Cryptography, Volume 5, Issue 4 (December 2021) – 13 articles

Cryptography has been used from time immemorial for preserving the confidentiality of data/information in storage or transit. Thus, cryptography research has also been evolving from the classical Caesar cipher to the modern cryptosystems, based on modular arithmetic to the contemporary cryptosystems based on quantum computing. The emergence of quantum computing poses a major threat to the modern cryptosystems based on modular arithmetic, whereby even the computationally hard problems which constitute the strength of the modular arithmetic ciphers could be solved in polynomial time. This threat triggered post-quantum cryptography research to design and develop post-quantum algorithms that can withstand quantum computing attacks. This paper provides an overview of the various research directions that have been explored in post-quantum cryptography and, specifically, the various code-based cryptography research dimensions that have been explored. Some potential research directions that are yet to be explored in code-based cryptography research from the perspective of codes is a key contribution of this paper. Full article

The need for cloud storage grows day after day due to its reliable and scalable nature. The storage and maintenance of user data at a remote location are severe issues due to the difficulty of ensuring data privacy and confidentiality. Some security issues within current cloud systems are managed by a cloud third party (CTP), who may turn into an untrustworthy insider part. This paper presents an automated Encryption/Decryption System for Cloud Data Storage (AEDS) based on hybrid cryptography algorithms to improve data security and ensure confidentiality without interference from CTP. Three encryption approaches are implemented to achieve high performance and efficiency: Automated Sequential Cryptography (ASC), Automated Random Cryptography (ARC), and Improved Automated Random Cryptography (IARC) for data blocks. In the IARC approach, we have presented a novel encryption strategy by converting the static S-box in the AES algorithm to a dynamic S-box. Furthermore, the algorithms RSA and Twofish are used to encrypt the generated keys to enhance privacy issues. We have evaluated our approaches with other existing symmetrical key algorithms such as DES, 3DES, and RC2. Although the two proposed ARC and ASC approaches are more complicated, they take less time than DES, DES3, and RC2 in processing the data and obtaining better performance in data throughput and confidentiality. ARC outperformed all of the other algorithms in the comparison. The ARC’s encrypting process has saved time compared with other algorithms, where its encryption time has been recorded as 22.58 s for a 500 MB file size, while the DES, 3DES, and RC2 have completed the encryption process in 44.43, 135.65, and 66.91 s, respectively, for the same file size. Nevertheless, when the file sizes increased to 2.2 GB, the ASC proved its efficiency in completing the encryption process in less time. Full article

In the last four years, the evolution and adoption of blockchain and, more generally, distributed ledger systems have shown the affirmation of many concepts and models with significant differences in system governance and suitable applications. This work aims to analyze distributed ledger technology (DLT) critically. Starting from the topical idea of decentralization, we introduce concepts and building blocks currently adopted in the available systems centering on their functional aspects and impact on possible applications. We present some conceptual framing tools helpful in the application context: a DLT reference architecture, off-chain and on-chain governance models, and classification of consensus protocols. Finally, we introduce the concept of process authenticity, reviewing tools and strategies for integrating DLT with the physical world and proposing a constructive scheme for the authentication of a physical resource through alphanumeric data. Full article

The design and implementation of two-factor schemes designed for roaming mobile users for global mobility networks in smart cities requires attention to protect the scheme from various security attacks, such as the replay attack, impersonation attack, man-in-the-middle attack, password-guessing attack and stolen-smart-card attack. In addition to these attacks, the scheme should achieve user anonymity, unlinkability and perfect forward secrecy. In the roaming scenario, as mobile users are connected to the foreign network, mobile users must provide authentication details to the foreign network to which they are connected. The foreign network forwards the authentication messages received from the mobile users to their home network. The home network validates the authenticity of the mobile user. In the roaming scenario, all communication between the three entities is carried over an insecure channel. It is assumed that the adversary has the capabilities to intercept the messages transmitted over an insecure channel. Hence, the authentication scheme designed must be able to resist the above-mentioned security attacks and achieve the security goals. Our proposed scheme ES-HAS (elliptic curve-based secure handover authentication scheme) is a two-factor authentication scheme in which the mobile user possesses the password, and the smart card resists the above-mentioned security attacks. It also achieves the above-mentioned security goals. We also extended our two-factor authentication to a multi-factor authentication scheme using the fingerprint biometric technique. The formal security analysis using BAN logic and the formal security verification of the proposed scheme using the widely accepted AVISPA (automated validation of internet security protocols and applications) tool is presented in this article. In comparison with the related schemes, the proposed scheme is more efficient and robust. This makes the proposed scheme suitable for practical implementation. Full article

