Quantum Cryptography for Internet of Things Security

Internet of things (IoT) is a growing technology with a big market and impact to our lives. It can ease various different tasks for us. Meanwhile, IoT has many serious security threats, like data breaches, side-channel attacks, and virus and data authentication. Our present classical cryptography, like the Rivest-Shamir-Adleman (RSA) algorithm, work well under the classical computers. However, the technology is slowly shifting towards quantum computing, which has immense processing power and is more than enough to break the current cryptographic primitives in affordable time. So, it is required to design quantum cryptographic algorithms to prevent our systems from security breaches even before quantum computers will be available for commercial purposes on the market. In this paper, we describe a new implementation of MST3 cryptosystems based on the group of automorphisms of the field of the Pu function. The main difference of the presented implementation is the extension of the logarithmic signature and, as a consequence, the presence of multi-stage recovery of message parts from the ciphertext. The presented implementation of the cryptosystem is more reliable. The cryptanalysis complexity and message size for encryption are square times larger than the MST3 cryptosystem in the Suzuki group. This approach shows advantages and it is a quantum safe for the IoT use.

Internet of Things (IoT) have opened the door to a world of unlimited possibilities for implementations in varied sectors in society, but it also has many challenges. One of those challenges is security and privacy. IoT devices are more susceptible to security threats and attacks. Due to constraints of the IoT devices such as area, power, memory, etc., there is a lack of security solutions that are compatible with IoT devices and applications, which is leading this world of securely connected things to the “internet of insecure things.” A promising solution to this problem is going beyond the standard or classical techniques to implementing the security solutions in the hardware of the IoT device. The integration of emerging technologies in IoT networks, such as machine learning, blockchain, fog/edge/cloud computing, and quantum computing have added more vulnerable points in the network. This paper introduces a comprehensive study on IoT security threats and solutions. Additionally, this survey outlines how emerging technologies such as machine learning and blockchain are integrated in IoT, challenges resulted from this integration, and potential solutions to these challenges. The paper utilizes the 4-layer IoT architecture as a reference to identify security issues with corresponding solutions.

The Internet of Things (IoT) are being enthusiastically adopted by consumers. By the year 2020 the sum of 31 billon IoT devices will be deployed globally. Subsequent as the IoT device landscape is expanding at such speed, so does the threat landscape and vulnerabilities it introduces increases. Thus, making IoT devices easily prone to attacks or to be used to for launching attacks at large economical scale and society is seeing a growth in the scale and frequencies of these attacks. The large scale of attacks and frequency have caught global attention and causing governments to take the security and privacy threats of IoT very seriously and the UK government amongst others are now turning these concerns into actionable measures by considering ways of protecting consumers against the vulnerabilities and threats of IoT. It is part of these actionable measures that the NCSC (National Cyber Security Centre) recently published in a report about the new laws being proposed by the government to strengthen IoT devices. This chapter will look at the IoT security threats and privacy issues, it will explore whether the growing concern of the government to protect consumer has a foundation by investigating consumers awareness and attitude towards IoT security threats and privacy issues and propose a framework to facilitate the introduction of the new initiative of the government to bring in laws to govern IoT products thereby shifting the responsibility of the security threats to the manufacturers and away from the consumer.

