A hybrid deep learning-based intrusion detection system for IoT networks.

The Internet of Things (IoT) is a rapidly evolving technology with a wide range of potential applications, but the security of IoT networks remains a major concern. The existing system needs improvement in detecting intrusions in IoT networks. Several researchers have focused on intrusion detection systems (IDS) that address only one layer of the three-layered IoT architecture, which limits their effectiveness in detecting attacks across the entire network. To address these limitations, this paper proposes an intelligent IDS for IoT networks based on deep learning algorithms. The proposed model consists of a recurrent neural network and gated recurrent units (RNN-GRU), which can classify attacks across the physical, network, and application layers. The proposed model is trained and tested using the ToN-IoT dataset, specifically collected for a three-layered IoT system, and includes new types of attacks compared to other publicly available datasets. The performance analysis of the proposed model was carried out by a number of evaluation metrics such as accuracy, precision, recall, and F1-measure. Two optimization techniques, Adam and Adamax, were applied in the evaluation process of the model, and the Adam performance was found to be optimal. Moreover, the proposed model was compared with various advanced deep learning (DL) and traditional machine learning (ML) techniques. The results show that the proposed system achieves an accuracy of 99% for network flow datasets and 98% for application layer datasets, demonstrating its superiority over previous IDS models.

Building towards Automated Cyberbullying Detection: A Comparative Analysis.

The increased use of social media among digitally anonymous users, sharing their thoughts and opinions, can facilitate participation and collaboration. However, this anonymity feature which gives users freedom of speech and allows them to conduct activities without being judged by others can also encourage cyberbullying and hate speech. Predators can hide their identity and reach a wide range of audience anytime and anywhere. According to the detrimental effect of cyberbullying, there is a growing need for cyberbullying detection approaches. In this survey paper, a comparative analysis of the automated cyberbullying techniques from different perspectives is discussed including data annotation, data preprocessing, and feature engineering. In addition, the importance of emojis in expressing emotions as well as their influence on sentiment classification and text comprehension leads us to discuss the role of incorporating emojis in the process of cyberbullying detection and their influence on the detection performance. Furthermore, the different domains for using self-supervised learning (SSL) as an annotation technique for cyberbullying detection are explored.