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.
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.
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 analysis of a cryptocurrency giveaway scam: Use case
(2024-06) Botha, Johannes G; Leenen, L
A giveaway scam is a type of fraud leveraging social media platforms and phishing campaigns. These scams have become increasingly common and are now also prevalent in the crypto community where attackers attempt to gain crypto-enthusiasts’ trust with the promise of high-yield giveaways. Giveaway scams target individuals who lack technical familiarity with the blockchain. They take on various forms, often presenting as genuine cryptocurrency giveaways endorsed by prominent figures or organizations within the blockchain community. Scammers entice victims by promising substantial returns on a nominal investment. Victims are manipulated into sending cryptocurrency under the pretext of paying for "verification" or "processing fees." However, once the funds have been sent, the scammers disappear and leave victims empty-handed. This study employs essential blockchain tools and techniques to explore the mechanics of giveaway scams. A crucial aspect of an investigation is to meticulously trace the movement of funds within the blockchain so that illicit gains resulting from these scams can be tracked. At some point a scammer wants to “cash-out” by transferring the funds to an off-ramp, for example, an exchange. If the investigator can establish a link to such an exchange, the identity of the owner of cryptocurrency address could be revealed. However, in organised scams, criminals make use of mules and do not use their own identities. The authors of this paper select a use case and then illustrate a comprehensive approach to investigate the selected scam. This paper contributes to the understanding and mitigation of giveaway scams in the cryptocurrency realm. By leveraging the mechanics of blockchain technology, dissecting scammer tactics, and utilizing investigative techniques and tools, the paper aims to contribute to the protection of investors, the industry, and the overall integrity of the blockchain ecosystem. This research sheds light on the intricate workings of giveaway scams and proposes effective strategies to counteract them.
An evaluation of DL T governance models
(2024-08) Ntshangase, Cynthia S; Ndhlovu, Nomalisa; Myaka, Zanele S; Mahlasela, Oyena N; Siphambili, Nokuthaba; Mthethwa, Sthembile
Distributed Ledger Technology (DL T) is a decentralised database architecture that allows multiple participants to have simultaneous access to a constantly updated digital ledger or record of information. This study presents a systematic literature review using the PRISMA framework to look at the DL T governance models. Six DL T governance models were identified: network, decentralized autonomous organisations, organisational, corporate, managerial, and operational. These models were then assessed based on how each is influenced by the four DLT governance dimensions, economical, political, technological, and social. Seven components of DL T governance were also considered during the evaluation such as stakeholders, participation, accountability, transparency, flexibility, enforcement, and decision-making. The results show that each governance model has a different level of influence from each dimension and a different level of consideration from key DL T governance components. The selection of which model to use depends on the requirements of each organisation and the users of the DL T system. Promoted results can assist organisations and researchers in selecting the best model that fits their requirements and prioritisation of dimensions and each component.
An analysis of a cryptocurrency giveaway scam: Use case
(2024-06) Botha, Johannes G; Leenen, L
A giveaway scam is a type of fraud leveraging social media platforms and phishing campaigns. These scams have become increasingly common and are now also prevalent in the crypto community where attackers attempt to gain crypto-enthusiasts’ trust with the promise of high-yield giveaways. Giveaway scams target individuals who lack technical familiarity with the blockchain. They take on various forms, often presenting as genuine cryptocurrency giveaways endorsed by prominent figures or organizations within the blockchain community. Scammers entice victims by promising substantial returns on a nominal investment. Victims are manipulated into sending cryptocurrency under the pretext of paying for "verification" or "processing fees." However, once the funds have been sent, the scammers disappear and leave victims empty-handed. This study employs essential blockchain tools and techniques to explore the mechanics of giveaway scams. A crucial aspect of an investigation is to meticulously trace the movement of funds within the blockchain so that illicit gains resulting from these scams can be tracked. At some point a scammer wants to “cash-out” by transferring the funds to an off-ramp, for example, an exchange. If the investigator can establish a link to such an exchange, the identity of the owner of cryptocurrency address could be revealed. However, in organised scams, criminals make use of mules and do not use their own identities. The authors of this paper select a use case and then illustrate a comprehensive approach to investigate the selected scam. This paper contributes to the understanding and mitigation of giveaway scams in the cryptocurrency realm. By leveraging the mechanics of blockchain technology, dissecting scammer tactics, and utilizing investigative techniques and tools, the paper aims to contribute to the protection of investors, the industry, and the overall integrity of the blockchain ecosystem. This research sheds light on the intricate workings of giveaway scams and proposes effective strategies to counteract them.
