Performance of the compute-intensive multichip platforms such as micro-servers and embedded systems are limited by the latency and power hungry chip-to-chip interconnections. Millimeter wave (mm-wave) wireless interconnection networks have emerged as an energy-efficient and low-latency solution for such multichip system communication. We refer such multichip systems with in-package mm-wave wireless interconnect as Wireless Network-in-Package (WiNiP). Despite providing performance enhancements, wireless channel, being an unguided medium, introduces potential security vulnerabilities inherited from traditional wireless networks such as jamming induced Denial-of-Service (DoS) and eavesdropping. Securing the systems against such induced threats often introduce large overheads and performance penalties. To address these challenges, we propose a WiNiP architecture that reuses the in-built Design for Testability (DFT) hardware for securing against external and Hardware Trojans (HT) induced internal attacks. The proposed architecture is capable of securing against adversaries with a reconfigurable wireless interconnection (AWARe-Wi). We deploy machine learning (ML) classifier to detect the threats. In addition, for a robust threat detection, we introduce an Adversarial ML (AML)-based approach in this work. To enable sustainable multichip communication in such systems even under jamming attack from both internal and external attackers, we design a reconfigurable Medium Access Control (MAC) and a suitable communication protocol. The simulation results show that, the ML and AML classifiers can achieve an accuracy of 99.87% and 95.95% respectively for attack detection while the proposed WiNiP can sustain chip-to-chip communication even under persistent jamming attack with an average 1.44× and 1.56× degradation in latency for internal and external attacks respectively for application-specific traffic patterns.

诸如微服务器和嵌入式系统之类的计算密集型多芯片平台的性能受到延迟和耗电的芯片到芯片互连的限制。毫米波（mm-wave）无线互连网络已经成为这种多芯片系统通信的一种节能，低延迟的解决方案。我们将具有封装内毫米波无线互连的这种多芯片系统称为无线封装内网络（WiNiP）。尽管提供了性能增强，但无线信道作为一种非指导性介质，仍会引入从传统无线网络继承的潜在安全漏洞，例如，干扰引起的拒绝服务（DoS）和窃听。保护系统免受此类诱发的威胁通常会带来大量开销和性能损失。为了应对这些挑战，我们提出了一种WiNiP架构，该架构可重用内置的可测性设计（DFT）硬件，以抵御外部和硬件特洛伊木马（HT）引起的内部攻击。所提出的体系结构能够通过可重新配置的无线互连（AWARe-Wi）来防御对手。我们部署机器学习（ML）分类器来检测威胁。此外，为了进行可靠的威胁检测，我们在这项工作中引入了基于对抗性ML（AML）的方法。为了即使在内部和外部攻击者的拥塞攻击下也能在此类系统中实现可持续的多芯片通信，我们设计了可重新配置的媒体访问控制（MAC）和合适的通信协议。仿真结果表明，ML和AML分类器可以达到99.87％和95的精度。