Conference Publications

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A comprehensive exploration of digital forensics investigations in embedded systems, ubiquitous computing, fog computing, and edge computing
(2024-08) Nelufule, Nthatheni; Singano, Zothile T; Masango, Mfundo G
The rapid evolution of digital ecosystems, characterized by the intricate interplay of diverse technologies, has necessitated a shift in the digital forensics’ paradigm. Traditional investigative methods are inadequate to perform digital forensic exercises in the new paradigm of dynamic digital ecosystem landscapes. The emergence of complex digital ecosystems encompassing an array of interconnected devices and data repositories poses formidable challenges for conventional digital forensics. There is a dire need to adapt and advance digital forensic methodologies to effectively combat cybercrime because the evolving landscape of digital ecosystems presents a critical juncture for the field of digital forensics. This study proposes a systematic literature review to understand the extent of these challenges and proposes a collaborative and innovative approach to digital forensic investigation within the context of digital ecosystems. The proposed approach emphasizes collaboration across diverse sectors and integration of innovative technologies by combining a spectrum of digital forensic experts, technologists, and legal professionals to produce a massive wealth of collective intelligence.
A decentralized cyber threat information (CTI) sharing platform
(2024-08) Singano, Zothile T; Mthethwa, Sthembile N; Ntshangase, Cynthia S
Embracing the changes in technology and cybersecurity is crucial especially for organisations. As the technology space evolves so does the techniques used by cyber attackers, it gets more sophisticated. For organisations to secure their environment, it is vital to collaborate with other organisations to fight against zero-day-attacks. This can be achieved through sharing cyber threat information. Most organisations are hesitant to share CTI because of trust. Therefore, this paper presents a DLT based CTI sharing platform, which presents a trust-less environment for sharing. The nature of DLT presents opportunities for sharing CTI in a secure and decentralized manner, thus allowing global collaboration.
A mathematical model of a commercial fuel cell for evaluating efficiency under varying ambient conditions
(2025-09) Ndebele, Bright B; Naidoo, Purusha; Ragimana, Phumudzo; Makhubo, Mamokete
Long-endurance aviation propulsion is currently dominated by carbon-based fuels. However, due to climate change, which is largely attributed to carbon dioxide (CO2) emissions from human activities, aviation contributes approximately 4 %, a more climate friendly fuel is sought. Hydrogen (H2), particularly in proton exchange membrane (PEM) fuel cells, offers a promising carbon-neutral alternative. To assess its viability, a mathematical model of a commercial fuel cell was developed to evaluate efficiency under varying ambient conditions representative of Africa climates and at high-altitude scenarios. The Nernst equation – modified to include terms for activation, polarisation, and Ohmic losses – was used to model an experimentally determined polarisation curve (current against cell voltage curve). When fit to experimental data the modified Nernst equation parameters were found to be: A = 0.0356 V, I0 = 0.0212 Amperes, Rint = 0.0059 Ohms, and Imax = 150 Amperes where A, I0, Rint and Imax are the Tafel slope, limit current, internal resistance, and maximum limit current, respectively. The influence of pressure and temperature were determined by varying the partial pressure of oxygen and temperature in the Nernst equation resulting in an efficiency map (𝜂(𝑃𝑂2,𝑇)). The maps showed that efficiency is high for high ambient pressure and low ambient temperature. However, the influence of ambient pressure and temperature were found to be insignificant compared to the influence of power drawn.
A perspective on the integration of ABB industrial robots into Open AI Gym using vision-based reinforcement learning
(2024-12) Dikole, Realeboga G; Faniso-Mnyaka, Zimbini; Sekopa, Teboho L
Reinforcement learning-based robotic manipulation has risen to prominence recently due to the rise of frameworks such as Open AI Gym. Although there has been much success in reinforcement learning applied to manipulators such as the Kuka iiwa, Franka Emika Panda, UR5 robots, there has been little to no exploration of the application of reinforcement learning on ABB robotic manipulators. This paper presents a perspective on integrating vision-based reinforcement learning and ABB robots with Open AI Gym environments. We focus mainly on the pick-and-place and push environments using proximal policy optimisation due to its simplicity and ease of memory. Our results affirm the possibility of vision-based reinforcement learning with ABBrobots with the best training performance yielding a success rate of close to sixty-five percent.