An analysis of crypto scams during the Covid-19 pandemic: 2020-2022
(2023-03) Botha, Johannes G; Botha-Badenhorst, Danielle P; Leenen, L
Blockchain and cryptocurrency adoption has increased significantly since the start of the Covid-19 pandemic. This adoption rate has overtaken the Internet adoption rate in the 90s and early 2000s, but as a result, the instances of crypto scams have also increased. The types of crypto scams reported are typically giveaway scams, rug pulls, phishing scams, impersonation scams, Ponzi schemes as well as pump and dumps. The US Federal Trade Commission (FTC) reported that in May 2021 the number of crypto scams were twelve times higher than in 2020, and the total loss increased by almost 1000%. The FTC also reported that Americans have lost more than $80 million due to cryptocurrency investment scams from October 2019 to October 2020, with victims between the ages of 20 and 39 represented 44% of the reported cases. Social Media has become the go to place for scammers where attackers hack pre-existing profiles and ask targets’ contacts for payments in cryptocurrency. In 2020, both Joe Biden and Bill Gates’ Twitter accounts were hacked where the hacker posted tweets promising that for all payments sent to a specified address, double the amount will be returned, and this case of fraud was responsible for $100,000 in losses. A similar scheme using Elon Musk’s Twitter account resulted in losses of nearly $2 million. This paper analyses the most significant blockchain and cryptocurrency scams since the start of the Covid-19 pandemic, with the aim of raising awareness and contributing to protection against attacks. Even though the blockchain is a revolutionary technology with numerous benefits, it also poses an international crisis that cannot be ignored.
An analysis of the MTI crypto investment scam
(2023-06) Botha, Johannes G; Pederson, T; Leenen, L
Since the start of the Covid-19 pandemic, blockchain and cryptocurrency adoption has increased significantly. The adoption rate of blockchain-based technologies has surpassed the Internet adoption rate in the 90s and early 2000s. As this industry has grown significantly, so too has the instances of crypto scams. Numerous cryptocurrency scams exist to exploit users. The generally limited understanding of how cryptocurrencies operate has increased the possible number of scams, relying on people's misplaced sense of trust and desire for making money quickly and easily. As such, investment scams have also been growing in popularity. Mirror Trading International (MTI) has been named South Africa's biggest crypto scam in 2020, resulting in losses of $1.7 billion. It is also one of the largest reported international crypto investment scams. This paper focuses on a specific aspect of the MTI scam; an analysis on the fund movements on the blockchain from the perpetrators and members who benefited the most from the scam. The authors used various Open-Source Intelligence (OSINT) tools, alongside QLUE, as well as news articles and blockchain explorers. These tools and techniques are used to follow the moneytrial on the blockchain, in search of possible mistakes made by the perpetrator. This could include instances where some personal information might have been leaked. With such disclosed personal information, OSINT tools and investigative techniques can be used to identify the criminals. Due to the CEO of MTI having been arrested, and the case currently being dealt with in the court of law in South Africa, this paper also presents investigative processes that could be followed. Thus, the focus of this paper is to follow the money and consequently propose a process for an investigator to investigate crypto crimes and scams on the blockchain. As the adoption of blockchain technologies continues to increase at unprecedented rates, it is imperative to produce investigative toolkits and use cases to help reduce time spent trying to catch bad actors within the generally anonymous realm of cryptocurrencies.
Application of geospatial data in cyber security
(2022-06) Veerasamy, Namosha; Yoolla, Yaseen; Dawood, Zubeida C
Geospatial data is often perceived as only being related to maps, compasses and locations. However, the application areas of geospatial data are far wider and even extend to the field of cybersecurity. Not only is there an ability to show points of interestand emerging network traffic conditions, geospatial data also has the ability to model cyber crime growth patterns and indicate affected areas as well as the emergence of certain type of cyber threats. Geospatial data can feed into intelligence systems, help with analysis, information sharing, and help create situational awareness. This is particularly useful in the area of cyber security. Geospatial data is very powerful and can help to prioritise cyber threats and identify critical areas of concern. Previously, geospatial data was primarily used by militaries, intelligence agencies, weather services or traffic control. Currently, the application of geospatial data has multiplied, and it spans many more industries and sectors. So too for cyber security, geospatial data has a wide number of uses. It may be difficult to find patterns or trends in large data sets. However, the graphic capabilities of geo mapping help present data in more digestible manner. This may help analysts identify emerging issues, threats and target areas. In this paper, the usefulness of geospatial data for cyber security is explored. The paper will cover a framework of the key application areas that geospatial data can serve in the field of cyber security. The ten application areas covered in the paper are: tracking, data analysis, visualisation, situational awareness, cyber intelligence, collaboration, improved response to cyber threats, decision-making, cyber threat prioritisation and protect cyber infrastructure It is aimed that through the paper, the application areas of geospatial data can be more widely adopted.
Artificial Intelligence impact on the realism and prevalence of deepfakes
(2024-07) Mahlasela, Oyena N; Baloyi, Errol; Baloyi, Errol; Dawood, Zubeida C
Deepfakes, synthetic media manipulated by Artificial Intelligence (AI), have become a growing concern in the information landscape. This paper explored the impact of AI on the realism and prevalence of deepfakes. Therefore, this study examined how AI advancements in machine learning and generative models have facilitated the creation of increasingly convincing deepfakes. The analysis looked at the rise of hyper-realistic deception and the societal impact of deepfakes. In addition to recognizing the challenges, a framework was developed for the detection of deepfakes. Finally, this study discussed the potential mitigation strategies, such as the development of deepfake detection tools and fostering media literacy.
