Investigating the Effectiveness of Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data.

As criminal activity increasingly relies on digital devices, the field of digital forensics plays a vital role in identifying and investigating criminals. In this paper, we addressed the problem of anomaly detection in digital forensics data. Our objective was to propose an effective approach for identifying suspicious patterns and activities that could indicate criminal behavior. To achieve this, we introduce a novel method called the Novel Support Vector Neural Network (NSVNN). We evaluated the performance of the NSVNN by conducting experiments on a real-world dataset of digital forensics data. The dataset consisted of various features related to network activity, system logs, and file metadata. Through our experiments, we compared the NSVNN with several existing anomaly detection algorithms, including Support Vector Machines (SVM) and neural networks. We measured and analyzed the performance of each algorithm in terms of the accuracy, precision, recall, and F1-score. Furthermore, we provide insights into the specific features that contribute significantly to the detection of anomalies. Our results demonstrated that the NSVNN method outperformed the existing algorithms in terms of anomaly detection accuracy. We also highlight the interpretability of the NSVNN model by analyzing the feature importance and providing insights into the decision-making process. Overall, our research contributes to the field of digital forensics by proposing a novel approach, the NSVNN, for anomaly detection. We emphasize the importance of both performance evaluation and model interpretability in this context, providing practical insights for identifying criminal behavior in digital forensics investigations.

Clinical and demographic characteristics of COVID-19 cases in Brunei Darussalam: comparison between the first and second waves, 2020 and 2021.

Objective: Differences in clinical manifestations between strains of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been reported. This retrospective descriptive study compares the clinical and demographic characteristics of all confirmed coronavirus disease (COVID-19) cases admitted to the National Isolation Centre (NIC) in the first wave and at the beginning of the second wave of the pandemic in Brunei Darussalam. Methods: All COVID-19 cases admitted to the NIC between 9 March and 6 May 2020 (first wave) and 7-17 August 2021 (second wave) were included. Data were obtained from NIC databases and case characteristics compared using Student's t-tests and χ2 tests, as appropriate. Results: Cases from the first wave were significantly older than those from the second wave (mean 37.2 vs 29.7 years, P < 0.001), and a higher proportion reported comorbidities (30.5% vs 20.3%, P = 0.019). Cases from the second wave were more likely to be symptomatic at admission (77.7% vs 63.1%, P < 0.001), with a higher proportion reporting cough, anosmia, sore throat and ageusia/dysgeusia; however, myalgia and nausea/vomiting were more common among symptomatic first wave cases (all P < 0.05). There was no difference in the mean number of reported symptoms (2.6 vs 2.4, P = 0.890). Discussion: Our study showed clear differences in the profile of COVID-19 cases in Brunei Darussalam between the first and second waves, reflecting a shift in the predominating SARS-CoV-2 strain. Awareness of changes in COVID-19 disease manifestation can help guide adjustments to management policies such as duration of isolation, testing strategies, and criteria for admission and treatment.