arXiv subjects

William J. Buchanan

Publications and source records attributed to William J. Buchanan.

At least 19 recordsLinked to original sources

arXiv2026

LLM vs. SAST: A Technical Analysis on Detecting Coding Bugs of GPT4-Advanced Data Analysis

Large language models (LLMs) are increasingly used for code understanding, yet their practical effectiveness for vulnerability detection relative to Static Application Security Testing (SAST) remains insufficiently quantified. We present a controlled comparative study between GPT-4 (Advanced Data Analysis) and two SAST tools (SonarQube and Cloud Defence) on 32 curated security scenarios representing common coding pitfalls. Each scenario is scored with a binary detection rule, the two SAST outputs are aggregated using a logical OR baseline, and paired outcomes are evaluated using McNemar's test for statistical significance. In our dataset, GPT-4 correctly detected 30 of 32 scenarios (93.75\%), while the aggregated SAST baseline detected 11 of 32. The paired comparison shows a statistically significant difference in detection performance in favour of GPT-4. We also discuss security considerations and operational constraints for integrating LLM-enhanced vulnerability scanning into secure software development workflows.

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arXiv2026

Eccfrog512ck2: An Enhanced 512-bit Weierstrass Elliptic Curve

Whilst many key exchange and digital signature methods use the NIST P256 (secp256r1) and secp256k1 curves, there is often a demand for increased security. With these curves, we have a 128-bit security. These security levels can be increased to 256-bit security with NIST P-521 Curve 448 and Brainpool-P512. This paper outlines a new curve - Eccfrog512ck2 - and which provides 256-bit security and enhanced performance over NIST P-521. Along with this, it has side-channel resistance and is designed to avoid weaknesses such as related to the MOV attack. It shows that Eccfrog512ck2 can have a 61.5% speed-up on scalar multiplication and a 33.3% speed-up on point generation over the NIST P-521 curve.

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arXiv2026

ECCFROG522PP: An Enhanced 522-bit Weierstrass Elliptic Curve

Whilst many key exchange and digital signature systems still rely on NIST P-256 (secp256r1) and secp256k1, offering around 128-bit security, there is an increasing demand for transparent and reproducible curves at the 256-bit security level. Standard higher-security options include NIST P-521, Curve448, and Brainpool-P512. This paper presents ECCFROG522PP ("Presunto Powered"), a 522-bit prime-field elliptic curve that delivers security in the same classical approx 260-bit ballpark as NIST P-521, but with a fundamentally different design philosophy. All of the curve parameters are deterministically derived from a fixed public seed via BLAKE3, with zero hidden choices. The curve has prime order (cofactor = 1), a verified twist with a proven approx 505-bit prime factor, safe embedding degree (greater than or equal to 14), and passes anti-MOV checks up to k less than or equal to 200 and CM discriminant sanity up to 100k. Unlike prior opaque or ad-hoc constructions, ECCFROG522PP is fully reproducible: anyone can regenerate and verify it byte-for-byte using the published scripts. The intent is not to outperform NIST P-521 in raw speed, but to maximise trust, verifiability, and long-term auditability in a practical curve of equivalent security level

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arXiv2025

Privacy-Preserving Credit Card Approval Using Homomorphic SVM: Toward Secure Inference in FinTech Applications

The growing use of machine learning in cloud environments raises critical concerns about data security and privacy, especially in finance. Fully Homomorphic Encryption (FHE) offers a solution by enabling computations on encrypted data, but its high computational cost limits practicality. In this paper, we propose PP-FinTech, a privacy-preserving scheme for financial applications that employs a CKKS-based encrypted soft-margin SVM, enhanced with a hybrid kernel for modeling non-linear patterns and an adaptive thresholding mechanism for robust encrypted classification. Experiments on the Credit Card Approval dataset demonstrate comparable performance to the plaintext models, highlighting PP-FinTech's ability to balance privacy, and efficiency in secure financial ML systems.

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arXiv2025

Breaking ECDSA with Two Affinely Related Nonces

The security of the Elliptic Curve Digital Signature Algorithm (ECDSA) depends on the uniqueness and secrecy of the nonce, which is used in each signature. While it is well understood that nonce $k$ reuse across two distinct messages can leak the private key, we show that even if a distinct value is used for $k_2$, where an affine relationship exists in the form of: $k_m = a \cdot k_n + b$, we can also recover the private key. Our method requires only two signatures (even over the same message) and relies purely on algebra, with no need for lattice reduction or brute-force search(if the relationship, or offset, is known). To our knowledge, this is the first closed-form derivation of the ECDSA private key from only two signatures over the same message, under a known affine relationship between nonces.

