<span id="docs-internal-guid-345787a5-7fff-6d93-73dd-f99a81d82f61"><span>The massive network traffic data between connected devices in the internet of things have taken a big challenge to many traditional intrusion detection systems (IDS) to find probable security breaches. However, security attacks lean towards unpredictability. There are numerous difficulties to build up adaptable and powerful IDS for IoT in order to avoid false alerts and ensure a high recognition precision against attacks, especially with the rising of Botnet attacks. These attacks can even make harmless devices becoming zombies that send malicious traffic and disturb the network. In this paper, we propose a new IDS solution, baptized BotIDS, based on deep learning convolutional neural networks (CNN). The main interest of this work is to design, implement and test our IDS against some well-known Botnet attacks using a specific Bot-IoT dataset. Compared to other deep learning techniques, such as simple RNN, LSTM and GRU, the obtained results of our BotIDS are promising with 99.94% in validation accuracy, 0.58% in validation loss, and the prediction execution time is less than 0.34 ms.</span></span>

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<p>Deep learning (DL) models are nowadays broadly applied and have shown outstanding performance in a variety of fields, including our focus topic of "IoTcybersecurity". Deep learning-based intrusion detection system (DL-IDS) models are more fixated and depended on the trained dataset. This poses a problem for these DL-IDS, especially with the known mutation and behavior changes of attacks, which can render them undetected. As a result, the DL-IDShas become outdated. In this work, we present a solution for updating DL-ID Semploying a transfer learning technique that allows us to retrain and fine-tune pre-trained models on small datasets with new attack behaviors. In our experiments, we built CNN-based IDS on the Bot-IoT dataset and updated it on small data from a new dataset named TON-IoT. We obtained promising results in multiple metrics regarding the detection rate and the training between the initial training for the original model and the updated one, in the matter of detecting new attacks behaviors and improving the detection rate for some classes by overcoming the lack of their labeled data.</p>

Multimedia applications vary radically from the traditional applications of data transfers such as email or file transfer. Indeed, these applications generally concern only two users: a source and a destination. In addition, the transmission delays do not influence the service. On the contrary, multimedia applications can imply more than two users (a videoconference for 100 people for example). In addition, these applications need short delays and flow guarantees in order to ensure a continuous playback of the multimedia flow. However, the current networks generally provide only a service, which is "at best" and point-to-point. Particular protocols and mechanisms must be developed at both the transport and application levels. Some mechanisms already exist: they enable the applications, using the multicast over Internet, to operate generally in satisfactory conditions. Hence, it becomes necessary to provide guarantees in terms of quality of service for this type of multimedia application.The quality of service (QoS) refers to the way a packet is delivered. Thus, it is defined by the following parameters:-the delay, which characterizes the end-to-end transfer time; -the jitter, which represents the variation of the communication delay;Chapter written by Abderrahim BENSLIMANE and Omar MOUSSAOUI.52 Multimedia Multicast on the Internet -the bandwidth, which corresponds to the possible throughput between two end entities; it is limited by the throughput of traveled physical links, but also by the concurrent flows and the equipment capacity; -the reliability, which is the average error ratio of various communication supports and equipment.Several studies were suggested in order to guarantee a certain QoS [BRA 97], or at least to differentiate the services [BLA 98]. However, there is still a lot left to be done in order to deploy the multicast protocols while taking into account QoS.Multicast routing algorithms and protocols have been largely studied in the literature [BAL 97, BIS 00, DEE 99, DID 97, EST 95, EST 98]. However, most of them are not scalable and do not manage the QoS efficiently. In addition, there were several studies advocating the importance of QoS in multicast routing [CAR 97, CHE 00, FAL 98, SRI 98]. These works mainly deal with the research of paths from the new members towards the multicast tree by considering the parameters of QoS, which is done by taking into account either a single path or multiple paths. However, with a single path, QoS is not necessarily considered.On the other hand, with multiple paths, there is an overloading of the network (i.e. reservation cost, flooding), which does not enable scalability. The development of multicast routing sensitive to QoS drew less attention, even though it is indispensable for multimedia applications. As for the scalability of multicast routing protocols, the solutions based on "hierarchical trees" are very promising [THY 95, HOF 96, PRA 01]. These protocols decompose the global multicast group into separated sub-groups. Each group consists of partic...

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<span lang="EN-US">The impact of wildfires, even following the fire's extinguishment, continues to affect harmfully public health and prosperity. Wildfires are becoming increasingly frequent and severe, and make the world's biodiversity in a growing serious danger. The fires are responsible for negative economic consequences for individuals, corporations, and authorities. Researchers are developing new approaches for detecting and monitoring wildfires, that make use of advances in computer vision, machine learning, and remote sensing technologies. IoT sensors help to improve the efficiency of detecting active forest fires. In this paper, we propose a novel approach for predicting wildfires, based on machine learning. It uses a regression model that we train over NASA's fire information for resource management system (FIRMS) dataset to predict fire radiant power in megawatts. The analysis of the obtained simulation results (more than 99% in the R2 metric) shows that the ensemble learning model is an effective method for predicting wildfires using an IoT device equipped with several sensors that could potentially collect the same data as the FIRMS dataset, such as smart cameras or drones.</span>

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The Internet of Things (IoT) is now present in every domain from applications in smart homes, Smart Cities, Industrial Internet of Things (IIoT), such as e-Health, and beyond. The wide use of Internet of Things is making its security a real concern. Techniques based on artificial intelligence (AI) and its subsets machine learning (ML) and deep learning (DL) are commonly used to develop a secure Intrusion Detection System (IDS) for IoT. Researchers and industrialists are commonly using commercial Internet of Things devices, broadly available on the market. In this paper, we present an analysis of the possibility to deploy a Deep Learning-Based Host-Intrusion Detection System (DL-HIDS) on some specific commercial IoT devices. We performed multiple optimizations regarding the types of our used devices to meet their limited hardware specifications. In our conducted analysis, we consider such criteria, as memory consumption and inference timing (attacks prediction timing), to conclude which model fits better to our proposed lightweight DL-HIDS for each studied device, and to anticipate about which IDS we must generate and expectedly deploy based on the characteristics of the devices we possess. The paper also discusses the proposed methodology for such deployment in a real IoT environment. The obtained results about the implementation of our DL-HIDS on different considered devices (up to 99.74% in accuracy and an inference of not more of 1µs for attacks prediction) are promising and prove that we can manage to install a suited IDS for each device, but it should be minutiously supported by a central IDS in fog or cloud layers.