The Internet of things (IoT) is a new ubiquitous technology that relies on heterogeneous devices and protocols. The IoT technologies are expected to offer a new level of connectivity thanks to its smart devices able to enhance everyday tasks and facilitate smart decisions based on sensed data. The IoT could collect sensitive data and should be able to face attacks and privacy issues. The IoT security issue is a hot topic of research and industrial concern. Indeed, threats against IoT devices and services could cause security breaches and data leakage. Aiming to identify attempts to abuse the IoT systems and mitigate malicious events, this paper studied the Intrusion Detection Systems (IDS) based on Machine Learning (ML) techniques. The ML approach could provide good tools to detect novel intrusion activities in a timely manner. This paper, therefore, highlighted the related issues to develop secured and efficient IoT services. It tried to allow a comprehensive review of IoT features and design. It mainly focused on intrusion detection based on the machine learning schema and built a taxonomy of different IoT attacks and threats. This paper also compared between the different intrusion detection techniques and established a taxonomy of machine leaning methods for intrusion detection solutions.
International audienceThe Quantized Congestion Notification (QCN) is a Layer 2 congestion control scheme for Carrier Ethernet data center networks. The QCN has been standardized as an IEEE 802.1Qau Ethernet Congestion Notification standard. This paper report a results of a QCN study with multicast traffic and proposes an enhancement to the QCN. In fact, in order to be able to scale up, the feedback implosion problem has to be solved. Therefore, we resorted to the representative technique, which uses a selected congestion point (i.e., the overloaded queue in a switch), to provide timely and accurate feedback on behalf of the congested switches in the path of multicast traffic. This paper evaluates the rate variation, the feedback overhead, the loss rate, the stability, the fairness, and the scalability performance of the standard QCN with multicast traffic and the enhanced QCN for multicast traffic. This paper also compares their performance criteria. The evaluation results show that the enhanced proposition of the QCN for multicast traffic gives better results than the standard QCN with multicast traffic. Indeed, the feedback implosion problem is settled by decreasing remarkably the feedback rate
A multicast congestion control scheme is an interesting feature to control group communication applications such as teleconferencing tools and information dissemination services. This paper addresses a comparison between multiple unicast and multicast traffic congestion control for Carrier Ethernet. In this work, we proposed to study the Quantized Congestion Notification (QCN), which is a Layer 2 congestion control scheme, in the case of multicast traffic and multiple unicast traffic. Indeed, the QCN has recently been standardized as the IEEE 802.1Qau Ethernet Congestion Notification standard. This scheme is evaluated through simulation experiments, which are implemented by the OMNeT++ framework. This paper evaluates the Reaction Point (RP) start time congestion detection, feedback rate, loss rate, stability, fairness and scalability performance of the QCN for multicast traffic transmission and multiple unicast traffic transmission. This paper also draws a parallel between QCN for multicast traffic transmission and that for multiple unicast traffic transmission. Despite the benefit of integrating the multicast traffic, results show that performance could degrade when the network scales up. The evaluation results also show that it is probable that the feedback implosion problem caused by the bottlenecks could be solved if we choose to set the queue parameter Qeq threshold value at a high value, 3/4 of the queue capacity for instance.
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