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arXiv2025

ECDSA Cracking Methods

The ECDSA (Elliptic Curve Digital Signature Algorithm) is used in many blockchain networks for digital signatures. This includes the Bitcoin and the Ethereum blockchains. While it has good performance levels and as strong current security, it should be handled with care. This care typically relates to the usage of the nonce value which is used to create the signature. This paper outlines the methods that can be used to break ECDSA signatures, including revealed nonces, weak nonce choice, nonce reuse, two keys and shared nonces, and fault attack.

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arXiv2025

Cross-Platform Benchmarking of the FHE Libraries: Novel Insights into SEAL and Openfhe

The rapid growth of cloud computing and data-driven applications has amplified privacy concerns, driven by the increasing demand to process sensitive data securely. Homomorphic encryption (HE) has become a vital solution for addressing these concerns by enabling computations on encrypted data without revealing its contents. This paper provides a comprehensive evaluation of two leading HE libraries, SEAL and OpenFHE, examining their performance, usability, and support for prominent HE schemes such as BGV and CKKS. Our analysis highlights computational efficiency, memory usage, and scalability across Linux and Windows platforms, emphasizing their applicability in real-world scenarios. Results reveal that Linux outperforms Windows in computation efficiency, with OpenFHE emerging as the optimal choice across diverse cryptographic settings. This paper provides valuable insights for researchers and practitioners to advance privacy-preserving applications using FHE.

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arXiv2025

Performance Evaluation of Threshold Signing Schemes in Cryptography

Threshold Signature Scheme (TSS) protocols have gained significant attention over the past ten years due to their widespread adoption in cryptocurrencies. The adoption is mainly boosted by Gennaro and Goldfedder's TSS protocol. Since then, various TSS protocols have been introduced with different features, such as security and performance, etc. Large organizations are using TSS protocols to protect many digital assets, such as cryptocurrency. However, the adoption of these TSS protocols requires an understanding of state-of-the-art research in threshold signing. This study describes the holistic view of TSS protocols, evaluates cutting-edge TSS protocols, highlights their characteristics, and compares them in terms of security and performance. The evaluation of these TSS protocols will help the researchers address real-world problems by considering the relevant merits of different TSS protocols.

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arXiv2024

DID:RING: Ring Signatures using Decentralised Identifiers For Privacy-Aware Identity

Decentralised identifiers have become a standardised element of digital identity architecture, with supra-national organisations such as the European Union adopting them as a key component for a unified European digital identity ledger. This paper delves into enhancing security and privacy features within decentralised identifiers by integrating ring signatures as an alternative verification method. This allows users to identify themselves through digital signatures without revealing which public key they used. To this end, the study proposed a novel decentralised identity method showcased in a decentralised identifier-based architectural framework. Additionally, the investigation assesses the repercussions of employing this new method in the verification process, focusing specifically on privacy and security aspects. Although ring signatures are an established asset of cryptographic protocols, this paper seeks to leverage their capabilities in the evolving domain of digital identities.

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arXiv2024

RNA-TransCrypt: Image Encryption Using Chaotic RNA Encoding, Novel Transformative Substitution, and Tailored Cryptographic Operations

Given the security concerns of Internet of Things (IoT) networks and limited computational resources of IoT devices, this paper presents RNA-TransCrypt, a novel image encryption scheme that is not only highly secure but also efficient and lightweight. RNA-TransCrypt integrates the biocryptographic properties of RNA encoding with the non-linearity and unpredictability of chaos theory. This scheme introduces three novel contributions: 1) the two-base RNA encoding method, which transforms the image into RNA strands-like sequence, ensuring efficient scrambling; 2) the transformative substitution technique, which transforms the s-box values before replacing the pixel values, and is responsible for making the scheme lightweight; and 3) three mathematical cryptographic operations designed especially for image encryption that ensure the effective transformation of the s-box values, resulting in a new outcome even for the same input values. These modules are key-dependent, utilizing chaotic keys generated by the De Jong Fractal Map and the Van der Pol Oscillator. Extensive security analysis, including histogram analysis, correlation analysis, and the results of the statistical security parameters obtained from the Gray-Level Co-occurrence Matrix (GLCM) validate the efficacy of the proposed scheme in encrypting input images with close-to-ideal results of 7.997 entropy and 0.0006 correlation.

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arXiv2023

PermutEx: Feature-Extraction-Based Permutation -- A New Diffusion Scheme for Image Encryption Algorithms

Traditional permutation schemes mostly focus on random scrambling of pixels, often neglecting the intrinsic image information that could enhance diffusion in image encryption algorithms. This paper introduces PermutEx, a feature-extraction-based permutation method that utilizes inherent image features to scramble pixels effectively. Unlike random permutation schemes, PermutEx extracts the spatial frequency and local contrast features of the image and ranks each pixel based on this information, identifying which pixels are more important or information-rich based on texture and edge information. In addition, a unique permutation key is generated using the Logistic-Sine Map based on chaotic behavior. The ranked pixels are permuted in conjunction with this unique key, effectively permuting the original image into a scrambled version. Experimental results indicate that the proposed method effectively disrupts the correlation in information-rich areas within the image resulting in a correlation value of 0.000062. The effective scrambling of pixels, resulting in nearly zero correlation, makes this method suitable to be used as diffusion in image encryption algorithms.

