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Presents corrections to the paper, (Corrections to “A Novel Approach Based on Machine Learning, Blockchain and Decision Process for Securing Smart Grid”).

Smart Grids (SGs) rely on advanced technologies, generating significant data traffic across the network, which plays a crucial role in various tasks such as electricity consumption billing, actuator activation, resource optimization, and network monitoring. This paper presents a new approach that integrates Machine Learning (ML), Blockchain Technology (BT), and Markov Decision Process (MDP) to improve the security of SG networks while ensuring accurate storage of events reported by various network devices through BT. The enhanced version of the Proof of Work (PoW) consensus mechanism ensures data integrity by preventing tampering and establishing the reliability of known and unknown attack detection. The proposed versions of PoW, namely GPoW 1.0 and GPoW 2.0, aim to make the consensus process more environmentally friendly.

Nowadays, wireless broadband communication has experienced tremendous growth. The requirement for various new and advanced multiple access techniques is constantly increasing. The interleave division multiple access (IDMA) technique has attracted the attention of the scientific community as an opportune code domain non-orthogonal multiple access (NOMA) for future communication standards. In this system, the detection of the signals received from different users is done by specific interleavers.In this paper, and regarding the transmission-rate loss caused by the use of spreading codes and interleavers in the IDMA systems, we are going to place ourselves in a blind separation context for IDMA signals. For this purpose, a study of a purely deterministic approach, which is the tensor decomposition, as well as a theoretical study of the Canonical Polyadic (CP) model’s existence and uniqueness issues, is also carried out. Then, a tensor modeling of the IDMA emitter and the proposal of a new form of the interleavers will be undertaken. The separation and equalization of received signals using the CP tensor decomposition compelled us to examine the methods utilized to carry out this decomposition, as well as suggest two iterative blind receivers that take use of the algebraic features of the suggested model. On the other hand, the application of an exact performance criterion will be performed, taken into account the scale and permutation indeterminacy constraints.

Cognitive radio (CR) is a promising paradigm that comes to address the scarcity of the radio spectrum by providing opportunistic utilization of the underutilized licensed channels to attain higher spectrum efficiency. The efficient use of the vacant portion of the spectrum depends directly on the medium access control (MAC) layer that coordinates fairly the access of the CR nodes to the idle spectrum. However, the MAC layer is vulnerable to several attacks driven by malicious nodes. One of those attacks is the backoff manipulation attack (BMA), where the selfish attacker deviates from the defined contention mechanism to gain inequitable access to the available channels. This unfair access presents some specific characteristics of an attacker, which can be considered as an input to the supervised machine learning algorithm for classification. In this paper, we propose a support vector machine (SVM) based model in order to distinctively identify the attacker depending on the throughput and the average packet delay to classify/predict an eventual attack. Finally, theoretical predictions and simulation results are presented to validate the proposed framework while giving useful insights into CR systems' performance, vulnerable to BMA attacks.

Nowadays, the cybersecurity issue involves new strategies to protect against advanced threats and unknown attacks. Intrusion detection system (IDS) is considered a robust system dealing with attacks detection, particularly unknown attacks and anomalies. Several IDS-based algorithms have been recently inspected in the literature, among them the well-known strengthen algorithms, i.e. Genetic algorithm (GA). Moreover, Epigenetic-based algorithm (EGA) is known as an improved version of GA ensuring high performance with reduced computational complexity. Its main goal is to converge within a short time towards an optimal solution by acting on genetic operators, namely mutation and crossover. In this article, we propose a new classifier based on EGA for IDS. Especially, based on a database of network traffics, EGA is applied to classify attacks. The results, performed through EGA simulation, show that the performance of the proposed technique outperforms the ones of GA classifier by obtaining a high detection rate up to 98% and a faster processing time than that of GA and other algorithms that we have compared in this article.

Cognitive radio technology addresses the problem of spectrum scarcity by allowing secondary users to use the vacant spectrum bands without causing interference to the primary users. However, several attacks could disturb the normal functioning of the cognitive radio network. Primary user emulation attacks are one of the most severe attacks in which a malicious user emulates the primary user signal characteristics to either prevent other legitimate secondary users from accessing the idle channels or causing harmful interference to the primary users. There are several proposed approaches to detect the primary user emulation attackers. However, most of these techniques assume that the primary user location is fixed, which does not make them valid when the primary user is mobile. In this paper, we propose a new approach based on the Kalman filter framework for detecting the primary user emulation attacks with a non-stationary primary user. Several experiments have been conducted and the advantages of the proposed approach are demonstrated through the simulation results.

Since the middle ages, the need to identify the vehicles position in their local environment has always been a necessity and a challenge. Today, GNSS-based positioning systems have penetrated several field, such as land transport, emergency systems and civil aviation requiring high positioning accuracy. However, the performances of GNSS-based systems can be degraded in harsh environment due to non-line-of-sight (NLOS), Multipath and masking effects. In this paper, for improving vehicle localization in urban canyons, we address a very challenging problem related to GNSS signal reception state detection (LOS, NLOS or Multipath). A SVMbased system for GNSS Multipath detection using the fusion of information provided by two GNSS antennas is proposed. In this work, we aim to explore the potential of machine learning, and more precisely, Support Vector Machines (SVM) to identify GNSS signals reception state. The SVM-based system developed in this work has used the C/N0 of signals provided by RHCP and LHCP antennas, and satellite elevation as classification criteria. The training data set is constructed by several experimental studies done in real environments, Calais, France . Furthermore, four SVM kernel functions are tested, namely, Linear, Gaussian, Cubic and Quadratic. A GNSS signal reception state detection by applying the proposed SVM-based classifier is demonstrated on real GPS signals, and the efficiency of the system is shown. We obtain empirically an accuracy of signal detection about 93%.