Mitigating side channel attacks on FPGA through deep learning and dynamic partial reconfiguration.

Area of Science:

• Computer Engineering
• Cybersecurity
• Artificial Intelligence

Background:

• Traditional static defenses are insufficient against adaptive Side Channel Attacks (SCAs).
• Field Programmable Gate Arrays (FPGAs) are vulnerable to SCAs due to their reconfigurable nature.
• Dynamic adaptation is crucial for effective real-time hardware security.

Purpose of the Study:

• To introduce a framework combining Deep Learning (DL) and Dynamic Partial Reconfiguration (DPR) for FPGA security.
• To demonstrate adaptive mitigation of power side-channel attacks in operational FPGAs.
• To establish a proactive defense mechanism against evolving hardware threats.

Main Methods:

• Integration of DL models for threat analysis and DPR for real-time hardware reconfiguration.
• Dynamic reconfiguration of FPGA resources to disrupt SCA patterns.
• Focus on power side-channel attack mitigation with potential extension to other SCA types.

Main Results:

• Achieved real-time detection and mitigation of SCAs within 20 clock cycles.
• Demonstrated resilience against power side-channel attacks through adaptive countermeasures.
• Showcased the framework's ability to operate effectively on live FPGA designs.

Conclusions:

• The DL-DPR framework offers a paradigm shift towards proactive hardware security.
• Combining AI and FPGA technologies redefines adaptive security mechanisms for hardware systems.
• The proposed methodology significantly enhances FPGA design resilience and paves the way for future adaptive security research.