a free
demo^[4] to discover how
ExeonTrace leverages ML algorithms to make your organisation more
cyber resilient – quickly, reliable and completely
hardware-free.