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arXiv2023

PASSION: Permissioned Access Control for Segmented Devices and Identity for IoT Networks

In recent years, there has been a significant proliferation of industrial Internet of Things (IoT) applications, with a wide variety of use cases being developed and put into operation. As the industrial IoT landscape expands, the establishment of secure and reliable infrastructure becomes crucial to instil trust among users and stakeholders, particularly in addressing fundamental concerns such as traceability, integrity protection, and privacy that some industries still encounter today. This paper introduces a privacy-preserving method in the industry's IoT systems using blockchain-based data access control for remote industry safety monitoring and maintaining event information confidentiality, integrity and authenticity.

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arXiv2023

CellSecure: Securing Image Data in Industrial Internet-of-Things via Cellular Automata and Chaos-Based Encryption

In the era of Industrial IoT (IIoT) and Industry 4.0, ensuring secure data transmission has become a critical concern. Among other data types, images are widely transmitted and utilized across various IIoT applications, ranging from sensor-generated visual data and real-time remote monitoring to quality control in production lines. The encryption of these images is essential for maintaining operational integrity, data confidentiality, and seamless integration with analytics platforms. This paper addresses these critical concerns by proposing a robust image encryption algorithm tailored for IIoT and Cyber-Physical Systems (CPS). The algorithm combines Rule-30 cellular automata with chaotic scrambling and substitution. The Rule 30 cellular automata serves as an efficient mechanism for generating pseudo-random sequences that enable fast encryption and decryption cycles suitable for real-time sensor data in industrial settings. Most importantly, it induces non-linearity in the encryption algorithm. Furthermore, to increase the chaotic range and keyspace of the algorithm, which is vital for security in distributed industrial networks, a hybrid chaotic map, i.e., logistic-sine map is utilized. Extensive security analysis has been carried out to validate the efficacy of the proposed algorithm. Results indicate that our algorithm achieves close-to-ideal values, with an entropy of 7.99 and a correlation of 0.002. This enhances the algorithm's resilience against potential cyber-attacks in the industrial domain.

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arXiv2023

SRSS: A New Chaos-Based Single-Round Single S-Box Image Encryption Scheme for Highly Auto-Correlated Data

With the advent of digital communication, securing digital images during transmission and storage has become a critical concern. The traditional s-box substitution methods often fail to effectively conceal the information within highly auto-correlated regions of an image. This paper addresses the security issues presented by three prevalent S-box substitution methods, i.e., single S-box, multiple S-boxes, and multiple rounds with multiple S-boxes, especially when handling images with highly auto-correlated pixels. To resolve the addressed security issues, this paper proposes a new scheme SRSS-the Single Round Single S-Box encryption scheme. SRSS uses a single S-box for substitution in just one round to break the pixel correlations and encrypt the plaintext image effectively. Additionally, this paper introduces a new Chaos-based Random Operation Selection System-CROSS, which nullifies the requirement for multiple S-boxes, thus reducing the encryption scheme's complexity. By randomly selecting the operation to be performed on each pixel, driven by a chaotic sequence, the proposed scheme effectively scrambles even high auto-correlation areas. When compared to the substitution methods mentioned above, the proposed encryption scheme exhibited exceptionally well in just a single round with a single S-box. The close-to-ideal statistical security analysis results, i.e., an entropy of 7.89 and a correlation coefficient of 0.007, validate the effectiveness of the proposed scheme. This research offers an innovative path forward for securing images in applications requiring low computational complexity and fast encryption and decryption speeds.

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arXiv2022

A DNA Based Colour Image Encryption Scheme Using A Convolutional Autoencoder

With the advancement in technology, digital images can easily be transmitted and stored over the Internet. Encryption is used to avoid illegal interception of digital images. Encrypting large-sized colour images in their original dimension generally results in low encryption/decryption speed along with exerting a burden on the limited bandwidth of the transmission channel. To address the aforementioned issues, a new encryption scheme for colour images employing convolutional autoencoder, DNA and chaos is presented in this paper. The proposed scheme has two main modules, the dimensionality conversion module using the proposed convolutional autoencoder, and the encryption/decryption module using DNA and chaos. The dimension of the input colour image is first reduced from N $\times$ M $\times$ 3 to P $\times$ Q gray-scale image using the encoder. Encryption and decryption are then performed in the reduced dimension space. The decrypted gray-scale image is upsampled to obtain the original colour image having dimension N $\times$ M $\times$ 3. The training and validation accuracy of the proposed autoencoder is 97% and 95%, respectively. Once the autoencoder is trained, it can be used to reduce and subsequently increase the dimension of any arbitrary input colour image. The efficacy of the designed autoencoder has been demonstrated by the successful reconstruction of the compressed image into the original colour image with negligible perceptual distortion. The second major contribution presented in this paper is an image encryption scheme using DNA along with multiple chaotic sequences and substitution boxes. The security of the proposed image encryption algorithm has been gauged using several evaluation parameters, such as histogram of the cipher image, entropy, NPCR, UACI, key sensitivity, contrast, etc. encryption.

