Introduction
In the ever-evolving landscape of cybersecurity, logs, that is information collected from various sources like network devices, endpoints, and applications, plays a crucial role in identifying and responding to threats. By analyzing this data, organizations can detect anomalies, pinpoint malicious activity, and mitigate potential cyberattacks before they cause significant damage. However, the sheer volume and complexity of logs often make them challenging to analyze effectively.
This is where machine learning (ML) comes into play. ML, a subset of artificial intelligence (AI), with its ability to process and analyze large datasets, offers a powerful solution to enhance threat detection capabilities. ML enables faster and more accurate identification of cyberthreats, helping organizations stay ahead of increasingly sophisticated attackers.
At Kaspersky, we have been using ML algorithms in our solutions for close on 20 years, and we have formulated ethical principles for the development and use of AI/ML. We utilize a variety of ML models and methods that are key to automating threat detection, anomaly recognition, and enhancing the accuracy of malware identification. In this post, we will share our experience hunting for new threats by processing Kaspersky Security Network (KSN) global threat data with ML tools to identify subtle new Indicators of Compromise (IoCs). We will also discuss challenges in implementing machine learning and interpreting threat hunting results.
The Kaspersky Security Network (KSN) infrastructure is designed to receive and process complex global cyberthreat data, transforming it into actionable threat intelligence that powers our products. A key source of threat-related data comes from voluntary contributions by our customers. To find out more, read about the < ..
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