A qualitative review of zero-knowledge proofs and biometrics in decentralized identity systems
(2025) Baruni, Kedimotse P; Ntshangase, Cynthia S; Ngobeni, Harvest T; Moatshe, Lesego S; Ndhlovu, Nomalisa
This paper presents a qualitative review of the integration of Zero Knowledge Proofs (ZKPs) and biometrics in Decentralized Identity (DID) sys tems. It explores how these technologies address key challenges in digital identity management, including privacy preservation, security enhancement, and regula tory compliance. Using three research questions, the study systematically reviews the recent literature to identify the problems these technologies solve, the sectors where they are applied, and the standards that govern their implementation. The review further reveals that ZKPs-DID is the most widely adopted method, dom inating finance and governance applications, while Bio-DID focuses on healthcare and education under GDPR, and BioZK-DID combines biometrics with ZKPs for enhanced security but with limited regulatory guidance. The find ings reveal that ZKPs enable privacy-preserving verification, while biometrics offer robust user-specific authentication. Integration within DID systems is par ticularly relevant in sectors such as finance, healthcare, governance, and educa tion. However, challenges remain in scalability, interoperability, and regulatory alignment. This paper contributes new insights by proposing technical guidelines, policy recommendations, and future research directions to support the ethical and effective deployment of ZKP-biometric-enabled DID systems.
A review of PFCP cyber attacks in 5G standalone for robotic telesurgery services
(2024-10) Makondo, Ntshuxeko; Baloyi, Errol; Kobo, Hlabishi I; Mathonsi, TE
The emergence of fifth-generation technology (5G) has revolutionised telecommunication networks, offering enhanced mobile broadband, ultra-reliable (eMBB), ultra-lowlatency communications (uRLLC), and massive machine-type communication (mMTC) service classes. This breakthrough has garnered significant attention and investment worldwide, driving innovation and growth in the digital era. However, the adoption of cloud-based 5G core (5GC) networks, while offering scalability and deployment flexibility, has posed challenges to meeting stringent latency requirements, particularly for uRLLC services specifically for robotic telesurgery. To address this problem, mobile network operators (MNOs) have turned to edge computing (EC), using the control and user plane separation (CUPS) architecture introduced in the thirdgeneration partnership project (3GPP) release 14 specification. This architecture enables the deployment of the user plane function (UPF) closer to users, reducing latency, and improving quality of service (QoS). However, the deployment of the UPF as a standalone node on the edge of the network exposes the packet forwarding control protocol (PFCP) to cybersecurity attacks, which pose risks to telesurgery services and could even lead to loss of life. In the existing literature, only a few techniques focus on minimising these attacks when the UPF is deployed on the edge of the network far from the 5GC. Therefore, this paper reviews PFCP attacks and explores machine learning (ML) techniques to mitigate these security threats. This paper further provides recommendations and future research directions for mitigating these attacks.
A review of the South African public sector’s capability in combating ransomware
(2024-09) Siphambili, Nokuthaba; Mahlasela, Oyeana N; Baloyi, Errol; Mukondeleli, Elekanyani
Ransomware attacks have emerged as a significant cybersecurity threat, impacting organisations globally, including the South African public sector. This study conducted a narrative review aimed at investigating the South African public sector's capability to address ransomware attacks. The review examined news articles, reports, and literature from databases such as Scopus, Google Scholar, and ScienceDirect. Furthermore, this review explored the evolving landscape of ransomware, including its modus operandi and impacts. The findings revealed that since 2019, ten different South African public sector entities have been targeted by ransomware, with one entity being hit twice. Based of those findings, this study provided recommendations to strengthen the South African public sector's national defences and improve its preparedness for future ransomware attacks.
A strategic path for digital transformation in cyber warfare for African  militaries
(2024-03) Mphahlela, James M; Mtsweni, Jabu S
Digital disruption has changed the battlefield and increased its complexity for the war fighter. The modern battlefield continues to increase this complexity, due to the evolution of components that constitute military capability. The technologies, processes and the users are such components. The modern battlefield relies on advanced technologies tapping on high connectivity, are more lethal, precise, and autonomous. Due to this evolution, areas once thought to be safe from conventional attacks are increasingly becoming vulnerable. This evolution of technology and shorter development curves have also increased the prominence of the cyberspace, as a domain of war. However, many militaries, especially in Africa are still operating legacy systems and struggling with modernizing their systems to take advantage of the digital evolution. This paper, therefore, uses a systematic literature review and benchmarking focusing on selected super cyber power nations’ indices to propose a strategic path for African militaries to drive digital transformation in their operational environments. The roadmap is proposed to stimulate the establishment and enhancement of African militaries’ cyber warfighting capabilities in the digital age. The objectives of this digital transformation path include establishing a digital backbone, where all the sensors, effectors and the deciders are plugged to share information and intelligence.
