Hardware Implementation Analysis of Ascon: A Structured Review

The Internet of Things (IoT) has become an integral part of future solutions, ranging from industrial to everyday human life applications. Adding a new level of intelligence to objects and automating decisions make this new technology appealing to everyone. However, applications that involve data are more vulnerable to various types of attacks. As a result, researchers are constantly exploring secure connections between IoT edge nodes. On one hand, suitable IoT nodes should be cheap and require low power, which means lower computational performance. On the other hand, a secure connection layer is power hungry and requires powerful hardware resources. Lightweight cryptography (LWC) algorithms are a promising solution to reduce computation complexity while maintaining a desired level of security. In the presented work, we attempt to address the issue of adding security to the IoT network layer by comparing the performance of 32 LWC algorithms with currently well-known algorithms on multiple IoT platforms (Raspberry Pi 3, Raspberry Pi Zero W, and iMX233). These 32 authenticated encryption with associated data algorithms have been selected from the second round of the LWC standardization process conducted by the National Institute of Standards and Technology. Power consumption, random access memory usage, and execution time are measured for these algorithms using the targeted embedded platforms that are used as IoT sensor nodes. The results of this study will assist researchers in choosing a suitable platform and optimal LWC algorithm for IoT applications.

The growing proliferation of the Internet of Things (IoT) necessitates lightweight cryptographic algorithms that balance security and efficiency f or r esource-constrained environments. Traditional cryptographic methods are often too resource-intensive for devices with limited processing power, memory, and energy capacity. Lightweight cryptography (LWC) addresses this by providing optimized solutions with minimal hardware over-head. One such candidate, the New Lightweight Cryptographic Algorithm (NLCA), a 128-bit symmetric block cipher. This paper introduces a modified version of NLCA, called ModNLCA, which replaces the original F-function with a dynamic S-Box generated from the secret key and round constants. This modification enhances resistance to linear attacks while potentially reducing computational complexity. The algorithm is implemented on ESP32 microcontrollers to simulate real-world IoT scenarios, where encryption and decryption processes are distributed across nodes via Wi-Fi communication. Experimental results show that while ModNLCA is slightly slower than PRESENT at small data sizes (e.g., 32 bits), it outperforms both NLCA and PRESENT significantly at l arger sizes. At 256 bits, ModNLCA a chieves up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{5 3 \%}$</tex> faster encryption and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3 8 \%}$</tex> faster decryption compared to PRESENT, while also using memory more efficiently. Moreover, ModNLCA demonstrates a strong diffusion effect, achieving an avalanche effect rate of 25.34 %. These findings s uggest that ModNLCA is a promising candidate for secure and efficient encryption in resource-constrained systems.

Bitcoin and Ethereum's low transaction per second (TPS) rate has prohibited the adoption of Blockchain in areas such as Internet of Things (IoT) and Internet of Medical Things (IoMT). Despite these constraints, there is considerable research in this area, outlining the benefits of employing Blockchain in IoT applications. Such benefits relate to ensuring that the applications and their data are secure, and integrity is maintained. However, the relatively low TPS rate is still an impediment for the widespread adoption of Blockchain in IoT. This research seeks to explore whether Solana, a new Blockchain technology designed to have a high TPS rate, can overcome this challenge. Can the Solana Blockchain achieve a TPS rate higher than that achieved in other works in the literature? Can a sufficient rate be achieved that could help Blockchain technologies become an enabler in the IoT industry? These are the questions that this research aims to answer. Our experiments conducted on an application coded in Rust on Solana Blockchain demonstrated that it can successfully achieve a 663 TPS rate. Consequently, we conclude that Solana, by overcoming the aforementioned TPS rate issue, could advance the further adoption of Blockchain in IoT context.

With the proliferation of Internet of Things (IoT) devices, cybersecurity has become an increasingly pressing concern. Due to their inherent vulnerabilities and the severity of the threats posed by malware, robust malware detection strategies for IoT devices are in high demand. However, the constraints imposed by the limited memory and processing power of IoT devices, alongside with the varied nature of these devices, have limited the development of effective malware detection strategies. Existing solutions primarily use either local computation or cloud-based services, each with its own set of challenges. In this paper, we propose a hybrid malware detection method that leverages both local and cloud-based computation to address these challenges. This method, based on similarity computations, optimizes the trade-off between computational resources and latency, offering an effective approach for malware detection in IoT devices. Through experimental evaluation using a substantial IoT malware dataset, we demonstrate that our proposed approach achieves an optimal balance between local and cloud-based computations, providing a promising solution for malware detection in IoT context.