Cryptography is traditionally considered as a main information security mechanism, providing several security services such as confidentiality, as well as data and entity authentication. This aspect is clearly relevant to the fundamental human right of privacy, in terms of securing data from eavesdropping and tampering, as well as from masquerading their origin. However, cryptography may also support several other (legal) requirements related to privacy. For example, in order to fulfil the data minimisation principle—i.e., to ensure that the personal data that are being processed are adequate and limited only to what is necessary in relation to the purposes for which they are processed—the use of advanced cryptographic techniques such as secure computations, zero-knowledge proofs or homomorphic encryption may be prerequisite. In practice though, it seems that the organisations performing personal data processing are not fully aware of such solutions, thus adopting techniques that pose risks for the rights of individuals. This paper aims to provide a generic overview of the possible cryptographic applications that suffice to address privacy challenges. In the process, we shall also state our view on the public “debate” on finding ways so as to allow law enforcement agencies to bypass the encryption of communication. Full article

The code-based McEliece and Niederreiter cryptosystems are promising candidates for post-quantum public-key encryption. Recently, q-ary concatenated codes over Gaussian integers were proposed for the McEliece cryptosystem, together with the one-Mannheim error channel, where the error values are limited to the Mannheim weight one. Due to the limited error values, the codes over Gaussian integers achieve a higher error correction capability than maximum distance separable (MDS) codes with bounded minimum distance decoding. This higher error correction capability improves the work factor regarding decoding attacks based on information-set decoding. The codes also enable a low complexity decoding algorithm for decoding beyond the guaranteed error correction capability. In this work, we extend this coding scheme to codes over Eisenstein integers. These codes have advantages for the Niederreiter system. Additionally, we propose an improved code construction based on generalized concatenated codes. These codes extend to the rate region, where the work factor is beneficial compared to MDS codes. Moreover, generalized concatenated codes are more robust against structural attacks than ordinary concatenated codes. Full article

Internet of things (IoT) technologies have recently gained much interest from numerous industries, where devices, machines, sensors, or simply things are linked with each other over open communication networks. However, such an operation environment brings new security threats and technology challenges in securing and stabilizing such large systems in the IoT world. Device identity in such an environment is an essential security requirement as a secure anchor for most applications towards clone-resistant resilient operational security. This paper analyzes different contemporary authenticated identification techniques and discusses possible future technologies for physically clone-resistant IoT units. Two categories of identification techniques to counteract cloning IoT units are discussed. The first category is inherently cloneable and includes the classical identification mechanisms based on secret and public key cryptography. Such techniques deploy mainly secret keys stored permanently somewhere in the IoT devices as classical means to make units clone-resistant. However, such techniques are inherently cloneable as the manufacturer or device personalizers can clone them by re-using the same secret key (which must be known to somebody) or reveal keys to third parties to create cloned entities. In contrast, the second, more resilient category is inherently unclonable because it deploys unknown and hard to predict born analog modules such as physical unclonable functions (PUFs) or mutated digital modules and so-called secret unknown ciphers (SUCs). Both techniques are DNA-like identities and hard to predict and clone even by the manufacturer itself. Born PUFs were introduced two decades ago; however, PUFs as analog functions failed to serve as practically usable unclonable electronic identities due to being costly, unstable/inconsistent, and non-practical for mass application. To overcome the drawbacks of analog PUFs, SUCs techniques were introduced a decade ago. SUCs, as mutated modules, are highly consistent, being digital modules. However, as self-mutated digital modules, they offer only clone-resistant identities. Therefore, the SUC technique is proposed as a promising clone-resistant technology embedded in emerging IoT units in non-volatile self-reconfiguring devices. The main threats and expected security requirements in the emerging IoT applications are postulated. Finally, the presented techniques are analyzed, classified, and compared considering security, performance, and complexity given future expected IoT security features and requirements. Full article

The learning with errors (LWE) problem is one of the main mathematical foundations of post-quantum cryptography. One of the main groups of algorithms for solving LWE is the Blum–Kalai–Wasserman (BKW) algorithm. This paper presents new improvements of BKW-style algorithms for solving LWE instances. We target minimum concrete complexity, and we introduce a new reduction step where we partially reduce the last position in an iteration and finish the reduction in the next iteration, allowing non-integer step sizes. We also introduce a new procedure in the secret recovery by mapping the problem to binary problems and applying the fast Walsh Hadamard transform. The complexity of the resulting algorithm compares favorably with all other previous approaches, including lattice sieving. We additionally show the steps of implementing the approach for large LWE problem instances. We provide two implementations of the algorithm, one RAM-based approach that is optimized for speed, and one file-based approach which overcomes RAM limitations by using file-based storage. Full article

This paper studies the use of deep learning (DL) models under a known-plaintext scenario. The goal of the models is to predict the secret key of a cipher using DL techniques. We investigate the DL techniques against different ciphers, namely, Simplified Data Encryption Standard (S-DES), Speck, Simeck and Katan. For S-DES, we examine the classification of the full key set, and the results are better than a random guess. However, we found that it is difficult to apply the same classification model beyond 2-round Speck. We also demonstrate that DL models trained under a known-plaintext scenario can successfully recover the random key of S-DES. However, the same method has been less successful when applied to modern ciphers Speck, Simeck, and Katan. The ciphers Simeck and Katan are further investigated using the DL models but with a text-based key. This application found the linear approximations between the plaintext–ciphertext pairs and the text-based key. Full article

