From Data Overload to Real-Time Security
A leading telecommunications provider faced significant challenges in detecting Advanced Persistent Threats (APT) with vast amounts of security data.
Skillfield implemented a Machine Learning (ML) and Big Data (BD) driven solution using Databricks to enhance threat detection capabilities. By structuring unstructured data logs, developing ML models, and integrating causation analysis, the client achieved real-time threat identification and response.
This resulted in improved accuracy, reduced false positives and enhanced proactive security monitoring.
The Problem
Our client, a leader in the telecommunications industry, struggled with the overwhelming volume of security data generated across their digital infrastructure.
Advanced Persistent Threats (APTs), which are often stealthy, complex and prolonged, posed a severe security risk.
Existing tools provided only basic log processing, failing to offer the advanced analytics required for real-time APT detection.
The client needed a solution capable of filtering through massive datasets, accurately distinguishing real threats from false positives, and providing actionable insights.
Without a robust detection system, the organisation faced prolonged exposure to sophisticated cyber-attacks, increasing the potential for financial and reputational damage.
The Solution
Skillfield partnered with the client to develop an advanced Machine Learning (ML) and Big Data (BD) solution, leveraging Databricks for scalable data processing.
The project began with constructing data pipelines to normalise and structure security logs for ML analysis. Custom unsupervised learning models were designed to identify APT-related behaviours, such as lateral movement, unusual communication channels, or data exfiltration attempts. Causation analysis was implemented to trace threats back to their source, providing actionable intelligence for remediation.
In addition, trend monitoring features were implemented to detect anomalous behaviours over time, such as irregular device activity or unexpected traffic surges, which are often early indicators of APT activity.
The Outcome
Implementing Skillfield’s ML-driven solution enabled the client to identify and respond to APTs with significantly improved accuracy and speed.
APTs were detected early, minimising the prolonged exposure and reducing security risks. The system provided security analysts detailed insights into affected devices, hosts and services, enabling swift triaging and mitigation.
By significantly reducing noise and false positives, security teams could focus on genuine threats, improving operational efficiency. Trend monitoring capabilities empowered the organisation to identify early warning signs of cyber threats, strengthening its overall security posture.
The adoption of Databricks ensured scalability and adaptability, equipping the client with a future-proof solution to combat evolving cyber threats