A Study on bistatic RCS simulations, measurements and calibration
(2018) Potgieter, Monique
Bistatic RCS improves detection, characterisation and identification and enhances target information. Its stealthy targets have a low monostatic return and shaping of larger bistatic scattering.
A survey of digital forensic tools for Android and iOS smart phones
(2024-09) Ntshangase, Cynthia S; Nelufule, Nthatheni; Mulihase, Nkgomeleng D; Mtshali, Mamello L; Mokoena, Chantel JM; Moloi, Palesa MM
Mobile theft has been an increasing problem in South African cities and townships. This is also motivated by the black market for cellphone sales, but it has recently emerged that in many instances, the phone is stolen to harvest the credential and defraud and clean the victims’ bank account. Such cases are hardly reported as the success rate of prosecution is low. This is due to the lack of capacity, investigative tools, and the financial constraints of the investigative authorities. This paper presents a review of modern mobile forensic investigative tools, both open-source and commercialized. The purpose of this survey article is to present an analysis of tools in terms of their strengths and weaknesses and to simplify the work of investigators by bringing all the latest tools into one article.
A survey on the application of blockchain technology for cyber-physical systems
(2024) Nelufule, Norman; Senamela, Pertunia M; Shadung, Lesiba D; Singano, Zothile T; Masemola, Kelebogile B; Mangole, Tshegofatso C
This paper presents a systematic review of the literature on the application of blockchain technology to improve the security of cyber-physical systems. The objective of the article was to identify current challenges, evaluate existing solutions, and propose future research directions. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was used to ensure that the result of this comprehensive review is not biased. The results and key findings have highlighted that there is a potential usage of the blockchain technology to address security challenges in a cyber-physical systems, including data integrity, authentication, and secure communication. This survey paper concludes by presenting the recommendations for integrating blockchain technology into CPSs to enhance their security and resilience.
A trust framework for peer-to-peer energy markets
(2025) Leotlela, Boitumelo; Ledwaba, L; Coetzee, M
Peer-to-peer energy markets rely on trust to enable secure participation; however existing trust models often address only isolated trust concerns. This fragmented approach leaves significant gaps in ensuring holistic trust across the peer-to-peer energy market and exposes participants to threats in the market. To address this, the paper proposes a trust framework grounded in the Trust over IP (ToIP) model, which integrates technical mechanisms and governance policies to sustain trust in decentralised environments. Using the STRIDE threat model, key threats in the peer-to-peer energy market are identified, while also analysing how existing research mitigates these risks. The corresponding trust and security mechanisms are then mapped to the ToIP architecture, offering a comprehensive approach to trust establishment, that unifies social-behavioural and security dimensions of trust. By leveraging ToIP as a formal foundation for trust establishment in this work, the proposed framework provides a holistic approach to building and maintaining trust in the market, thereby fostering greater user confidence and encouraging broader market participation.
A web scraping approach towards cryptocurrency investigations
(2025-06) Mawhayi, B; Botha, Johannes G; Leenen, L
The investigation of cryptocurrency crimes is still in its infancy with no standardised process or methodology to follow. This paper describes research that forms part of a broader project led by the second author (Botha, et al., 2025). The broader project’s aim is to develop a methodology to follow when conducting cryptocurrency crime investigations. One of the steps in the proposed methodology is web scraping. The authors of this paper present a detailed exploration of web scraping techniques within the broader context of the proposed investigation methodology. In this paper, the focus is on developing a well-structured methodology for scraping social media platforms and online forums to gather data related to fraudulent activities; the goal is to find posts that include references to the wallet address of interest. This exploration uses an iterative approach; for every new cryptocurrency wallet address discovered or revealed through on-chain analysis, a parallel path is followed by scraping the Internet. If a mention of the cryptocurrency address should be discovered it is considered to be a key finding, creating a pivot point in the investigation. From a pivot point, further open-source intelligence (OSINT) techniques will be applied, though this aspect falls beyond the scope of this paper. If no relevant information or link is found, the scraping path will not be pursued, and the investigation proceeds with on-chain analysis to identify additional wallet addresses. Additionally, challenges encountered in web scraping, such as handling platform restrictions, ensuring data accuracy, and managing large volumes of data, are addressed. The goal of the proposed methodology is to enhance data extraction and analysis efficiency contributing to the proposed methodology for investigating cryptocurrency scams.