Aspects of Wind Tunnel Testing: Practices
(2024) Morelli, Mauro F
This presentation focuses on various aspects of wind tunnels - their testing, types, processes, balances and model design and procurement
BFO Classifier: Aligning domain ontologies to BFO
(2022-08) Emeruem, C; Keet, CM; Dawood, Zubeida C; Wang, S
Foundational ontologies are known to have a steep learning curve, which hampers casual use by domain ontology developers to use them for domain ontology development. Foundational ontology developers have not provided methods or tools to lower the barriers of uptake beyond offering, at best, a computational version. We investigate an approach to bridge this gap through the development of a decision diagram for BFO, which offers the modeller a series of questions with closed answer options in order to step-wise arrive at a suitable entity to align the domain entity to. This diagram was implemented in a tool, the BFO Classifier, that keeps track of the question and answer trace and with the click of a button the alignment axiom can be added to the ontology. It was evaluated with two BFO-aligned ontologies, which showed that in at least half.
Biases and debiasing of decisions in ageing military systems
(2019-09) Pelser, Winnie C
Many of the administrative decisions that must be made in a military environment are complex and rely on a rational analysis of situations. Decisions within the domain of ageing systems are particularly difficult and often riddled with different biases. This paper investigates why rational thinking is not always the norm, and suggests possible ways to assist decision making. A few biases are identified, and available debiasing techniques are discussed. It was found that research in this field is limited and must be expanded in order to ensure optimal decision.
A bibliometric approach to support redefining management of technology for the post-digital world
(2021-09) Oosthuizen, Rudolph
Management of Technology (MoT) has evolved since its inception in the 1980s and definitions from the 1990s. However, the field's definition may not be keeping up with the ever-increasing changes in our world. This paper implements bibliometrics, through natural language processing and topic modelling, of published literature on MoT to trace the evolution of research focus areas. The processed literature consists of an extensive sample from a keyword search of publication databases. Analysing the topic priorities over time indicates how research in the field evolved. Comparing these focus areas to the different definitions provide inputs for improving the definition of MoT. The topics extracted in this paper over the history of MoT offers a base from where to initiate such an investigation.
Biometric recognition of infants using fingerprints: Can the infant fingerprint be used for secure authentication?
(2023-03) Nelufule, Nthatheni N; Moolla, Yasneen; Ntshangase, Cynthia S; De Kock, Antoine J
One of the first recognised and commonly used biometric modalities for men is the fingerprint, which is frequently used to register adults at home and in traffic centres. Fingerprint biometrics for babies, in particular, are not commonly used or approved. The infant recognition system discussed in this article is tested in infants as early as six weeks of age using a prototype infant fingerprint capture device. To compare and contrast the identification performance of the prototype fingerprint scanner with the traditional fingerprint scanner, the same error rates, standard deviations, and Failure to Acquire were calculated. The results of this investigation point to the possibility of registering newborns as early as six weeks using a baby’s fingerprint.
Blockchain technology adoption in outbound logistics for the fourth industrial revolution
(2022-08) Steynberg, L; Erasmus, Louwrence D; Pretorius, L
There are opportunities for disruptive applications of blockchain technology in outbound logistics management, especially with peer-to-peer networking support to keep records of verified transactions in distributed ledgers without control by an intermediate party. The impact of implementing a blockchain solution in logistics processes and the effect on process parameters is not well understood or quantified in the literature. The objective of this study is to investigate whether there are opportunities in outbound logistics operations to benefit from the application of blockchain technology and to evaluate the impact of implementing a blockchain technology solution in a specific industry use case. A design science research process, which combines qualitative and quantitative research methods, guided this study. A stochastic discrete event simulation model was developed to evaluate the impact of a blockchain solution for an industry use case in an outbound logistics process. The time to reach visibility is quantified for a Hyperledger Fabric implementation. The results indicated that it would have a significant effect on the time it takes to gain insight into the process and transparency. This study provides evidence that a blockchain solution can have a notable impact on information availability and transparency in an outbound logistics process.
Building an integrated cyber defence capability for African missions
(2022) Mtsweni, Jabu S; Thaba, James M
Cyberspace has been designated by organizations such as NATO as the fifth domain for battlespace, and many nations are already having and/or building their capabilities in the cyber defence environment in order to protect and defend their assets against any onslaught by their adversaries. It is a common belief that many African countries are not well positioned or prepared to respond effectively to cyberattacks against their citizens, critical infrastructure, and government. In many instances, the gap can be traced to the shortage of skills, lack of cybersecurity readiness and preparedness, and lack of investment in cybersecurity programmes, including policies within the military’s strategic, tactical, and operational environments. This paper seeks to present a conceptual approach into how African countries could develop a resilient cyber defence capability in order to effectively respond to constant cyberattacks. The approach is underpinned by an integrated capability management philosophy using case studies in large and complex environments, including strategic and capability development learnings from other military domains outside the African continent. It is envisaged that the output of this paper may influence and support African states in building their cyber defence capabilities in a coordinated and integrated manner.

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