Advanced persistent threat detection through multi-modal behavioral analysis

Area of Science:

• Cybersecurity
• Artificial Intelligence
• Machine Learning

Background:

• Advanced Persistent Threats (APTs) exhibit behaviors similar to insider threats during lateral movement and data exfiltration.
• Traditional detection mechanisms struggle against sophisticated APTs due to their stealth and evasion tactics.

Purpose of the Study:

• To develop a novel machine learning approach for simulating and detecting APT behaviors using insider threat data.
• To leverage AI-augmented analytics for enhanced cybersecurity threat detection.

Main Methods:

• Utilized the CERT Insider Threat Dataset to simulate APT patterns.
• Integrated multi-modal data analysis, language model-driven behavioral understanding, and advanced machine learning.
• Developed a multi-agent language model for log analysis, temporal sequence modeling, and deep evidential clustering for uncertainty-aware detection.

Main Results:

• Achieved 96.3% detection accuracy for APT behaviors.
• Reduced false positives by 42% compared to state-of-the-art methods.
• Successfully simulated realistic APT scenarios and provided interpretable explanations via natural language generation.

Conclusions:

• The proposed AI-driven methodology effectively simulates and detects APTs by learning from insider threat data, addressing a critical gap in cybersecurity.
• This approach enhances APT detection capabilities for organizations, particularly those with limited APT-specific training data and resource constraints.
• The system offers practical deployment guidelines for enterprise security operations and threat hunting teams.