The modern trend of moving artificial intelligence computation near to the origin of data sources has increased the demand for new hardware and software suitable for such environments. We carried out a scoping study to find the current resources used when developing Edge AI applications. Due to the nature of the topic, the research combined scientific sources with product information and software project sources. The paper is structured as follows. In the first part, Edge AI applications are briefly discussed followed by hardware options and finally, the software used to develop AI models is described. There are various hardware products available, and we found as many as possible for this research to identify the best-known manufacturers. We describe the devices in the following categories: artificial intelligence accelerators and processors, field-programmable gate arrays, system-on-a-chip devices, system-on-modules, and full computers from development boards to servers. There seem to be three trends in Edge AI software development: neural network optimization, mobile device software and microcontroller software. We discussed these emerging fields and how the special challenges of low power consumption and machine learning computation are being taken into account. Our findings suggest that the Edge AI ecosystem is currently developing, and it has its own challenges to which vendors and developers are responding.
Rinnakkaistallennettu versio voi erota alkuperäisestä julkaistusta sivunumeroiltaan ja ilmeeltään.Abstract. The number of intrusions and attacks against data networks and networked systems increases constantly, while encryption has made it more difficult to inspect network traffic and classify it as malicious. In this paper, an anomaly-based intrusion detection system using Haar wavelet transforms in combination with an adversarial autoencoder was developed for detecting malicious TLS-encrypted Internet traffic. Data containing legitimate, as well as advanced malicious traffic was collected from a large-scale cyber exercise and used in the analysis. Based on the findings and domain expertise, a set of features for distinguishing modern malware from packet timing analysis were chosen and evaluated. Performance of the adversarial autoencoder was compared with a traditional autoencoder. The results indicate that the adversarial model performs better than the traditional autoencoder. In addition, a machine learning pipeline capable of analyzing traffic in near real time was developed for data analysis.
This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections.
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