Today’s networked society is populated by complex business ecosystems that have moved away from the centralised firm-centric structure to a more distributed, crowd-driven ecosystem structure that adopts an open participatory value creation model. Such crowd-driven ecosystems take advantage of technological advancements and underlying network effects in order to harness the collective power and intelligence of the crowd. This ability to co-create value by actively collaborating with the crowd has been fueled by the Internet of Things (IoT) introducing new user-centric paradigms, such as mobile crowd sensing (MCS), which leverage the power and the wisdom of the crowd to observe, measure, and make sense of particular phenomena by exploiting user-owned mobile and wearable devices. Crowd-driven IoT ecosystems have emerged taking advantage of technological advancements and underlying network effects in order to harness the collective power and intelligence of the crowd. However, although research in the context of business ecosystems and business models has gained substantial importance in recent years, there is still a limited understanding of the business dynamics of these emerging, technologically advanced, crowddriven ecosystems. The vast majority of existingresearch efforts place emphasis on business models in the IoT context and only a few studies consider business models for IoT ecosystems. However, one can identify a research gap in relation to business models for crowd-driven ecosystems in the IoT context. This study aims to contribute to the emerging crowd-driven IoT ecosystems literature by examining business models in such environments. For this reason both an exploratory and explanatory research approach is employed. This study provides an exploratory review analysis that synthesizes current scientific knowledge and an explanatory framework that facilitates the analysis of business model components and frameworks that correspond to the needs of firm-centric and ecosystem-centric IoT environments. The study concludes by providing suggestions for the business model development for technologically advanced, crowd-driven ecosystems as in the area of IoT based on its findings, establishing this way a solid foundation for future research in the area of crowd-driven IoT ecosystems.

Data security in Internet of Things (IoT) devices is a critical challenge due to their inherent limitations in memory, energy, and processing capacity. Traditional security mechanisms are often unfeasible, driving the search for lightweight cryptographic algorithms. In this context, DNA-based cryptography stands out as a promising bioinspired approach. This study presents an empirical and direct comparison of four DNA-based cryptographic algorithms, implemented on a real resource-constrained IoT device, the ESP8266. The analysis quantified performance and efficiency in both encryption and decryption modes, using instrumented measurements of energy consumption (mJ), execution time (𝜇s), electric current (mA), and RAM usage (B). The results reveal statistically significant differences among the algorithms, confirmed by Shapiro-Wilk and Wilcoxon tests. Alg_3 demonstrated the highest efficiency in time and energy, being the fastest and with the lowest total energy consumption, although it required the most RAM. The research highlights the potential of bioinspired cryptography and reinforces the importance of considering the constraints of the target environment when selecting a cryptographic scheme.

With the development of Information Technology, Internet of Things (IoT) has been applied widely in human civilization, but it leads the increase of the risk about information security. The traditional security solutions has revealed the lack of compatibility with IoT because of the difference about complex communication protocol, low energy, processing ability and limited memory. Therefore, the suitable IoT security solutions needs the balance between energy and costs as well as wide adaption for the various network protocols. Our research team, after had been under a long process of analyzing theoretical documents and operating simulated experiments, recognized that the Lightweight cryptography is the most optimal solution in new network platform generation. Our team also improved and implemented CurveCP which is one of these Lightweight cryptographies in the Wireless sensor Networks (WSN) so as to enhance data secure and information security of IoT System. This study describes briefly the improvement of CurveCP Lightweight cryptography by reducing length of cryptographic key as well as implement in IoT System. It also includes the simulation experiment, solutions evaluation, conclusion and future development.

In the rapidly developing world of the Internet of Things (IoT), data security has become increasingly important since massive personal data are collected. IoT devices have resource constraints, which makes traditional cryptographic algorithms ineffective for securing IoT devices. To overcome resource limitations, lightweight cryptographic algorithms are needed. To identify research trends and patterns in IoT security, it is crucial to analyze existing works, keywords, authors, journals, and citations. We conducted a bibliometric analysis using performance mapping, science mapping, and enrichment techniques to collect the necessary information. Our analysis included 979 Scopus articles, 214 WOS articles, and 144 IEEE Xplore articles published during 2015–2023, and duplicates were removed. We analyzed and visualized the bibliometric data using R version 4.3.1, VOSviewer version 1.6.19, and the bibliometrix library. We discovered that India is the leading country for this type of research. Archarya and Bansod are the most relevant authors; lightweight cryptography and cryptography are the most relevant terms; and IEEE Access is the most significant journal. Research on lightweight cryptographic algorithms for IoT devices (Raspberry Pi) has been identified as an important area for future research.

The emergence of the internet of things (IoT) is changing the behavior of people, companies, and their businesses. The IoT can enhance e-commerce by reducing the gaps between the virtual and physical worlds. Although the integration of the IoT into e-commerce has several positive effects, security concerns are greater in the IoT context than in traditional e-commerce settings. The proposed research model is based on the unified theory of acceptance and use of technology 2 (UTAUT2) along with trust, privacy, and confidential data concerns to explain the intention to use the IoT in e-commerce. The authors developed a questionnaire and received a total of 328 responses. The results show that among the IoT security factors, trust and privacy are significant determinants of the intention to use the IoT in e-commerce.