The Internet of Things (IoT) now underpins critical systems like healthcare monitors, smart home, smart grids, automotive V2V & V2X networks, and industrial controls but it still relies on classical cryptography, whose security assumptions are vulnerable to quantum computers. A large quantum computer could run Shor’s algorithm to break RSA/ECC and use Grover’s algorithm to accelerate brute-forcing of symmetric keys, undermining the confidentiality and authentication of data in these IoT applications. Researchers have proposed quantum cryptographic solutions. Quantum key distribution (QKD) and quantum random number generators (QRNGs) exploit quantum mechanics to detect eavesdropping and deliver provably secure key material. For example, chip-scale QRNGs can be embedded in IoT sensors to generate true random keys, boosting resistance to brute-force attacks. These quantum techniques provide information-theoretic secrecy, for instance, QKD-generated keys have been used to authenticate smart-grid SCADA. However, most IoT devices are resource-constrained, making direct deployment of conventional QKD hardware challenging. Post-quantum cryptography (PQC) complements quantum methods by providing algorithms such as lattice-based, hash-based that resist quantum attacks on existing hardware. Standardized PQC can be deployed via software updates on legacy IoT nodes, enabling scalable and quantum-safe security within current constraints. Researchers are exploring lightweight PQC for connected cars and e-health sensors. Hybrid approaches combining QKD/QRNG with PQC offer a layered defense. Since QKD protocols are regarded as ‘future-proof’ and PQC standards mature, their combined use will be essential for robust, quantum-safe IoT security. Miniaturizing quantum components such as integrated QKD/QRNG modules and optimizing lightweight PQC schemes for low-power IoT devices are future research directions to secure IoT networks.

Internet of Things (IoT) plays an increasingly significant role in our daily activities, connecting physical objects around us into digital services. In other words, IoT is the driving force behind home automation, smart cities, modern health systems, and advanced manufacturing. This also increases the likelihood of cyber threats against IoT devices and services. Attackers may attempt to exploit vulnerabilities in application protocols, including Domain Name System (DNS), Hyper Text Transfer Protocol (HTTP) and Message Queue Telemetry Transport (MQTT) that interact directly with backend database systems and client-server applications to store data of IoT services. Successful exploitation of one or more of these protocols can result in data leakage and security breaches. In this paper, an ensemble intrusion detection technique is proposed to mitigate malicious events, in particular botnet attacks against DNS, HTTP, and MQTT protocols utilized in IoT networks. New statistical flow features are generated from the protocols based on an analysis of their potential properties. Then, an AdaBoost ensemble learning method is developed using three machine learning techniques, namely decision tree, Naive Bayes (NB), and artificial neural network, to evaluate the effect of these features and detect malicious events effectively. The UNSW-NB15 and NIMS botnet datasets with simulated IoT sensors' data are used to extract the proposed features and evaluate the ensemble technique. The experimental results show that the proposed features have the potential characteristics of normal and malicious activity using the correntropy and correlation coefficient measures. Moreover, the proposed ensemble technique provides a higher detection rate and a lower false positive rate compared with each classification technique included in the framework and three other state-of-the-art techniques.

Cryptographic algorithms are crucial for secure communication in the Internet of Things (IoT). Quantum computers threaten public key cryptography algorithms like RSA (Rivest, Shamir, Adleman) and Diffie-Hellman (DH) key exchange schemes, which are widely in use today. As cryptographic algorithms are the foundation of secure internet communication, the development of Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC) is essential to protect transactions over the internet. However, solutions like Quantum Key Distribution require heavy infrastructure. So, it is not feasible for the Internet of Things with fewer resources in computing power and form factor. Post-Quantum Cryptography is helpful on these devices. But, it needs further developments and improvements. This paper analyzes cryptography algorithms and protocols of IoT with different categories. Integrated PQC of National Institute of Standards and Technology (NIST) third level finalists on Raspberry Pi 4 (RPI4) board with Linux operating system. The performance of Post-Quantum Cryptography schemes for IoT has been evaluated. Finally, a Post-Quantum Cryptography scheme has been suggested for the Internet of Things based on the performance analysis.

The exponential development in Quantum Computing is becoming a threat to the existing asymmetric cryptographic algorithms like RSA (Rivest, Shamir, Adleman). A threat to these public key cryptographic algorithms is a threat to the entire Internet itself, as algorithms like RSA are the basic building blocks of existing secure Internet communication. Thus, a new area of quantum cryptography emerged, that lead to the development of Post-Quantum Cryptography (PQC) algorithms and Quantum-safe Internet protocol Quantum Key Distribution (QKD). These systems, which are generally known as Quantum Cryptographic Systems, have many practical limitations that make them complex and expensive to be implemented or even experimented with, in a real-life scenario. Therefore, to develop new protocols and analyze the feasibility of the existing protocols in a cost-effective manner, we need efficient simulation frameworks. So, in this paper, we are profiling some of the recent Quantum Cryptographic based networks and Quantum Key Distribution (QKD) simulation frameworks and conducting a systematic and thorough analysis of the implementation and functionalities of these experimental platforms, with an aim to build a universal quantum testbed in the future.