Individual users’ sensitive information, such as heart rate, calories burned, or even sleep patterns, are casually tracked by smart wearable devices to be further processed or exchanged, utilizing the ubiquitous capabilities of Internet of Things (IoT) technologies. This work aims to explore the existing literature on various data privacy concerns, posed by the use of wearable devices, and experimentally analyze the data exchanged through mobile applications, in order to identify the underlying privacy and security risks. Emulating a man-in-the-middle attack scenario, five different commercial fitness tracking bands are examined, in order to test and analyze all data transmitted by each vendor’s suggested applications. The amount of personal data collected, processed, and transmitted for advertising purposes was significant and, in some cases, highly affected the network’s total overhead. Some of the applications examined requested access for sensitive data driven device functionalities, such as messaging, phone calling, audio recording, and camera usage, without any clear or specific reason stated by their privacy policy. This paper concludes by listing the most critical aspects in terms of privacy and security concerning some of the most popular commercial fitness tracking applications. Full article

According to recent security analysis reports, malicious software (a.k.a. malware) is rising at an alarming rate in numbers, complexity, and harmful purposes to compromise the security of modern computer systems. Recently, malware detection based on low-level hardware features (e.g., Hardware Performance Counters (HPCs) information) has emerged as an effective alternative solution to address the complexity and performance overheads of traditional software-based detection methods. Hardware-assisted Malware Detection (HMD) techniques depend on standard Machine Learning (ML) classifiers to detect signatures of malicious applications by monitoring built-in HPC registers during execution at run-time. Prior HMD methods though effective have limited their study on detecting malicious applications that are spawned as a separate thread during application execution, hence detecting stealthy malware patterns at run-time remains a critical challenge. Stealthy malware refers to harmful cyber attacks in which malicious code is hidden within benign applications and remains undetected by traditional malware detection approaches. In this paper, we first present a comprehensive review of recent advances in hardware-assisted malware detection studies that have used standard ML techniques to detect the malware signatures. Next, to address the challenge of stealthy malware detection at the processor’s hardware level, we propose StealthMiner, a novel specialized time series machine learning-based approach to accurately detect stealthy malware trace at run-time using branch instructions, the most prominent HPC feature. StealthMiner is based on a lightweight time series Fully Convolutional Neural Network (FCN) model that automatically identifies potentially contaminated samples in HPC-based time series data and utilizes them to accurately recognize the trace of stealthy malware. Our analysis demonstrates that using state-of-the-art ML-based malware detection methods is not effective in detecting stealthy malware samples since the captured HPC data not only represents malware but also carries benign applications’ microarchitectural data. The experimental results demonstrate that with the aid of our novel intelligent approach, stealthy malware can be detected at run-time with 94% detection performance on average with only one HPC feature, outperforming the detection performance of state-of-the-art HMD and general time series classification methods by up to 42% and 36%, respectively. Full article

In this paper, we propose and present secure multiparty computation (SMC) protocols for single-source shortest distance (SSSD) and all-pairs shortest distance (APSD) in sparse and dense graphs. Our protocols follow the structure of classical algorithms—Bellman–Ford and Dijkstra for SSSD; Johnson, Floyd–Warshall, and transitive closure for APSD. As the computational platforms offered by SMC protocol sets have performance profiles that differ from typical processors, we had to perform extensive changes to the structure (including their control flow and memory accesses) and the details of these algorithms in order to obtain good performance. We implemented our protocols on top of the secret sharing based protocol set offered by the Sharemind SMC platform, using single-instruction-multiple-data (SIMD) operations as much as possible to reduce the round complexity. We benchmarked our protocols under several different parameters for network performance and compared our performance figures against each other and with ones reported previously. Full article

A differential fault attack framework for the Enocoro family of stream ciphers is presented. We only require that the attacker can reset the internal state and inject a random byte-fault, in a random register, during a known time period. For a single fault injection, we develop a differential clocking algorithm that computes a set of linear equations in the in- and output differences of the non-linear parts of the cipher and relates them to the differential keystream. The usage of these equations is two-fold. Firstly, one can determine those differentials that can be computed from the faulty keystream, and secondly they help to pin down the actual location and timing of the fault injection. Combining these results, each fault injection gives us information on specific small parts of the internal state. By encoding the information we gain from several fault injections using the weighted Horn clauses, we construct a guessing path that can be used to quickly retrieve the internal state using a suitable heuristic. Finally, we evaluate our framework with the ISO-standardized and CRYPTREC candidate recommended cipher Enocoro-128v2. Simulations show that, on average, the secret key can be retrieved within 20 min on a standard workstation using less than five fault injections. Full article