AJIC Issue 25, 2020
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- ItemAJIC Issue 25, 2020 - Full Issue(LINK Centre, University of the Witwatersrand (Wits), Johannesburg, 2020-06-30)Articles on digital terrorism, SMS fraud, machine learning, mHealth, natural language processing, and international telecommunications law and policy.
- ItemAJIC Issue 25, 2020 - Full Issue - print-on-demand version(LINK Centre, University of the Witwatersrand (Wits), Johannesburg, 2020-06-30)Articles on digital terrorism, SMS fraud, machine learning, mHealth, natural language processing, and international telecommunications law and policy.
- ItemThe Digitalised Terrorism Ecology: A Systems Perspective(2020-06-30) Ochara, Nixon Muganda; Odhiambo, Nancy Achieng; Kadyamatimba, ArmstrongThis study uses a systematic review methodology to interpret existing literature on the digital dimensions of contemporary terrorism and counter-terrorism. Using the theory of synergetics as a guiding analytical framework, the study conducts meta-synthesis of relevant literature, including application of soft systems methodology (SSM), in order to generate conceptualisation of a digitalised terrorism ecology. This ecology comprises five interacting sub-systems: open digital infrastructure; digital information ecology; digital terrorism enactment; digital capabilities; and digital enslavement.
- ItemApplication of Machine Learning Classification to Detect Fraudulent E‑wallet Deposit Notification SMSes(LINK Centre, University of the Witwatersrand (Wits), Johannesburg, 2020-06-30) Enkono, Fillemon S.; Suresh, NalinaFraudulent e-wallet deposit notification SMSes designed to steal money and goods from m-banking users have become pervasive in Namibia. Motivated by an observed lack of mobile applications to protect users from such deceptions, this study evaluated the ability of machine learning to detect the fraudulent e-wallet deposit notification SMSes. The naïve Bayes (NB) and support vector machine (SVM) classifiers were trained to classify both ham (desired) SMSes and scam (fraudulent) e-wallet deposit notification SMSes. The performances of the two classifier models were then evaluated. The results revealed that the SVM classifier model could detect the fraudulent SMSes more efficiently than the NB classifier.
- ItemA Supplementary Tool for Web-archiving Using Blockchain Technology(LINK Centre, University of the Witwatersrand (Wits), Johannesburg, 2020-06-30) De Villiers, John E.; Calitz, André P.The usefulness of a uniform resource locator (URL) on the World Wide Web is reliant on the resource being hosted at the same URL in perpetuity. When URLs are altered or removed, this results in the resource, such as an image or document, being inaccessible. While web-archiving projects seek to prevent such a loss of online resources, providing complete backups of the web remains a formidable challenge. This article outlines the initial development and testing of a decentralised application (DApp), provisionally named Repudiation Chain, as a potential tool to help address these challenges presented by shifting URLs and uncertain web-archiving. Repudiation Chain seeks to make use of a blockchain smart contract mechanism in order to allow individual users to contribute to web-archiving. Repudiation Chain aims to offer unalterable assurance that a specific file and its URL existed at a given point in time—by generating a compact, non-reversible representation of the file at the time of its non-repudiation. If widely adopted, such a tool could contribute to decentralisation and democratisation of web-archiving