arXiv:2606.18190v1 Announce Type: cross Abstract: Multi-stage cyberattacks span system, network, and browser logs. Detecting them requires correlating events across all three sources. Machine learning methods can learn these cross-source patterns, but they need labeled multi-source data. Existing public datasets fall short. Network-only datasets such as CICIDS and UNSW-NB15 miss host and browser activity. Host-focused datasets such as LMDG and CICAPT-IIoT lack browser telemetry. ATLAS includes all three sources but labels events only as malicious or benign, without MITRE Adversarial Tactics, T
The increasing sophistication of multi-stage cyberattacks necessitates more advanced AI-driven detection methods, which in turn require comprehensive, labeled datasets. This work addresses a critical gap in current cybersecurity research and development.
Improved multi-source threat detection capabilities, particularly those leveraging AI and MITRE ATT&CK labeling, are crucial for national security, critical infrastructure protection, and the resilience of digital-first economies.
This new dataset and evaluation method will accelerate the development of more robust AI models for complex cyberattack detection, potentially shifting the advantage slightly towards defenders in the cybersecurity landscape.
- · Cybersecurity firms
- · National security agencies
- · Critical infrastructure operators
- · AI/ML researchers in security
- · Sophisticated cyber attackers
- · Organizations with outdated security systems
Security operations centers will gain more effective tools for identifying and responding to advanced persistent threats.
Reduced incidence and impact of major data breaches and cyber espionage campaigns due to proactive detection.
A potential increase in the cost and complexity for adversaries to launch successful multi-stage cyberattacks, driving them towards new exploit vectors.
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