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The strength of cryptographic keys rely on the random number generators (RNGs) to produce random seed values. Unfortunately there are not many RNGs options suitable for Internet of Things (IoTs) scenario, due to limited processing resources and bulk quantity of IoT data that needs to be secured. In this article, we studied sawtooth map which is a chaotic map. However, when implemented on a computer, the sawtooth map results on a non‐chaotic orbit due to the finite precision of computation. This can be avoided if we use the sawtooth map as the local map in a coupled map lattice (CML) system. We explore such coupled map systems for randomness through entropy and statistical analysis. Based on the results, we propose a lightweight hybrid pseudo random number generator (PRNG) based on sawtooth based CML system and SPONGENT hashing. The proposed PRNG is thoroughly tested against statistical attacks, entropy analysis, key space analysis and compared with existing state of the art solutions. The results provide evidence that the proposed PRNG produces random numbers that could produce sufficiently strong cryptographic keys for resource constrained IoT devices.

Image and video data make up a significant portion of the content shared over the Internet and social media. The use of image and video communication allows more information to be shared while simultaneously presenting higher risks in terms of data security. The traditional encryption schemes are general purpose; however, to encrypt image and video data, application-specific encryption solutions are needed. An image or a video frame comprises a two-dimensional matrix where pixel intensity values are integers in range [0,255], leading to data redundancy problems. Moreover, the bulk amount of image and video data adds another challenge when deploying security primitives. In this paper, a novel coupled map lattice system-based image cryptosystem has been proposed that uses generalised symmetric maps for generation of pseudo-random sequences. The generalization of symmetric maps allows the user to choose the source of pseudo-random sequence generation by varying a single control parameter. Other adaptive control parameters ensure an adequate degree of randomness in the generated sequences. The proposed encryption system relies on three independent sources of pseudo-random sequence generators, which are further re-randomized before the final encryption process. Comprehensive experimentation has been performed to test the proposed system against various attack models on publicly available datasets. A detailed comparative analysis has also been conducted with existing state-of-the-art image encryption techniques. Results show that the proposed algorithm provides high information entropy, negative correlation, large key space, and high sensitivity to key variations, and is resistant to various types of attacks, including chosen-text, statistical, and differential attacks.

Chaos-based cryptosystems have been an active area of research in recent years. Although these algorithms are not standardized like AES, DES, RSA, etc., chaos-based cryptosystems like Chebyshev polynomials can provide additional security when used with standard public key cryptosystems like RSA and El-gamal. Standard encryption algorithms such as AES have always been the primary choice, but when it comes to image or video encryption, many researchers recommend chaos-based encryption techniques due to their computational efficiency. This paper presents a survey on the most up-to-date chaos-based image encryption techniques and classifies them into spatial, temporal and spatiotemporal domains for better understanding. The significant improvements in the field of image encryption are discussed. In addition, comparative analysis is performed to validate the evaluation matrices for quantifying the encryption algorithms’ security and performance in recent papers.

Pseudo-random number generators (PRNGs) are one of the building blocks of cryptographic methods and therefore, new and improved PRNGs are continuously developed. In this study, a novel method to generate pseudo-random sequences using coupled map lattices is presented. Chaotic maps only show their chaotic behaviour for a specified range of control parameters, what can restrict their application in cryptography. In this work, generalised symmetric maps with adaptive control parameter are presented. This novel idea allows the user to choose any symmetric chaotic map, while ensuring that the output is a stream of independent and random sequences. Furthermore, to increase the complexity of the generated sequences, a lattice-based structure where every local map is linked to its neighbouring node via coupling factor has been used. The dynamic behaviour and randomness of the proposed system has been studied using Kolmogorov–Sinai entropy, bifurcation diagrams and the NIST statistical suite for randomness. Experimental results show that the proposed PRNG provides a large key space, generates pseudo-random sequences and is computationally suitable for IoT devices.

Internet of Things (IoT) technology is increasingly pervasive in all aspects of our life and its usage is anticipated to significantly increase in future Smart Cities to support their myriad of revolutionary applications. This paper introduces a new architecture that can support several IoT-enabled smart home use cases, with a specified level of security and privacy preservation. The security threats that may target such an architecture are highlighted along with the cryptographic algorithms that can prevent them. An experimental study is performed to provide more insights about the suitability of several lightweight cryptographic algorithms for use in securing the constrained IoT devices used in the proposed architecture. The obtained results showed that many modern lightweight symmetric cryptography algorithms, as CLEFIA and TRIVIUM, are optimized for hardware implementations and can consume up to 10 times more energy than the legacy techniques when they are implemented in software. Moreover, the experiments results highlight that CLEFIA significantly outperforms TRIVIUM under all of the investigated test cases, and the latter performs 100 times worse than the legacy cryptographic algorithms tested.

In wireless multimedia surveillance networks (WMSNs), the sensors generate continuous data streams which are saved on cloud servers for processing and future use. Large-scale security and monitoring decisions are based on the video data fed back to the cloud by the visual sensors. Internet of Things (IoT) networks are deployed in certain resource constrained scenarios where edge computing is not available for data analytics and security considering the IoT devices have also limited resources such as memory, power and processing capabilities. In this paper, the problems of limited resources and data security are addressed by the proposal of a secure model for video surveillance systems working in resource constrained IoT assisted scenarios. The suggested approach comprises of the following four main stages: i) save only those video frames in which any activity is detected, ii) encrypt the saved frames for secure transmission, iii) synchronize the encrypted frames between cloud and sensor node and iv) remove the transmitted frames from sensor and decrypt the stored data on cloud. The performance of the encryption process for resource constrained devices is analyzed on different types of sensor nodes during experimentation. The results prove that the proposed method automates and speeds up the process of live video data extraction and occupies less space on cloud when compared to the conventional approach for saving surveillance videos. Furthermore, adding encryption to video frames ensures integrity of video data during their journey from sensor to the cloud.