There has been a great deal of research in the area of using graph engines and graph databases to model network traffic and network attacks, but the novelty of this research lies in visually or graphically representing the Reconnaissance Tactic (TA0043) of the MITRE ATT&CK framework. Using the newly created dataset, UWF-Zeekdata22, based on the MITRE ATT&CK framework, patterns involving network connectivity, connection duration, and data volume were found and loaded into a graph environment. Patterns were also found in the graphed data that match the Reconnaissance as well as other tactics captured by UWF-Zeekdata22. The Star motif was particularly useful in mapping the Reconnaissance tactic. The results of this paper show that graph databases/graph engines can be essential tools for understanding network traffic and trying to detect network intrusions before they happen. Finally, an analysis of the run-time performance of the reduced dataset used to create the graph databases showed that the reduced datasets performed better than the full dataset.

Tactics, Techniques, and Procedures (TTPs) constitute the most valuable aspect of Cyber Threat Intelligence (CTI). However, TTPs are often implicit in unstructured text, necessitating manual analysis by field experts. Automating the classification of TTPs from unstructured text is a crucial task in contemporary research. MITRE ATT&CK serves as the de facto standard for studying TTPs. Existing research constructs classification datasets based on its procedural examples for tactics and techniques. However, due to a significant proportion of small sample categories, a long-tail phenomenon exists, leading to a highly imbalanced sample distribution. Consequently, more research concentrates on categories with relatively abundant samples. This paper proposes a method that combines ChatGPT data augmentation with Instruction Supervised Fine-Tuning of open large language models. This approach offers a solution for TTPs classification in few-shot learning scenarios, achieving coverage of 625 technical categories. The Precision, Recall, and F1 scores reach 86.2%, 89.9%, and 87.3%, respectively.

In various domains such as energy, manufacturing, and maritime, cyber-physical systems (CPS) have seen increased interest. Both academia and industry have focused on the cybersecurity aspects of such systems. The assessment of cyber risks in CPS is a popular research area with many existing approaches that aim to suggest relevant methods and practices. However, few works have addressed the extensive and objective evaluation of the proposed approaches. In this paper, a standard-aligned evaluation methodology is presented and empirically conducted to evaluate a newly proposed cyber risk assessment approach for CPS. The approach, which is called FMECA-ATT&CK is based on Failure Mode, Effects & Criticality Analysis (FMECA) risk assessment process and enriched with the semantics and encoded knowledge in the Adversarial Tactics, Techniques, and Common Knowledge framework (ATT&CK). Several experts were involved in conducting two risk assessment processes, FMECA-ATT\&CK and Bow-Tie, against two use cases in different application domains, particularly an autonomous passenger ship (APS) as a maritime use case and a digital substation as an energy use case. This allows for the evaluation of the approach based on a group of characteristics, namely, applicability, feasibility, accuracy, comprehensiveness, adaptability, scalability, and usability. The results highlight the positive utility of FMECA-ATT&CK in model-based, design-level, and component-level cyber risk assessment of CPS with several identified directions for improvements. Moreover, the standard-aligned evaluation method and the evaluation characteristics have been demonstrated as enablers for the thorough evaluation of cyber risk assessment methods.

Background: CK-18 is a serological marker of apoptosis that has been widely associated with fibrosis and steatosis in NASH. Many studies have showed association CK-18 levels with NAFLD. To date, there is a profound lack of rheumatology studies on the effect of MTX therapy with regard to CK-18 levels. The relationship between CK-18 levels and cumulative MTX dose, MTX treatment duration, weekly MTX dose is not available in the literature. Objectives: The main purpose of this study was to determine the relationship between serum cytokeratin-18 (CK-18) and cumulative methotrexate dose in rheumatology patients on methotrexate (MTX) therapy. Besides, we studied the correlations between CK-18 and clinical parameters including age, disease duration , duration of MTX therapy, cumulative steroid dose and biochemical parameters such as alanine transaminase (ALT) and aspartate aminotransferase AST). Methods : We recruited 79 rheumatology patients on methotrexate (MTX) therapy as MTX group and 38 patients not on MTX therapy as non-MTX group . All subjects were tested for their serum CK-18 levels using an enzyme-linked immunosorbent assay (ELISA) test. We identified 20 patients with the highest CK-18 levels and 20 patients with the lowest CK-18 levels and had ultrasound liver performed on them. Results : The median serum CK-18 levels were marginally higher among the patients on MTX (1.20 ng/mL [0.19-37.15]) compared to the non-MTX group (1.17 ng/mL [0.37-34.32]). There was a significantly positive relationship between serum CK-18 levels and cumulative MTX dose (r = 0.329, p = 0.003) and total duration of MTX therapy (r = 0.284, p = 0.011). Apart from the above-mentioned variables, the CK-18 levels in MTX group significantly correlated with age (r = 0.265, p = 0.018), ALT (r = 0.440, p = <0.001) and AST (r = 0.478, p= 0.004). Ultrasound liver findings showed median CK-18 levels was higher among patients with abnormal liver findings although statistical significance was not reached. Conclusion : Among patients on MTX therapy, serum CK-18 levels correlated positively with cumulative MTX dose , total duration of MTX treatment , ALT and AST levels. These findings are preliminary and requires validation by future studies in the field of rheumatology.

Background: Schizophrenia is a mental disorder associated with inflammatory and oxidative stress markers such as lactate; in addition, the rate of physical inactivity in this population is very high, which leads to physical and metabolic alterations. Aims: This study measured the effect of assisted physical exercise over lactate levels in schizophrenia. Methods: Stable outpatients with schizophrenia and a group of Healthy controls received two different programs of Physical exercise (Aerobic and Functional) in a clinical trial. Results: Patients had higher lactate compared to healthy controls before and after intervention, and had higher rate of lactate increase after activity. The finding of increased lactate in schizophrenia detected before and after physical exercise deserve further attention in biomarker studies and in the development of physical rehabilitation in schizophrenia, suggesting different profile of oxidative metabolism after physical exercise. Basal increased lactate may reflect mitochondrial dysfunction or metabolic dysregulation, and the higher rate of increase may reflect a different metabolic and oxidative process. Conclusion: The finding is in line with recent studies as surrogate of mitochondrial dysfunction in schizophrenia and points to the need of additional studies on mitochondrial activity in schizophrenia, and to additional care in the design of physical interventions in schizophrenia.