Anomaly-Based Intrusion Detection Model Using Deep Learning for IoT Networks

The rapid growth of Internet of Things (IoT) devices has brought numerous benefits to the interconnected world. However, the ubiquitous nature of IoT networks exposes them to various security threats, including anomaly intrusion attacks. In addition, IoT devices generate a high volume of unstructured data. Traditional intrusion detection systems often struggle to cope with the unique characteristics of IoT networks, such as resource constraints and heterogeneous data sources. Given the unpredictable nature of network technologies and diverse intrusion methods, conventional machine-learning approaches seem to lack efficiency. Across numerous research domains, deep learning techniques have demonstrated their capability to precisely detect anomalies. This study designs and enhances a novel anomaly-based intrusion detection system (AIDS) for IoT networks. Firstly, a Sparse Autoencoder (SAE) is applied to reduce the high dimension and get a significant data representation by calculating the reconstructed error. Secondly, the Convolutional Neural Network (CNN) technique is employed to create a binary classification approach. The proposed SAE-CNN approach is validated using the Bot-IoT dataset. The proposed models exceed the performance of the existing deep learning approach in the literature with an accuracy of 99.9%, precision of 99.9%, recall of 100%, F1 of 99.9%, False Positive Rate (FPR) of 0.0003, and True Positive Rate (TPR) of 0.9992. In addition, alternative metrics, such as training and testing durations, indicated that SAE-CNN performs better

Anomaly intrusion detection systems in IoT using deep learning techniques: a survey

Security has a major role to play in the utilization and operations of the internet of things (IoT). Several studies have explored anomaly intrusion detection and its utilization in a variety of applications. Building an effective anomaly intrusion detection system requires researchers and developers to comprehend the complex structure from noisy data, identify the dynamic anomaly patterns, and detect anomalies while lacking sufficient labels. Consequently, improving the performance of anomaly detection requires the use of advanced deep learning techniques instead of traditional shallow learning approaches. The large number of devices connected to IoT which massively generate a large amount of data require large computation as well. This study presents a survey on anomaly intrusion detection using deep learning approaches with emphasis on resource-constrained devices used in real-world problems in the realm of IoT. The findings from the reviewed studies showed that deep learning is superior to detect anomaly in terms of high detection accuracy and false alarm rate. However, it is highly recommended to conduct further studies using deep learning techniques for robust IDS

Metaverse Framework: A Case Study on E-Learning Environment (ELEM)

Metaverse is a vast term that can contain every digital thing in the future. Therefore, life domains, such as learning and education, should have their systems redirected to adopt this topic to keep their availability and longevity. Many papers have discussed the metaverse, the applications to run on, and the historical progress to have the metaverse the way it is today. However, the framework of the metaverse itself is still unclear, and its components cannot be exactly specified. Although E-Learning systems are a need that has developed over the years along with technology, the structures of the available E-Learning systems based on the metaverse are either not well described or are adopted, in their best case, as just a 3D environment. In this paper, we examine some previous works to find out the special technologies that should be provided by the metaverse framework, then we discuss the framework of the metaverse if applied as an E-Learning environment framework. This will make it easy to develop future metaverse-based applications, as the proposed framework will make the virtual learning environments work smoothly on the metaverse. In addition, E-Learning will be a more interactive and pleasant process