The work will explore how emulated environments—representing networked applications, IoT devices, or cloud infrastructures—can be used to simulate adversarial attacks (e.g., denial-of-service, data exfiltration, privilege escalation) and assess system resilience. The research will include designing a configurable emulation environment, integrating attack simulation tools or AI-based attack models, and conducting experiments on representative case studies. Evaluation will focus on the realism of emulation, the variety of attacks covered, and the usefulness of generated insights for system hardening. The expected contribution is a methodology and prototype platform for testing and analyzing cybersecurity resilience through system emulation and attack simulation. Work to be done:
- Literature Review: Explore existing emulation platforms (e.g., Mininet, GNS3, QEMU), attack simulation tools (e.g., Metasploit, Caldera, Atomic Red Team), and AI-based attack modeling.
- System Selection: Choose representative systems to emulate (e.g., IoT gateway, cloud microservices, industrial control systems).
- Emulation Setup: Create a configurable testbed that can replicate the selected system’s architecture and behavior.
- Attack Design: Define a set of attacks to simulate (e.g., DDoS, SQL injection, data exfiltration, lateral movement).
- Integration: Develop a mechanism to inject attacks into the emulation environment and collect logs/metrics.
- Experiments: Run attack simulations under different configurations to measure resilience, detection, and recovery.
- Evaluation Metrics: Define KPIs such as attack success rate, detection time, system downtime, and resource overhead.
- Analysis & Recommendations: Propose improvements to system design or defense strategies based on findings.