Sofiane Kharbech

Bio: Sofiane Kharbech is an academic researcher from university of lille. The author has contributed to research in topics: Computer science & Cognitive radio. The author has an hindex of 7, co-authored 17 publications receiving 177 citations. Previous affiliations of Sofiane Kharbech include TELECOM Lille 1 & Tunis University.

Novel Medical Image Encryption Scheme Based on Chaos and DNA Encoding

Abstract: In this paper, we propose a new chaos-based encryption scheme for medical images. It is based on a combination of chaos and DNA computing under the scenario of two encryption rounds, preceded by a key generation layer, and follows the permutation-substitution-diffusion structure. The SHA-256 hash function alongside the initial secret keys is employed to produce the secret keys of the chaotic systems. Each round of the proposed algorithm involves six steps, i.e., block-based permutation, pixel-based substitution, DNA encoding, bit-level substitution (i.e., DNA complementing), DNA decoding, and bit-level diffusion. A thorough search of the relevant literature yielded only this time the pixel-based substitution and the bit-level substitution are used in cascade for image encryption. The key-streams in the bit-level substitution are based on the logistic-Chebyshev map, while the sine-Chebyshev map allows producing the key-streams in the bit-level diffusion. The final encrypted image is obtained by repeating once the previous steps using new secret keys. Security analyses and computer simulations both confirm that the proposed scheme is robust enough against all kinds of attacks. Its low complexity indicates its high potential for real-time and secure image applications.

On classifiers for blind feature-based automatic modulation classification over multiple-input–multiple-output channels

Abstract: Modulation recognition is crucial for a good environmental awareness required by cognitive radio systems. In this study, the authors design and compare models of four among the most commonly used classifiers for feature-based automatic modulation classification (FB-AMC) algorithms. Classifiers whose models will be designed are classification tree, K-nearest neighbours, artificial neural networks (ANNs), and support vector machines. In this study, they apply some statistical pattern recognition techniques in the context of blind FB-AMC over multiple-input–multiple-output channels. Comparison criteria are classification accuracy and computational complexity. To improve the impartiality of this comparison, each classifier is optimally deployed by selecting its optimal model with respect to their context. Model selection for the classifiers is done using the ‘k-fold cross-validation’ model validation technique. The comparison study, within the considered context, shows that ANN classifiers have the best performance/complexity tradeoff.

Blind Digital Modulation Identification for Time-Selective MIMO Channels

Abstract: This paper addresses the problem of blind digital modulation identification in time-selective Multiple-Input Multiple-Output channels. Our objective is to recognize modulation schemes in highly-mobile communication environments, for military or high-speed railway applications, without signal knowledge or Channel State Information at the receiver. The proposed identification process is based on Blind Source Separation (BSS) and feature classification. We introduce a sliding window technique for the BSS of a faded-mixture to overcome the effect of the high mobility. Then, to improve the recognition of modulation schemes, we adopt a specific multi Artificial- Neural-Network (ANN) classifier, where each ANN is trained to be used within a particular Signal-to-Noise Ratio range. The proposed identifier has a good probability for achieving correct identifications under high velocity for typical carrier frequency and bandwidth.

Blind Digital Modulation Identification for MIMO Systems in Railway Environments With High-Speed Channels and Impulsive Noise

Abstract: In order to improve on existing radio communications for railways, cognitive radio (CR) is a promising concept, allowing the combination of artificial intelligence and software-defined radios. To ensure the best environmental awareness, as required by CR receivers, automatic modulation identification is a key feature for promoting efficient and secure communications in the CR context. This paper deals with blind modulation identification in the railway transmission environment and specifically considers its two major constraints: the high-speed and the impulsive nature of the noise. To achieve this goal, we propose a feature-based process of blind identification, which is made up of three subsystems: impulsive noise mitigation (which is the main contribution of this paper), feature extraction, and classification. For the purpose of mitigating the impulsive noise, we introduce a blind filtering myriad-based approach, which, in turn, requires the estimation of the noise parameters. Simulation results prove that the developed filtering approach provides a good filtering performance, and consequently, high identification performances of the overall identification system.

Fourth order MCA and chaos-based image encryption scheme

Abstract: This paper presents a fast and efficient cryptosystem for enciphering digital images. It employs two of the most prominent dynamical systems-chaotic maps and cellular automata. The key streams in the proposed encryption scheme are derived from the SHA-256 hash function. Hash functions produce the digest of the input plaintext, known as a hash value, which can be considered as a unique signature of the input. This makes the keys more plaintext dependent, which is a desirable property of a robust cryptosystem. These key streams are used as the secret keys (i.e., initial conditions and control parameters) of an improved one-dimensional (1-D) chaotic map, i.e., the Logistic-Sine map. As far as we know, this paper is a first that combines the well-known diffusion-confusion architecture and the fourth order 1-D memory cellular automata (MCA) for image encryption. First, a pixel-wise XOR operation is applied to the original image, followed by a pixel-wise random permutation. The resulting image is decomposed into four blocks according to the quadtree decomposition strategy. Then, a fourth order reversible MCA is applied, the blocks obtained from the quadtree decomposition are considered as the initial MCA configurations, and the transition rules are determined using the chaotic map. The performance analyses show that the proposed encryption scheme presents a high immunity against all kind of attacks while maintaining a low complexity, which outcome a notably better performance/complexity trade-off compared to some recently proposed image schemes.

