SPECTRE: A Hybrid System for an Adaptative and Optimised Cyber Threats Detection, Response and Investigation in Volatile Memory

2025-01-07

AI Summary

To address the challenges of detecting fileless malware—particularly memory-resident threats—and enabling timely response, this paper proposes an adaptive threat detection and response system tailored for volatile memory. Methodologically, it introduces the first Volatility JSON standardization layer for seamless integration with DFIR tools; combines memory snapshot analysis, lightweight security emulation (e.g., credential dumping), hybrid rule- and statistics-based anomaly detection, and cross-domain correlation of multi-source threat intelligence (IPs, geolocation, VirusTotal, GeoIP); and implements a D3.js-driven interactive visualization platform for forensic provenance tracing. Contributions include: (1) significantly improved detection rates for fileless malware; (2) over 60% reduction in memory forensics cycle time; and (3) real-time threat attribution and tactical red-teaming simulation support. The system has been validated in multiple blue-team training and red-blue teaming platforms.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Security and Privacy: Cyber-crime defenses and forensics

Abstract

The increasing sophistication of modern cyber threats, particularly file-less malware relying on living-off-the-land techniques, poses significant challenges to traditional detection mechanisms. Memory forensics has emerged as a crucial method for uncovering such threats by analysing dynamic changes in memory. This research introduces SPECTRE (Snapshot Processing, Emulation, Comparison, and Threat Reporting Engine), a modular Cyber Incident Response System designed to enhance threat detection, investigation, and visualization. By adopting Volatility JSON format as an intermediate output, SPECTRE ensures compatibility with widely used DFIR tools, minimizing manual data transformations and enabling seamless integration into established workflows. Its emulation capabilities safely replicate realistic attack scenarios, such as credential dumping and malicious process injections, for controlled experimentation and validation. The anomaly detection module addresses critical attack vectors, including RunDLL32 abuse and malicious IP detection, while the IP forensics module enhances threat intelligence by integrating tools like Virus Total and geolocation APIs. SPECTRE advanced visualization techniques transform raw memory data into actionable insights, aiding Red, Blue and Purple teams in refining strategies and responding effectively to threats. Bridging gaps between memory and network forensics, SPECTRE offers a scalable, robust platform for advancing threat detection, team training, and forensic research in combating sophisticated cyber threats.

Problem

• Cybersecurity
• Memory-resident Malware
• Traceless Threats

Innovation

• Memory Forensics
• Dynamic Threat Detection
• Integrated Cybersecurity Platform