How is machine learning used in threat detection?

Machine learning (ML) is widely used in threat detection to identify patterns, anomalies, and malicious activities that traditional rule-based systems might miss. By analyzing large volumes of data, ML models can learn from historical threats and adapt to new, evolving attack techniques.

How ML is Used in Threat Detection:

1. Anomaly Detection – ML models establish a baseline of normal behavior (e.g., network traffic, user activity) and flag deviations that could indicate attacks like insider threats or unauthorized access.

   • Example: If a user suddenly accesses sensitive files at unusual hours, an ML model can detect this anomaly and trigger an alert.

2. Malware Detection – ML algorithms analyze file characteristics, code patterns, and behavior to identify malware, including zero-day threats that signature-based systems can’t detect.

   • Example: A model trained on thousands of malware samples can detect suspicious executable files by their structure, even if they haven’t been seen before.

3. Phishing & Fraud Detection – Natural Language Processing (NLP) and ML classify suspicious emails, links, or login attempts by analyzing language patterns, sender behavior, and metadata.

   • Example: An ML system can detect phishing emails by spotting unusual phrasing, fake domains, or mismatched sender addresses.

4. Network Intrusion Detection – ML monitors network traffic in real-time to spot unusual data flows, port scans, or DDoS attacks.

   • Example: A model can identify a botnet attack by detecting a sudden spike in requests from multiple IP addresses.

5. User & Entity Behavior Analytics (UEBA) – ML tracks user activity over time to detect compromised accounts or insider threats.

   • Example: If an employee’s login behavior changes (e.g., accessing different systems than usual), ML can flag it for investigation.

Recommended Tencent Cloud Services for Threat Detection:

• Tencent Cloud Host Security (HSM) – Uses ML to detect malware, vulnerabilities, and abnormal processes on servers.
• Tencent Cloud T-Sec Network Intrusion Protection System (NIPS) – Employs ML to identify and block network-based attacks.
• Tencent Cloud T-Sec Anti-DDoS – Leverages ML to distinguish between legitimate traffic and DDoS attacks.
• Tencent Cloud T-Sec Security Intelligence – Provides threat intelligence and ML-driven anomaly detection for cloud environments.

By continuously learning from new data, ML improves threat detection accuracy over time, reducing false positives and enhancing security response.