SPECTRE: A Hybrid System for an Adaptative and Optimised Cyber Threats Detection, Response and Investigation in Volatile Memory
Arslan Tariq Syed, Mohamed Chahine Ghanem, Elhadj Benkhelifa, and Fauzia Idrees Abro

TL;DR
SPECTRE is a modular system that enhances cyber threat detection, investigation, and visualization by analyzing volatile memory, emulating attack scenarios, and integrating with existing forensic tools for improved response to sophisticated threats.
Contribution
Introduces SPECTRE, a comprehensive, modular platform that combines memory forensics, emulation, and visualization to improve detection and response to advanced cyber threats.
Findings
Effective detection of file-less malware techniques.
Seamless integration with existing DFIR tools.
Enhanced visualization for threat analysis.
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…
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