Statistical Pattern Recognition

Abstract: This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Towards the Internet of Smart Trains: A Review on Industrial IoT-Connected Railways

Abstract: Nowadays, the railway industry is in a position where it is able to exploit the opportunities created by the IIoT (Industrial Internet of Things) and enabling communication technologies under the paradigm of Internet of Trains. This review details the evolution of communication technologies since the deployment of GSM-R, describing the main alternatives and how railway requirements, specifications and recommendations have evolved over time. The advantages of the latest generation of broadband communication systems (e.g., LTE, 5G, IEEE 802.11ad) and the emergence of Wireless Sensor Networks (WSNs) for the railway environment are also explained together with the strategic roadmap to ensure a smooth migration from GSM-R. Furthermore, this survey focuses on providing a holistic approach, identifying scenarios and architectures where railways could leverage better commercial IIoT capabilities. After reviewing the main industrial developments, short and medium-term IIoT-enabled services for smart railways are evaluated. Then, it is analyzed the latest research on predictive maintenance, smart infrastructure, advanced monitoring of assets, video surveillance systems, railway operations, Passenger and Freight Information Systems (PIS/FIS), train control systems, safety assurance, signaling systems, cyber security and energy efficiency. Overall, it can be stated that the aim of this article is to provide a detailed examination of the state-of-the-art of different technologies and services that will revolutionize the railway industry and will allow for confronting today challenges.

Cognitive radio for vehicular ad hoc networks (CR-VANETs): approaches and challenges

Abstract: With growing interest in using cognitive radio (CR) technology in wireless communication systems for vehicles, it is envisioned that future vehicles will be CR-enabled. This paper discusses CR technologies for vehicular networks aimed at improving vehicular communication efficiency. CR for vehicular networks has the potential of becoming a killer CR application in the future due to a huge consumer market for vehicular communications. This paper surveys novel approaches and discusses research challenges related to the use of cognitive radio technology in vehicular ad hoc networks. We review how CR technologies such as dynamic spectrum access, adaptive software-defined radios, and cooperative communications will enhance vehicular communications and, hence, present the potential of transforming vehicle communication in terms of efficiency and safety. Our work is different from existing works in that we provide recent advances and open research directions on applying cognitive radio in vehicular ad hoc networks (CR-VANETs) focusing on architecture, machine learning, cooperation, reprogrammability, and spectrum management as well as QoE optimization for infotainment applications. A taxonomy of recent advances in cognitive radio for vehicular networks is also provided. In addition, several challenges and requirements have been identified. The research on applying CR in vehicular networks is still in its early stage, and there are not many experimental platforms due to their complex setup and requirements. Some related testbeds and research projects are provided at the end.

Deep learning-based automated modulation classification for cognitive radio

Abstract: Automated Modulation Classification (AMC) has been applied in various emerging areas such as cognitive radio (CR). In our paper, we propose a deep learning-based AMC method that employs Spectral Correlation Function (SCF). In our proposed method, one deep learning technology, Deep Belief Network (DBN), is applied for pattern recognition and classification. By using noise-resilient SCF signatures and DBN that is effective in learning complex patterns, we achieve high accuracy in modulation detection and classification even in the presence of environment noise. Our simulation results illustrate the efficiency of our proposed method in classifying 4FSK, 16QAM, BPSK, QPSK, and OFDM modulation techniques in various environments.

A New Chaotic Map With Dynamic Analysis and Encryption Application in Internet of Health Things

Abstract: In this paper, we report an effective cryptosystem aimed at securing the transmission of medical images in an Internet of Healthcare Things (IoHT) environment. This contribution investigates the dynamics of a 2-D trigonometric map designed using some well-known maps: Logistic-sine-cosine maps. Stability analysis reveals that the map has an infinite number of solutions. Lyapunov exponent, bifurcation diagram, and phase portrait are used to demonstrate the complex dynamic of the map. The sequences of the map are utilized to construct a robust cryptosystem. First, three sets of key streams are generated from the newly designed trigonometric map and are used jointly with the image components (R, G, B) for hamming distance calculation. The output distance-vector, corresponding to each component, is then Bit-XORed with each of the key streams. The output is saved for further processing. The decomposed components are again Bit-XORed with key streams to produce an output, which is then fed into the conditional shift algorithm. The Mandelbrot Set is used as the input to the conditional shift algorithm so that the algorithm efficiently applies confusion operation (complete shuffling of pixels). The resultant shuffled vectors are then Bit-XORed (Diffusion) with the saved outputs from the early stage, and eventually, the image vectors are combined to produce the encrypted image. Performance analyses of the proposed cryptosystem indicate high security and can be effectively incorporated in an IoHT framework for secure medical image transmission.