The Internet of Things (IoT) is seen as a novel paradigm enabling ubiquitous and pervasive communication of objects with each other and with the physical/virtual world via internet. With the... Show More

The Internet of Things (IoT) is seen as a novel paradigm enabling ubiquitous and pervasive communication of objects with each other and with the physical/virtual world via internet. With the exponential rise of sensor and RFID-based communication, much data is getting generated; which becomes arduous to manage given the constrained power and computation of low-powered devices. To resolve this issue, the integration of Cloud and IoT, also known as CloudIoT, is seen as panacea to create more heterogeneous smart services and handle increasing data demands. In this article, the authors examine and survey literature with a focus on the integration components of CloudIoT and present diverse applications including driving factors for CloudIoT integration. The article also identifies security vulnerabilities implied by the integration of Cloud and IoT and outlines some suggested measures to mitigate the challenge. Finally, the article presents some open issues and challenges providing potential directions for future research in this area.

Electric vehicles (EVs) are key players for transport oriented smart cities (TOSC) powered by smart grids (SG) because they help those cities to become greener by reducing vehicle emissions and... Show More

Electric vehicles (EVs) are key players for transport oriented smart cities (TOSC) powered by smart grids (SG) because they help those cities to become greener by reducing vehicle emissions and carbon footprint. In this article, the authors analyze different use-cases to show how big data analytics (BDA) can play vital role for successful electric vehicle (EV) to smart grid (SG) integration. Followed by this, this article presents an edge computing model and highlights the advantages of employing such distributed edge paradigms towards satisfying the store, compute and networking (SCN) requirements of smart EV applications in TOSCs. This article also highlights the distinguishing features of the edge paradigm, towards supporting BDA activities in EV to SG integration in TOSCs. Finally, the authors provide a detailed overview of opportunities, trends, and challenges of both these computing techniques. In particular, this article discusses the deployment challenges and state-of-the-art solutions in edge privacy and edge forensics.

Secured transmission of electrophysiological signals is one of the crucial requirements in telemedicine, telemonitoring, cardiovascular disease diagnosis (CVD) and telecardiology applications. The... Show More

Secured transmission of electrophysiological signals is one of the crucial requirements in telemedicine, telemonitoring, cardiovascular disease diagnosis (CVD) and telecardiology applications. The chaotic systems have good potential in secured transmission of ECG/EEG signals due to their inherent characteristics relevant to cryptography. This article introduces a new cryptosystem for clinical signals such as electrocardiograms (ECG) and electroencephalograms (EEG) based on hyperchaotic DNA confusion and diffusion transform (HC-DNA-CDT). The algorithm uses a hyperchaotic system with cubic nonlinearity and deoxyribonucleic acid (DNA) encoding rules. The performance of the cryptosystem is evaluated for different clinical signals using different encryption/decryption quality metrics. Simulation and comparison results show that the cryptosystem yield good encryption results and is able to resist various cryptographic attacks. The proposed algorithm can also be used in picture archiving and communication systems (PACS) to provide an efficient sharing of medical image over the networks.

Recently, due to the advance and impressive results of deep learning techniques in the fields of image recognition, natural language processing and speech recognition for various long-standing... Show More

Recently, due to the advance and impressive results of deep learning techniques in the fields of image recognition, natural language processing and speech recognition for various long-standing artificial intelligence (AI) tasks, there has been a great interest in applying towards security tasks too. This article focuses on applying these deep taxonomy techniques to network intrusion detection system (N-IDS) with the aim to enhance the performance in classifying the network connections as either good or bad. To substantiate this to NIDS, this article models network traffic as a time series data, specifically transmission control protocol / internet protocol (TCP/IP) packets in a predefined time-window with a supervised deep learning methods such as recurrent neural network (RNN), identity matrix of initialized values typically termed as identity recurrent neural network (IRNN), long short-term memory (LSTM), clock-work RNN (CWRNN) and gated recurrent unit (GRU), utilizing connection records of KDDCup-99 challenge data set. The main interest is given to evaluate the performance of RNN over newly introduced method such as LSTM and IRNN to alleviate the vanishing and exploding gradient problem in memorizing the long-term dependencies. The efficient network architecture for all deep models is chosen based on comparing the performance of various network topologies and network parameters. The experiments of such chosen efficient configurations of deep models were run up to 1,000 epochs by varying learning-rates between 0.01-05. The observed results of IRNN are relatively close to the performance of LSTM on KDDCup-99 NIDS data set. In addition to KDDCup-99, the effectiveness of deep model architectures are evaluated on refined version of KDDCup-99: NSL-KDD and most recent one, UNSW-NB15 NIDS datasets.

Homomorphic encryption (HE) is an encryption form that offers a third-party with the ability to carry out computations on encrypted data. This property can be considered as a great solution to get... Show More

Homomorphic encryption (HE) is an encryption form that offers a third-party with the ability to carry out computations on encrypted data. This property can be considered as a great solution to get over some obstacles limiting the wide-spread adoption of cloud computing (CC) services. Since CC environments are threatened by insider/outsider security attacks and since CC consumers often access to CC services using resource-limited devices, the HE schemes need to be promoted at security level and at running time to work effectively. For this reason, at EMENA-TSSL'16 and at WINCOM'16, the authors respectively boosted the RSA and ElGamal cryptosystems at security level, Cloud-RSA and Cloud-ElGamal. At SCAMS'17 and at EUSPN'17, the authors then suggested two fast variants of the Cloud-RSA scheme. All proposed schemes support the multiplicative homomorphism (MH) over the integers. The aim of this article is to compare the Cloud-ElGamal scheme with the Cloud-RSA schemes. This article first briefly presents the HE schemes and analyzes their security. This article then implements the schemes, compare and discuss their efficiency.