In recent years, research on the Internet of Things (IoT) has seen widespread growth, with a particular focus on mobile networks, especially intelligent transportation systems (ITS), due to their potential application in smart cities. However, communication within ITS suffers from numerous issues, leading to multiple attacks. Therefore, ensuring the secure transmission of messages in ITS poses a significant challenge. Recently, Srinivas et al. have proposed “a three-factor authentication scheme for Big Data collection in IoT-based ITS, called UAP-BCIoT”. However, the proposed scheme has some shortcomings in terms of authentication. In this paper, we show how this flaw affects the users’ access to the data provided by the IoT devices, which are deployed on vehicles. In addition, we propose enhancements to overcome the scheme’s authentication issue. Furthermore, a comparative analysis reveals that the enhanced UAP-BCIoT achieves better efficiency, security, and performance compared to UAP-BCIoT and other schemes.

With the great potential of internet of things (IoT) infrastructure in different domains, cyber-attacks are also rising commensurately. Distributed denials of service (DDoS) attacks are one of the cyber security threats. This paper will focus on DDoS attacks by adding the design of an intrusion detection system (IDS) tailored to IoT systems. Moreover, machine learning techniques will be investigated to distinguish the data representing flows of network traffic, which include both normal and DDoS traffic. In addition, these techniques will be used to help make a refined detection model for identifying different types of DDoS attacks. Furthermore, the performance of machine learning-based proposed solution is validated using N-BaIoT dataset and compared through different evaluation metrics. The experimental results show that the proposed IDS not only detects DDoS attacks types but also has a high detection rate and low false positive rate, which argues the usefulness of the proposed approach in comparison with several existing DDoS attacks detection techniques.

Access control mechanisms are the way to guarantee secure access to grid resources. Recent research works were focused on how to improve the representation of the resources' security policies for faster decisions making. PCM, HCM, GAG, and WGAG are all different ways to represent these security policies. This paper presents an enhancement to WGAG, the action-weighted grid authorization graph (Action-WGAG). A security policy-parser (SP-Parser) has been developed to implement the Action-WGAG. The evaluation results of the proposed model showed that it assures a smaller number of security rule checking in some cases and a reduction of the answer time to an access control request.

Purpose – The purpose of this paper is to build a new hierarchical intrusion detection system (IDS) based on a binary tree of different types of classifiers. The proposed IDS model must possess the following characteristics: combine a high detection rate and a low false alarm rate, and classify any connection in a specific category of network connection. Design/methodology/approach – To build the binary tree, the authors cluster the different categories of network connections hierarchically based on the proportion of false-positives and false-negatives generated between each of the two categories. The built model is a binary tree with multi-levels. At first, the authors use the best classifier in the classification of the network connections in category A and category G2 that clusters the rest of the categories. Then, in the second level, they use the best classifier in the classification of G2 network connections in category B and category G3 that represents the different categories clustered in G2 without category B. This process is repeated until the last two categories of network connections. Note that one of these categories represents the normal connection, and the rest represent the different types of abnormal connections. Findings – The experimentation on the labeled data set for flow-based intrusion detection, NSL-KDD and KDD’99 shows the high performance of the authors' model compared to the results obtained by some well-known classifiers and recent IDS models. The experiments’ results show that the authors' model gives a low false alarm rate and the highest detection rate. Moreover, the model is more accurate than some well-known classifiers like SVM, C4.5 decision tree, MLP neural network and naïve Bayes with accuracy equal to 83.26 per cent on NSL-KDD and equal to 99.92 per cent on the labeled data set for flow-based intrusion detection. As well, it is more accurate than the best of related works and recent IDS models with accuracy equal to 95.72 per cent on KDD’99. Originality/value – This paper proposes a novel hierarchical IDS based on a binary tree of classifiers, where different types of classifiers are used to create a high-performance model. Therefore, it confirms the capacity of the hierarchical model to combine a high detection rate and a low false alarm rate.

In this paper we propose a meta-model for architectures with heterogeneous multimedia components. Currently, a generic solution does not exist to automatically deploy a distributed architecture based on multimedia components. The description of the incompatibilities between components is a need in such approaches. Indeed, software architectures validate the functional aspects, which are not sufficient to guarantee a realistic assembly. For instance, the problem of heterogeneity related to the exchanged data flows. In order to highlight these incompatibilities and to find solutions, a model-based approach called MMSA (Meta-model Multimedia Software Architecture) is proposed. It enables the description of the software architectures expressing a software system as a collection of components which handle various types and formats of data, and interacts between them via connectors including the adaptation connectors.