pAElla: Edge AI-Based Real-Time Malware Detection in Data Centers

Abstract

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.

Artificial intelligence Internet of Things: A new paradigm of distributed sensor networks

“…AIoT security for smart cities, 68 AIoT and intrusion detection, 69,70 AIoT and trust recommendation, 71 other works on security in AIoT [72][73][74][75][76][77] The convergence of architectures, techniques, and platforms for AIoT…”

“…Jia et al 76 proposed an approach termed as FlowGuard to defend against IoT DDoS attacks. Libri et al 77 proposed an approach termed as pAElla for real-time malware detection in edge IoT devices.…”

Monte Cimone: Paving the Road for the First Generation of RISC-V High-Performance Computers

“…In addition to libraries and tools for HPC application deployment, a production-ready HPC system must support fine-grain utilization, performance and power monitoring of the computing resources to enable efficient computing, power, thermal management and anomaly detection for reliability. Recently, several works have been proposed to extend the power monitoring attainable from the voltage regulator modules leveraging shunt resistors, current probes, and out-ofband telemetry [23]. In addition, Operational Data Analytics [26] (ODA) has been introduced focusing on monitoring and managing large scale HPC installations.…”

Büyük Veri Ortamlarında Zararlı Yazılım Tespiti Kapsamında Makine Öğrenmesi Algoritmalarının Performansının İncelenmesi

“…Sıfırıncı gün zararlı yazılımlarının yüksek 1539 doğrulukla tespiti ve sınıflandırılmasını amaçlayan çalışmada [23], statik ve dinamik tekniklerin birleştirilerek etkin şekilde kullanıldığı bir büyük veri çerçevesi önerilmiştir, en iyi sınıflandırma destek vektör makineleri ile sağlanmıştır. Libri ve diğerleri [24] tarafından veri merkezlerinin ve süper bilgisayarların güvenliği için, bant dışı IoT tabanlı izleme sistemleri üzerinde çalışan, gerçek zamanlı zararlı yazılım tespiti yapabilen, hafif ve ölçeklenebilir bir yaklaşım olan pAElla sunulmuş ve anomali tespitine uygun bir sinir ağı olan otokodlayıcı ile en iyi tespit performansı elde edilmiştir.…”