Academic and skills credentialing using distributed ledger technology (DLT) and W3C Standards: Technology assessment
(2022-12) Mthethwa, Sthembile; Pretorius, Morne
The ongoing push for the 4th industrial revolution is setting the stage to digitise, persist and verify identity along with credentials. Academic and skills credentials are currently verified manually and have much scope for automation using cryptographic techniques but requires standardisation to facilitate future systems interoperability. The Distributed Ledger Technology (DLT) and World Wide Web Consortium (W3C) Verifiable Credentials (VC) standards presents the possibility to achieve this credential verification automation. To accomplish this, an understanding of various DLTs and requirements for a viable skills tracking system is important. Therefore, this research aims to access the selected DLTs against the assessment criterion presented and an analysis has been completed to determine which DLT is suitable for the proposed system. The DLTs are assessed in terms of their ability to support the rapid prototyping of such a system and provide recommendations to guide a future development path from the perspective of standards compliance. We conclude that few DLTs possess the maturity to provide proper requirements coverage due to the emergent nature of the DLT space. Additionally, this paper presents the high-level requirements to achieve a minimally viable solution that can demonstrate such digital credential verification in the academic and skills tracking context.
Accelerating the use of mobile phone capabilities to maximise the effectiveness of public emergency alerts in South Africa
(2024-10) Mukange, Tsumbedzo; Mokoto, Bayanda T; Moipolai, Tumelo B; Ndamase, Zimasa
An emergency alert system (EAS) enables government authorities, its agencies, and community authorities at various levels to use communication platforms to inform people in threatened areas of imminent disaster. Disseminating emergency alerts to the public is crucial to ensure effective and efficient disaster management. The purpose of disseminating emergency alerts is to provide important and life-saving information to the public so they can take the necessary actions to ensure their safety. EAS uses various communication channels to disseminate alerts and warnings, including TVand radio, sirens and long-range acoustic devices, message signage and public address systems, the Internet, fixed phones, and mobile phones through cell broadcast services (CBS), SMS and mobile apps. The United Nations launched the Early Warning for All initiative that promotes the use of geo-located mobile-based early warning services, such as CBS and location-based SMS (LB SMS), to disseminate emergency alerts to all by 2027. The accessibility of mobile phones has accelerated the use of mobile phone capabilities to disseminate emergency alerts. In South Africa, using CBS and LB SMS capabilities to disseminate emergency information to targeted geographical areas by authorities is still an area of improvement. T hestudy aims to accelerate the adoption and use of cell broadcast and location-based SMS to maximize the effectiveness of public emergency alerts in South Africa. The study forms a basis to accelerate the adoption and use of CBSandLBSMStodisseminatepublic emergency alerts. Partial results show emergency alert message crafting and appropriate communication approaches are vital in influencing the public to comply with the alert. In addition, South Africa had implemented some components of emergency alert systems, but in isolation and focusing on specific types of emergency alerts.
Acceleration of hidden Markov model fitting using graphical processing units, with application to low-frequency tremor classification
(2021-11) Stoltz, M; Stoltz, George G; Obara, K; Wang, T; Bryant, D
Hidden Markov models (HMMs) are general purpose models for time-series data widely used across the sciences because of their flexibility and elegance. Fitting HMMs can often be computationally demanding and time consuming, particularly when the number of hidden states is large or the Markov chain itself is long. Here we introduce a new Graphical Processing Unit (GPU)-based algorithm designed to fit long-chain HMMs, applying our approach to a model for low-frequency tremor events. Even on a modest GPU, our implementation resulted in an increase in speed of several orders of magnitude compared to the standard single processor algorithm. This permitted a full Bayesian inference of uncertainty related to model parameters and forecasts based on posterior predictive distributions. Similar improvements would be expected for HMM models given large number of observations and moderate state spaces ( states with current hardware). We discuss the model, general GPU architecture and algorithms and report performance of the method on a tremor dataset from the Shikoku region, Japan. The new approach led to improvements in both computational performance and forecast accuracy, compared to existing frequentist methodology.