The Internet of Things (IoT) has assumed a pivotal role in the advancement of communication technology and in our daily lives. However, an IoT system such as a smart grid with poorly designed topology and weak security protocols might be vulnerable to cybercrimes. Exploits may arise from sensor data interception en route to the intended consumer within an IoT system. The increasing integration of electronic devices interconnected via the internet has galvanized the acceptance of this technology. Nonetheless, as the number of users of this technology surges, there must be an aligned concern to ensure that security measures are diligently enforced within IoT communication systems, such as in smart homes, smart cities, smart factories, smart hospitals, and smart grids. This research addresses security lacunae in the topology and configuration of IoT energy monitoring systems using post-quantum cryptographic techniques. We propose tailored implementations of the Rivest–Shamir–Adleman (RSA), N-th degree Truncated Polynomial Ring Units (NTRU), and a suite of cryptographic primitives based on Module Learning With Rounding (Saber) as post-quantum cryptographic candidate algorithms for IoT devices. These aim to secure publisher–subscriber end-to-end communication in energy system monitoring. Additionally, we offer a comparative analysis of these tailored implementations on low-resource devices, such as the Raspberry Pi, during data transmission using the Message Queuing Telemetry Transport (MQTT) protocol. Results indicate that the customized implementation of NTRU outperforms both SABER and RSA in terms of CPU and memory usage, while Light SABER emerges as the front-runner when considering encryption and decryption delays.

Quantum computing has quickly become at the vanguard of many computer science fields today. This is motivated by quantum computers ability to perform computational tasks with efficiencies that are not possible with classical computers. Quantum computing technology has a lasting potential impact on society and a broad implications on many aspects of our lives. Communications Industry Researchers (CIR) estimates that the global quantum-computing market is expected to reach $1.9 billion in 2023, increasing to $8 billion by 2027, in spending on associated software and services. Although quantum computing has several advantages, it threatens the current cryptographic techniques, which are necessary for today’s Internet standards like the Transport Layer Security (TLS). This is due to its ability to perform infinite complex computations in parallel. Therefore, it is expected that quantum computers will be able to solve the key distribution problem and break every single cryptography and authentication algorithm based on Asymmetric cryptography, such as: RSA, ECC, and Diffie-Hellman. New emerging technologies such as the Internet of Things (IoT), blockchain, autonomous vehicles, 5G, Artificial Intelligence (AI) and robotics all rely on cryptography for their secure operation. Although quantum computing supports technological evolution and growth, it will pose a threat to the current progress and future development of these technologies. Therefore, cryptography and authentication techniques need improvements as introducing quantum computing would put today’s cybersecurity in serious risk. NSA and NIST proposed solutions to tackle these challenges. However, these solutions are still immature as they need major upgrade of hardware implementation. Instead, the recommendations focus on what is called post-quantum, quantum-proof, quantum-safe or quantum-resistant cryptosystems. This talk outlines how the impact of quantum technologies on authentication, data exchange, and data storage, as well as areas where cryptography plays an essential role, such as: blockchain, 5G, IoT, and AI technologies. It investigate the possible impact of quantum technologies on these emerging technologies. Finally, it discusses some of the current quantum security solution that are devoted to protect the devices and systems in the quantum era, and mitigate quantum attacks.