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arXiv2022

Comparison of Entropy Calculation Methods for Ransomware Encrypted File Identification

Ransomware is a malicious class of software that utilises encryption to implement an attack on system availability. The target's data remains encrypted and is held captive by the attacker until a ransom demand is met. A common approach used by many crypto-ransomware detection techniques is to monitor file system activity and attempt to identify encrypted files being written to disk, often using a file's entropy as an indicator of encryption. However, often in the description of these techniques, little or no discussion is made as to why a particular entropy calculation technique is selected or any justification given as to why one technique is selected over the alternatives. The Shannon method of entropy calculation is the most commonly-used technique when it comes to file encryption identification in crypto-ransomware detection techniques. Overall, correctly encrypted data should be indistinguishable from random data, so apart from the standard mathematical entropy calculations such as Chi-Square, Shannon Entropy and Serial Correlation, the test suites used to validate the output from pseudo-random number generators would also be suited to perform this analysis. he hypothesis being that there is a fundamental difference between different entropy methods and that the best methods may be used to better detect ransomware encrypted files. The paper compares the accuracy of 53 distinct tests in being able to differentiate between encrypted data and other file types. The testing is broken down into two phases, the first phase is used to identify potential candidate tests, and a second phase where these candidates are thoroughly evaluated. To ensure that the tests were sufficiently robust, the NapierOne dataset is used. This dataset contains thousands of examples of the most commonly used file types, as well as examples of files that have been encrypted by crypto-ransomware.

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arXiv2022

GLASS: A Citizen-Centric Distributed Data-Sharing Model within an e-Governance Architecture

E-governance is a process that aims to enhance a government's ability to simplify all the processes that may involve government, citizens, businesses, and so on. The rapid evolution of digital technologies has often created the necessity for the establishment of an e-Governance model. There is often a need for an inclusive e-governance model with integrated multiactor governance services and where a single market approach can be adopted. e-Governance often aims to minimise bureaucratic processes, while at the same time including a digital-by-default approach to public services. This aims at administrative efficiency and the reduction of bureaucratic processes. It can also improve government capabilities, and enhances trust and security, which brings confidence in governmental transactions. However, solid implementations of a distributed data sharing model within an e-governance architecture is far from a reality; hence, citizens of European countries often go through the tedious process of having their confidential information verified. This paper focuses on the sinGLe sign-on e-GovernAnce Paradigm based on a distributed file-exchange network for security, transparency, cost-effectiveness and trust (GLASS) model, which aims to ensure that a citizen can control their relationship with governmental agencies. The paper thus proposes an approach that integrates a permissioned blockchain with the InterPlanetary File System (IPFS). This method demonstrates how we may encrypt and store verifiable credentials of the GLASS ecosystem, such as academic awards, ID documents and so on, within IPFS in a secure manner and thus only allow trusted users to read a blockchain record, and obtain the encryption key. This allows for the decryption of a given verifiable credential that stored on IPFS. This paper outlines the creation of a demonstrator that proves the principles of the GLASS approach.

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arXiv2022

Ransomware: Analysing the Impact on Windows Active Directory Domain Services

Ransomware has become an increasingly popular type of malware across the past decade and continues to rise in popularity due to its high profitability. Organisations and enterprises have become prime targets for ransomware as they are more likely to succumb to ransom demands as part of operating expenses to counter the cost incurred from downtime. Despite the prevalence of ransomware as a threat towards organisations, there is very little information outlining how ransomware affects Windows Server environments, and particularly its proprietary domain services such as Active Directory. Hence, we aim to increase the cyber situational awareness of organisations and corporations that utilise these environments. Dynamic analysis was performed using three ransomware variants to uncover how crypto-ransomware affects Windows Server-specific services and processes. Our work outlines the practical investigation undertaken as WannaCry, TeslaCrypt, and Jigsaw were acquired and tested against several domain services. The findings showed that none of the three variants stopped the processes and decidedly left all domain services untouched. However, although the services remained operational, they became uniquely dysfunctional as ransomware encrypted the files pertaining to those services

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