pAElla：基于边缘AI的数据中心实时的恶意软件检测
pAElla: Edge-AI based Real-Time Malware Detection in Data Centers
摘要：
物联网（IoT）设备越来越多地用于监控各种应用程序，以及它们经常需要用于数据分析的“大数据”流支持的挑战，现在正在推动人们越来越关注新兴的边缘计算范式。特别是，直接在网络边缘管理和分析数据的智能方法越来越多地被研究，人工智能（AI）驱动的边缘计算被认为是一个有前途的方向。在本文中，我们将重点关注数据中心（DC）和超级计算机（SC），其中部署了新一代高分辨率监控系统，为异常检测和安全性等分析提供了新的机会，但也为处理其产生的大量数据带来了新的挑战。详细地说，我们报告了一种新的轻量级和可扩展的方法来提高DC/SC的安全性，该方法涉及高分辨率功耗的AI驱动的边缘计算。该方法被称为pAElla，目标是实时的【形】恶意软件检测（MD），它运行在DC/SC的带外物联网监控系统上，并涉及功率测量的功率谱密度以及AutoEncoders。结果令人鼓舞，F1分数接近1，误报率和恶意软件未命中率接近0%。我们将我们的方法与最先进的MD技术进行了比较，并表明，在DC/SC的背景下，pAEella可以覆盖更广泛的恶意软件，在准确性方面显著优于SoA方法。此外，我们提出了一种适合生产中DC/SC的在线培训方法，并发布了开放的数据集和代码。
英文摘要：
The increasing use of Internet-of-Things (IoT) devices for monitoring a wide spectrum of applications, along with the challenges of "big data" streaming support they often require for data analysis, is nowadays pushing for an increased attention to the emerging edge computing paradigm. In particular, smart approaches to manage and analyze data directly on the network edge, are more and more investigated, and Artificial Intelligence (AI) powered edge computing is envisaged to be a promising direction. In this paper, we focus on Data Centers (DCs) and Supercomputers (SCs), where a new generation of high-resolution monitoring systems is being deployed, opening new opportunities for analysis like anomaly detection and security, but introducing new challenges for handling the vast amount of data it produces. In detail, we report on a novel lightweight and scalable approach to increase the security of DCs/SCs, that involves AI-powered edge computing on high-resolution power consumption. The method -- called pAElla -- targets real-time Malware Detection (MD), it runs on an out-of-band IoT-based monitoring system for DCs/SCs, and involves Power Spectral Density of power measurements, along with AutoEncoders. Results are promising, with an F1-score close to 1, and a False Alarm and Malware Miss rate close to 0%. We compare our method with State-of-the-Art MD techniques and show that, in the context of DCs/SCs, pAElla can cover a wider range of malware, significantly outperforming SoA approaches in terms of accuracy. Moreover, we propose a methodology for online training suitable for DCs/SCs in production, and release open dataset and code.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