The security of public-key cryptography relies on well-studied hard problems, problems for which we do not have efficient algorithms. Factorization and discrete logarithm are the two most known and used hard problems. Unfortunately, they can be easily solved on a quantum computer by Shor's algorithm. Also, the research area of cryptography demands for crypto-diversity which says that we should offer a range of hard problems for public-key cryptography. If one hard problem proves to be easy, we should be able to provide alternative solutions. Some of the candidates for post-quantum hard problems, i.e. problems which are believed to be hard even on a quantum computer, are the Learning Parity with Noise (LPN), the Learning with Errors (LWE) and the Shortest Vector Problem (SVP). A thorough study of these problems is needed in order to assess their hardness. In this thesis we focus on the algorithmic study of LPN. LPN is a hard problem that is attractive, as it is believed to be post-quantum resistant and suitable for lightweight devices. In practice, it has been employed in several encryption schemes and authentication protocols. At the beginning of this thesis, we take a look at the existing LPN solving algorithms. We provide the theoretical analysis that assesses their complexity. We compare the theoretical results with practice by implementing these algorithms. We study the efficiency of all LPN solving algorithms which allow us to provide secure parameters that can be used in practice. We push further the state of the art by improving the existing algorithms with the help of two new frameworks. In the first framework, we split an LPN solving algorithm into atomic steps. We study their complexity, how they impact the other steps and we construct an algorithm that optimises their use. Given an LPN instance that is characterized by the noise level and the secret size, our algorithm provides the steps to follow in order to solve the instance with optimal complexity. In this way, we can assess if an LPN instance provides the security we require and we show what are the secure instances for the applications that rely on LPN. The second framework handles problems that can be decomposed into steps of equal complexity. Here, we assume that we have an adversary that has access to a finite or infinite number of instances of the same problem. The goal of the adversary is to succeed in just one instance as soon as possible. Our framework provides the strategy that achieves this. We characterize an LPN solving algorithm in this framework and show that we can improve its complexity in the scenario where the adversary is restricted. We show that other problems, like password guessing, can be modeled in the same framework.

The Internet of Things (IoT) carries the meaning of a developing prototype that focuses on the connection between devices, objects, sensors and users. Using IoT, the method of communications and interactions changes in form of ‘Human to Thing’ to ‘Thing to Thing’. IoT technology is rapidly evolving and spreading its technology influence across various domains including healthcare (personal and enterprise), smart campuses, smart homes, smart mobility, and Industry 4.0. As a result, a lot of IoT devices are being used as a crucial component as one of human made high-tech butlers of everyday lifestyle. This paper focuses on the IoT security threats and vulnerabilities that shall be considered in the implementation of IoT ecosystem by conducting a research in the area of smart home environment. The current security threats and vulnerabilities are being discussed in the contexts of the IoT architecture layers, which is the application layer, network layer and physical layer are also presented. This paper also provides an analysis of the possible threats and proposes possible solutions for improving the security of the IoT smart home, that may influence as beneficial factors that are able to promote a secured eco-system of IoT devices in smart home operational environment.

There are cryptographic systems that are secure against attacks by both quantum and classical computers. Some of these systems are based on the Binary Ring-LWE problem which is presumed to be difficult to solve even on a quantum computer. This problem is considered secure for IoT (Internet of things) devices with limited resources. In Binary Ring-LWE, a polynomial a is selected randomly and a polynomial b is calculated as b = a.s + e where the secret s and the noise e are polynomials with binary coefficients. The polynomials b and a are public and the secret s is hard to find. However, there are Side Channel Attacks that can be applied to retrieve some coefficients (random known bits) of s and e. In this work, we analyze that the secret s can be retrieved successfully having at least 50 % of random known bits of s and e.

