Malware is a major threat to present-day computing systems. With the rapid growth of Internet of Things (IoT) devices and their usage in safety critical systems, security has become increasingly important. Securing IoT devices is a challenge for designers, as they are generally resource constrained, which makes real-time software-based malware detection difficult or infeasible. A promising alternative approach is to utilize intrinsic hardware-based malware detectors to alleviate power and performance overheads. In this brief, we introduce a novel Hardware Immune System (HWIS), a stand-alone, hardware-supported malware detection approach for microprocessors that leverages Artificial Immune Systems for detecting botnet activity. This technique is intended for low-power, resource constrained and network facing embedded devices. The proposed model is capable of detecting botnet behavior with an accuracy of 96.7% and F1-score of 0.96. The technique is implemented in hardware and verified using Spartan-7 FPGA. Our technique achieves power, LUTs, FFs, DSPs, and BRAMs utilization overheads of 0.6%, 8.5%, 11.8%, 0%, and 0%, respectively, with no impact on delay using the RISC-V CPU as a baseline.

中文翻译：

---

嵌入式物联网的硬件免疫系统


恶意软件是当今计算系统的主要威胁。随着物联网 (IoT) 设备的快速增长及其在安全关键系统中的使用，安全性变得越来越重要。保护物联网设备的安全对设计人员来说是一个挑战，因为它们通常资源有限，这使得基于软件的实时恶意软件检测变得困难或不可行。一种有前途的替代方法是利用基于硬件的内在恶意软件检测器来减轻功耗和性能开销。在本文中，我们介绍了一种新颖的硬件免疫系统 (HWIS)，这是一种独立的、硬件支持的微处理器恶意软件检测方法，利用人工免疫系统来检测僵尸网络活动。该技术适用于低功耗、资源受限且面向网络的嵌入式设备。所提出的模型能够检测僵尸网络行为，准确率为 96.7%，F1 分数为 0.96。该技术在硬件中实现，并使用 Spartan-7 FPGA 进行验证。我们的技术以 RISC-V CPU 作为基准，分别实现了 0.6%、8.5%、11.8%、0% 和 0% 的功耗、LUT、FF、DSP 和 BRAM 利用率开销，并且对延迟没有影响。