Most recent issue published online in the International Journal of Critical Computer-Based Systems.

1. Algorithm-based fault-tolerant parallel sorting
   High performance computing (HPC) systems often require substantial resources, and can take up to several hours or days to execute. Upon a failure, it is important to loose as little computation as possible. In this work we present an algorithm-based fault tolerance (ABFT) strategy for hypercube-based parallel algorithms. The strategy assumes the virtual VCube topology, which has several logarithmic properties that are preserved even as nodes fail. The strategy guarantees that the algorithm does not halt even after up to (<i>N</i> - 1) nodes crash, in a system of <i>N</i> nodes. We use parallel sorting as a case study, describing how to make a fault-tolerant version of three parallel sorting algorithms: HyperQuickSort, QuickMerge and Bitonic Sort. The algorithms were implemented in MPI using ULMF to handle faults. Experimental results are presented showing the performance and robustness of the solution for sorting up to a billion integers in scenarios with faults.
2. Using the theorem of inaccessibility to assess dependable real-time networks
   This paper builds on the foundations of computer-based networks in order to assess their dependable and real-time properties. We extend the analysis and modelling of network inaccessibility by using its associated theorem to gauge the origins of dependability issues during the network operation. We show how network transmissions in the presence of errors can be observed and analysed, implying a richer view and understanding of their negative impact to the whole distributed real-time ecosystem. We present results extracted from a simulated scenario of an industrial wireless sensors and actuators network (IWSAN) implemented in C Language, enabling us to conclude that more resilient computer-based networks are needed in such environments, as well as improved modelling and prototyping of distributed real-time systems running on top of them.
3. Application of multi-criteria decision-making approach using TOPSIS to identify the vulnerable time zone of earthquake time series signal
   Conventional analysis of time series signals representing earthquakes does not provide any clue about the vulnerability of such disastrous events. Time series signals contain P and S waves, which can detect earthquake epicentres. Due to the failure of the old method for determining earthquake susceptibility over time, decision-making is needed. This research suggests a multi-criteria decision-making method to determine earthquake signal risk time zones. This study used TOPSIS for this job. TOPSIS ranks greatest and worst resemblance to positive and negative ideal solutions. Alternatives and criteria constitute the decision matrix. Segmenting the earthquake's duration creates alternate time zones, and seismic signal dynamics are used to set criteria. Statistical mean and standard deviation are two criteria among many. Other criteria include Hurst exponent, power spectrum maximum amplitude, and segmented signal anomaly (assumed as alternate). The proposed approach was tested using Indian Meteorological Department Bhuj earthquake data. The paper describes how to evaluate criteria for a time zone alternative. To simplify computation, earthquake incidence is separated into 14 equal-length time segments. Results demonstrate that the proposed method accurately detects earthquake time series signal sensitive time zones.
4. Conceptual design and implementation of FIDO2 compatible smart medical card for healthcare information system
   This paper addresses the escalating risk of electronic health records (EHRs) breaches and unauthorised profiling, emphasising the need for standardised solutions to safeguard patient information. Focusing on privacy and security, the proposed conceptual design introduces Fast IDentity Online Specifications (FIDO2) compatible smart medical cards for healthcare information systems. By leveraging FIDO2, the solution ensures password-less authentication through device attestation, enhancing security in accessing patient information. The cloud computing model adds multiple layers of security, maintaining data confidentiality. Experimental results demonstrate performance comparable to traditional healthcare information systems, with a notable advantage in resource-limited settings. The implementation extends the reach of EHR systems, particularly beneficial in low- and middle-income countries with developing health data exchange infrastructure. The use of FIDO2-based smart cards presents a secure and scalable alternative, addressing critical challenges in EHR privacy and security effectively.
5. Refining malware detection with enhanced machine learning algorithms using hyperparameter tuning
   The aim of this research is to investigate and demonstrate the advantages and limitations of various machine learning techniques for malware classification, specifically focusing on portable executable (PE) files. The study addresses common challenges in machine learning, such as overfitting and underfitting, by employing ensemble methods and pre-processing techniques, including feature selection and hyperparameter tuning. The primary objective is to enhance classifier performance in distinguishing between malicious and benign PE files. Through a comparative analysis of machine learning methodologies such as random forests, decision trees, and gradient boosting, the study highlights the superiority of the random forests algorithm, achieving an impressive accuracy rate of 99%. By thoroughly evaluating the strengths and limitations of each algorithm, the research provides valuable insights into effectively handling diverse malware categories. This paper underscores the significance of ensemble methods, feature engineering, and pre-processing in improving classifier performance for malware classification, specifically in the context of portable executable files.