Global cyber trends : a South African reality

Cyber trends are a reality across the globe. Not only are technology and electronic devices and media used more regularly in easing everyday activities, but these technological advances are also used in sophisticated criminal activities. Regardless of the global innovation and development ranking of a country, all countries tend to show the same global trends, either on a more or lesser scale. Some of the more prominent global trends are identified and discussed. This paper aims to show that these global cyber trends are also a reality in South Africa, by addressing some of the most prominent global trends. Based on the statistics available to support the presence of high cyber trends, as present in high ranked countries across the globe, this article shows that technology within South Africa has advanced to such an extent that the country is not unduly hurdled by large scale lack of connectivity and bandwidth, computer illiteracy, low Internet penetration and inadequate cyber crime related legislation. The discussion within this paper places South Africa on par with first world countries in terms of cyber trends

Simplifying Cyber Security Maturity Models through National Culture: A Fuzzy Logic Approach

Different assessment models exist to measure a country's cyber security maturity levels. These levels serve as a benchmark for indicating how well prepared a nation is against a cyber security attack and how resilient it would be in recovering from such an attack. However, results from these maturity assessments are either too general, overly complex, or resource intensive to apply and guide important national cyber security strategies and frameworks. To address this we propose a model to link national culture with a country's cyber security maturity through fuzzy logic mapping to ensure that a more uniform reflection of the cyber security maturity level within a country can be measured. In this paper, we present additional research towards optimising our model. The extended model incorporates input from two cyber security assessment models, and validates the refined output models on 11 countries to compare the maturity levels from the traditional assessment model with our optimised fuzzy model. Our results show that it is viable to reduce the resources required to conduct a national cyber security maturity assessment

Smart Contract-based Consensus Building for Collaborative Medical Decision-Making

Medical decision-making is moving away from the traditional one-off dyadic encounter between the patient and physician, and transitioning towards a more inclusive, shared decision-making process that also considers the inputs from other stakeholders. This ensures that a patient's decision is not only based on a medical opinion, but also includes other considerations such as impact on family members, legal and financial implications, and experiences of patients in similar situations. However, given the sensitive nature of health data and decisions, there are several challenges associated with safeguarding the privacy, security and consent of all contributors and assuring the integrity of the process. We propose a collaborative medical decision-making platform that uses a consensus building mechanism implemented using Blockchain-based Smart Contracts to address some of the above challenges, thereby giving the participants confidence that both the decision-making process and the outcome(s) can be trusted. We also present a proof-of-concept implementation using the private Ethereum Blockchain to demonstrate practicability

Conceptualizing human resilience in the face of the global epidemiology of cyber attacks

Computer security is a complex global phenomenon where different populations interact, and the infection of one person creates risk for another. Given the dynamics and scope of cyber campaigns, studies of local resilience without reference to global populations are inadequate. In this paper we describe a set of minimal requirements for implementing a global epidemiological infrastructure to understand and respond to large-scale computer security outbreaks. We enumerate the relevant dimensions, the applicable measurement tools, and define a systematic approach to evaluate cyber security resilience. From the experience in conceptualizing and designing a cross-national coordinated phishing resilience evaluation we describe the cultural, logistic, and regulatory challenges to this proposed public health approach to global computer assault resilience. We conclude that mechanisms for systematic evaluations of global attacks and the resilience against those attacks exist. Coordinated global science is needed to address organised global ecrime

Local Differential Privacy for Federated Learning

Advanced adversarial attacks such as membership inference and model memorization can make federated learning (FL) vulnerable and potentially leak sensitive private data. Local differentially private (LDP) approaches are gaining more popularity due to stronger privacy notions and native support for data distribution compared to other differentially private (DP) solutions. However, DP approaches assume that the FL server (that aggregates the models) is honest (run the FL protocol honestly) or semi-honest (run the FL protocol honestly while also trying to learn as much information as possible). These assumptions make such approaches unrealistic and unreliable for real-world settings. Besides, in real-world industrial environments (e.g., healthcare), the distributed entities (e.g., hospitals) are already composed of locally running machine learning models (this setting is also referred to as the cross-silo setting). Existing approaches do not provide a scalable mechanism for privacy-preserving FL to be utilized under such settings, potentially with untrusted parties. This paper proposes a new local differentially private FL (named LDPFL) protocol for industrial settings. LDPFL can run in industrial settings with untrusted entities while enforcing stronger privacy guarantees than existing approaches. LDPFL shows high FL model performance (up to 98%) under small privacy budgets (e.g., epsilon = 0.5) in comparison to existing methods.Comment: 17 page