We investigate lightweight techniques for detecting common SYN atttacks on devices that are attachd to the Internet, such as IoT devices and gateways, Fog servers or edge devices which may have low processing capacity. In particular we examine the Random Neural Network with Deep Learning, trained with "normal" non-attack traffic, and a Long-Short-Term-Memory (LSTM) neural network. Using the same traffic traces for attack traffic, our experiments show that the Random Neurla Network provides substantially better attack detection and significantly lower false alarm rates as compared to the LSTM network.
The purpose of this article is to present a framework for capturing and analyzing social media posts using a sentiment analysis tool to determine the views of the general public towards autonomous mobility. The paper presents the systems used and the results of this analysis, which was performed on social media posts from Twitter and Reddit. To achieve this, a specialized lexicon of terms was used to query social media content from the dedicated application programming interfaces (APIs) that the aforementioned social media platforms provide. The captured posts were then analyzed using a sentiment analysis framework, developed using state-of-the-art deep machine learning (ML) models. This framework provides labeling for the captured posts based on their content (i.e., classifies them as positive or negative opinions). The results of this classification were used to identify fears and autonomous mobility aspects that affect negative opinions. This method can provide a more realistic view of the general public’s perception of automated mobility, as it has the ability to analyze thousands of opinions and encapsulate the users’ opinion in a semi-automated way.
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