Age invariant face recognition methods: A review
(2021-12) Baruni, Kedimotse P; Mokoena, Nthabiseng ME; Veeraragoo, Mahalingam; Holder, Ross P
Face recognition is one of the biometric technologies that is mostly used in surveillance and law enforcement for identification and verification. However, face recognition remains a challenge in verifying and identifying individuals due to significant facial appearance discrepancies caused by age progression. Especially in applications that verify individuals from their passports, driving licenses and finding missing children after decades. The most critical step in Age- Invariant Face Recognition (AIFR) is extracting rich discriminative age-invariant features for each individual in face recognition applications. The variation of facial appearance across aging can be solved using three methods, namely, generative (aging simulation), discriminative (feature-based) and deep neural networks methods. This work reviews and compares the state-of-art AIFR methods to address the work that has been done to minimize the effect of aging in face recognition application during the pre-processing and feature extraction stages to extract rich discriminative age-invariant features from facial images of individuals (subjects) captured at different ages, shortfalls and advantages of these methods. The novelty of this work lies in analyzing the state-of-art work that has been done during the pre-processing and/or feature extraction stages to minimize the difference between the query and enrolled face images captured over age progression.
Algebraic analysis of Toeplitz decorrelation techniques for direction-of-arrival estimation
(2019-11) Shafuda, F; McDonald, Andre M; Van Wyk, MA; Versfeld, J
In this paper, we investigate the correlation Toeplitz (CTOP) and averaging Toeplitz (AVTOP) decorrelation techniques, as applied towards direction of arrival (DOA) estimation of coherent narrowband sources with the multiple signals classi cation (MUSIC) algorithm. Numerical studies suggest that CTOP leads towards more accurate DOA estimation than AVTOP; however, no theoretical motivation for this performance gap has yet been presented. In this paper, we derive expressions for the Toeplitz matrices produced by the CTOP and AVTOP techniques, for a scenario involving a three-element uniform linear array and two coherent source signals in additive white Gaussian noise. These expressions lead to the claim that the accuracy of the CTOP technique can be attributed to its retention of source DOA information as independent sums (i.e. in a superposition form) in the Toeplitz matrix. The claim is supported by an investigation of the MUSIC spectra corresponding to the distinct Toeplitz matrices.
An adaptive digital forensic framework for the evolving digital landscape in industry 4.0 and 5.0
(2024-01) Nelufule, Nthatheni N; Singano, Zothile; Masemola, Kelebogile B; Shadung, Lesiba D; Nkwe, Boitumelo C; Mokoena, Chantel J
Digital forensics is one of the most challenging disciplines in the field of cybercriminals. This article examines the evolving landscape of digital forensic investigations, identifies the unique challenges posed by emerging technologies such as Industry 4.0, and outlines a comprehensive approach not only to confront these challenges, but also to pave the way for a seamless transition to Industry 5.0. The proposed framework focuses on the development of an adaptive digital investigation framework customized for the evolving digital landscape in emerging technology environments. The framework combines dynamic evidence collection techniques, advanced analytics technologies, and multi-stakeholder collaborative engagement to ensure the fidelity and admissibility of the collected digital evidence. The analysis of the proposed framework has been discussed in detail using real-life case studies to ensure that the framework can be implemented and deployed in real-life scenarios.
An Adaptive Digital Forensic Framework for the Evolving Digital Landscape in Industry 4.0 and 5.0
(2024-01) Nelufule, NthaNthatheni Ntheni N; Singano, Zothile; Masemola, Kelebogile B; Shadung, Lesiba D; Nkwe, Boitumelo C; Mokoena, Chantel J
Digital forensics is one of the most challenging disciplines in the field of cybercriminals. This article examines the evolving landscape of digital forensic investigations, identifies the unique challenges posed by emerging technologies such as Industry 4.0, and outlines a comprehensive approach not only to confront these challenges, but also to pave the way for a seamless transition to Industry 5.0. The proposed framework focuses on the development of an adaptive digital investigation framework customized for the evolving digital landscape in emerging technology environments. The framework combines dynamic evidence collection techniques, advanced analytics technologies, and multi-stakeholder collaborative engagement to ensure the fidelity and admissibility of the collected digital evidence. The analysis of the proposed framework has been discussed in detail using real-life case studies to ensure that the framework can be implemented and deployed in real-life scenarios.

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Browsing Conference Publications by browse.metadata.cluster "Defence and Security"

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