The Internet of Things (IoT) continues to grow at a rapid pace, becoming integrated into the daily operations of individuals and organisations. IoT systems automate crucial services within daily life that users may rely on, which makes the assurance of security towards entities such as devices and information even more significant. In this paper, we present a comprehensive survey of papers that model cyber risk management processes within the context of IoT, and provide recommendations for further work. Using 39 collected papers, we studied IoT cyber risk management frameworks against four research questions that delve into cyber risk management concepts and human-orientated vulnerabilities. The importance of this work being human-driven is to better understand how individuals can affect risk and the ways that humans can be impacted by attacks within different IoT domains. Through the analysis, we identified open areas for future research and ideas that researchers should consider.

The Internet of Things (IoT) is a network of interconnected devices that can transmit data over a network without the need for physical interactions. IoT devices include wearable sensors, software, actuators, and computer devices. They are linked to a specific object that connects through the internet. At the same time, the lack of protection for IoT devices has increase in security threats. IoT attacks are one of the most common illegal activities involving in IoT, they spread quickly and can cause so much more harm than other destructive activities. In recent years, the Intrusion Detection System has shown to be a helpful tool for securing data in IoT devices. This study presents an Intrusion Detection System (IDS) based on machine learning that can detect and mitigate security threats in an IoT context. The paper hence, carried out a comparative review of literature on previous researches and studies on attack identification using Machine language techniques. Machine approach are the most appropriate detective control approach against threats generated from IoT devices. The goal of this work is to provide a comprehensive survey of ML methods that can be used to develop improved security methods IoT devices.

Increasingly, the heterogeneity of devices and software that comprise the Internet of Things (IoT) is impeding innovation. IoT deployments amalgamate compute, storage, networking capabilities provisioned at multiple resource scales, from low-cost, resource constrained microcontrollers to resource rich public cloud servers. To support these different resource scales and capabilities, the operating systems (OSs) that manage them have also diverged significantly. Because the OS is the “API” for the hardware, this proliferation is causing a lack of portability across devices and systems, complicating development, deployment, management, and optimization of IoT applications. To address these impediments, we investigate a new, “clean slate” OS design and implementation that hides this heterogeneity via a new set of abstractions specifically for supporting microservices as a universal application programming model in IoT contexts. The operating system, called Ambience, supports IoT applications structured as microservices and facilitates their portability, isolation, and deployment time optimization. We discuss the design and implementation of Ambience, evaluate its performance, and demonstrate its portability using both microbenchmarks and end-to-end IoT deployments. Our results show that Ambience can scale down to 64MHz microcontrollers and up to modern x86_64 servers, while providing similar or better performance than comparable commodity operating systems on the same range of hardware platforms.

In-network caching within the distributed computing is one of the most promising aspects of Information-Centric Networking (ICN) in comparison with traditional centralized server based data retrieval. With ICN, it can ensure that required data is readily available within the intermediate IoT routers, even when the original producer is not reachable. However, in Internet of Things (IoT), where the storage capacity is often severely limited, IoT nodes cannot simply cache any and all the content that will receive to the intermediate caching device(routers). Hence, it is very significant to know which content has to cache and how long the content has to be in the intermediate caching device. Moreover, cache-based routing plays a predominant role in reducing the end-to-end delay with enhanced network throughput by efficiently discovering the shortest-path from the intermediate caching devices in ICN-IoT networks. In this paper, comparison of different cache based routing protocols for ICN-IoT networks is discussed in terms of ICN caching and cache replacement strategies along with the performance metrics suitable for evaluating in an IoT context.

In the Internet of Things (IoT) era, technologies have become even more immersive and ubiquitous in people's lives. Therefore, developing collaborative systems becomes a more complex task involving different factors and actors. The nature of these applications encompasses different stakeholders and interests. The software development process encompasses large-scale sensors and actuators to characterize appropriated the Internet of Things (IoT). The introduction of this technology in the scenario of collaborative systems and IoT increased the complexity of software development. This work presents a computational architecture based on middleware to deal with the challenges of developing collaborative systems in the context of IoT. As a contribution, results acquired from the case study demonstrate how the architecture supports IoT application development in the context of collaborative systems.

With the rapid advancements and notable achievements across various application domains, Machine Learning (ML) has become a vital element within the Internet of Things (IoT) ecosystem. Among these use cases is IoT security, where numerous systems are deployed to identify or thwart attacks, including intrusion detection systems (IDSs), malware detection systems (MDSs), and device identification systems (DISs). Machine Learning-based (ML-based) IoT security systems can fulfill several security objectives, including detecting attacks, authenticating users before they gain access to the system, and categorizing suspicious activities. Nevertheless, ML faces numerous challenges, such as those resulting from the emergence of adversarial attacks crafted to mislead classifiers. This paper provides a comprehensive review of the body of knowledge about adversarial attacks and defense mechanisms, with a particular focus on three prominent IoT security systems: IDSs, MDSs, and DISs. The paper starts by establishing a taxonomy of adversarial attacks within the context of IoT. Then, various methodologies employed in the generation of adversarial attacks are described and classified within a two-dimensional framework. Additionally, we describe existing countermeasures for enhancing IoT security against adversarial attacks. Finally, we explore the most recent literature on the vulnerability of three ML-based IoT security systems to adversarial attacks.