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

Introduction. Widespread use of unmanned aerial vehicles in the civilian and military spheres requires the development of new algorithms for identification friend or foe of targets, as used in the Armed Forces of Ukraine (AFU) devices of the "Parol" system are designed to service approximately 110 objects military equipment. AFU automation systems allow the use of additional sources of information about various objects from civil or special data transmission networks, which can be the basis for building a networked multi-level system of state recognition. Predictions of the development of quantum computers foresee the possibility of breaking modern algorithms for information security in polynomial time in the next 5-10 years, which requires the development and implementation of new encryption algorithms and revision of modern parameters. The purpose of the article is to develop a new algorithm for state recognition of objects, which can be scaled to process the required number of manned and unmanned aerial vehicles. Potential threats to classical cryptographic protection algorithms for data networks, which will result in the execution of algorithms such as Grover and Shore on quantum computers, were also discussed. Results. The article proposes a new multilevel algorithm of state recognition based on modern cryptographic methods of information protection, which allows to perform reliable automated identification of objects, scale systems using data on potential targets from other sources through secure special networks. Grover's search algorithm does not give a strong increase in key search performance for symmetric encryption algorithms, so there is no need to increase the key lengths for this type of information security algorithms. Post-quantum asymmetric encryption algorithms require additional study and comprehensive testing of information security or increasing the key lengths of cryptographic algorithms, which corresponds to the number of qubits, i.e. more than twice. The most promising is the family of asymmetric post-quantum cryptographic algorithms based on supersingular isogenic elliptic curves. Conclusions. The developed algorithm of identification friend or foe of objects is more secure compared to existing algorithms and is focused on the use of modern on-board computers and programmable radio modems. Shore's algorithm and the like will be a significant threat to modern asymmetric cryptography algorithms when the number of qubits of quantum computers exceeds the number of bits in public keys more than twice. Keywords: identification friend or foe, symmetric encryption, asymmetric cryptography, quantum computer, post-quantum cryptography.

With the advancement of 5G mobile telecommunication, various IoT (Internet of Things) devices communicate massive amounts of data by being connected to wireless networks. Since this wireless communication is vulnerable to hackers via data leakage during communication, the transmitted data should be encrypted through block ciphers to protect the data during communication. In addition, in order to encrypt the massive amounts of data securely, it is essential to apply one of secure mode of operation. Among them, CTR (CounTeR) mode is the most widely used in industrial applications. However, these IoT devices have limited resources of computing and memory compared to typical computers, so that it is challenging to process cryptographic algorithms that have computation-intensive tasks in IoT devices at high speed. Thus, it is required that cryptographic algorithms are optimized in IoT devices. In other words, optimizing cryptographic operations on these IoT devices is not only basic but also an essential effort in order to build secure IoT-based service systems. For efficient encryption on IoT devices, even though several ARX (Add-Rotate-XOR)-based ciphers have been proposed, it still necessary to improve the performance of encryption for smooth and secure IoT services. In this article, we propose the first parallel implementations of CTR mode of ARX-based ciphers: LEA (Lightweight Encryption Algorithm), HIGHT (high security and light weight), and revised CHAM on the ARMv8 platform, a popular microcontroller in various IoT applications. For the parallel implementation, we propose an efficient data parallelism technique and register scheduling, which maximizes the usage of vector registers. Through proposed techniques, we process the maximum amount of encryption simultaneously by utilizing all vector registers. Namely, in the case of HIGHT and revised CHAM-64/128 (resp. LEA, revised CHAM-128/128, and CHAM-128/256), we can execute 48 (resp. 24) encryptions simultaneously. In addition, we optimize the process of CTR mode by pre-computing and using the intermediate value of some initial rounds by utilizing the property that the nonce part of CTR mode input is fixed during encryptions. Through the pre-computation table, CTR mode is optimized up until round 4 in LEA, round 5 in HIGHT, and round 7 in revised CHAM. With the proposed parallel processing technique, our software provides 3.09%, 5.26%, and 9.52% of improved performance in LEA, HIGHT, and revised CHAM-64/128, respectively, compared to the existing parallel works in ARM-based MCU. Furthermore, with the proposed CTR mode optimization technique, our software provides the most improved performance with 8.76%, 8.62%, and 15.87% in LEA-CTR, HIGHT-CTR, and revised CHAM-CTR, respectively. This work is the fastest implementation of CTR mode on ARMv8 architecture to the best of our knowledge.