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Enhanced detection of APT vector lateral movement in organizational networks using lightweight machine learning.
Get PDFThe successful penetration of government, corporate, and organizational IT systems by state and non-state actors deploying APT vectors continues at an alarming pace. Advanced Persistent Threat (APT) attacks continue to pose significant challenges for organizations despite technological advancements in artificial intelligence (AI)-based defense mechanisms. While AI has enhanced organizational capabilities for deterrence, detection, and mitigation of APTs, the global escalation in reported incidents, particularly those successfully penetrating critical government infrastructure has heightened concerns among information technology (IT) security administrators and decision-makers. Literature review has identified the stealthy lateral movement (LM) of malware within the initially infected local area network (LAN) as a significant concern. However, current literature has yet to propose a viable approach for resource-efficient, real-time detection of APT malware lateral movement within the initially compromised LAN following perimeter breach. Researchers have suggested the nature of the dataset, optimal feature selection, and the choice of machine learning (ML) techniques as critical factors for detection. Hence, the objective of the research described here was to successfully demonstrate a simplified lightweight ML method for detecting the LM of APT vectors. While the nearest detection rate achieved in the LM domain within LAN was 99.89%, as reported in relevant studies, our approach surpassed it, with a detection rate of 99.95% for the modified random forest (RF) classifier for dataset 1. Additionally, our approach achieved a perfect 100% detection rate for the decision tree (DT) and RF classifiers with dataset 2, a milestone not previously reached in studies within this domain involving two distinct datasets. Using the ML life cycle methodology, we deployed K-nearest neighbor (KNN), support vector machine (SVM), DT, and RF on three relevant datasets to detect the LM of APTs at the affected LAN prior to data exfiltration/destruction. Feature engineering presented four critical APT LM intrusion detection (ID) indicators (features) across the three datasets, namely, the source port number, the destination port number, the packets, and the bytes. This study demonstrates the effectiveness of lightweight ML classifiers in detecting APT lateral movement after network perimeter breach. It contributes to the field by proposing a non-intrusive network detection method capable of identifying APT malware before data exfiltration, thus providing an additional layer of organizational defense
Extracting data-driven insights from LiDAR and SCADA data for improved analysis of offshore wind farms.
Get PDFOffshore wind energy is essential for the global transition to renewable energy. Wind farms, such as those at Anholt and Westermost Rough, are crucial in providing clean electricity. LiDAR (Light Detection and Ranging) and SCADA (Supervisory Control and Data Acquisition) data are particularly valuable for understanding turbine behavior and predicting energy output. Recent advancements in machine learning (ML) and artificial intelligence (AI) have opened new possibilities for analyzing complex wind farm data. This study employs data-driven techniques, specifically XGBoost and Long Short-Term Memory (LSTM) networks, to enhance the analysis of LiDAR and SCADA data from the Anholt and Westermost Rough offshore wind farms. Data-driven filters were applied to enhance the quality of input data, thereby improving the accuracy of the models and reducing noise. XGBoost demonstrated computational efficiency, training faster than Bi-LSTM while achieving an R2 of 0.97 for Anholt and 0.86 for Westermost Rough. While Bi-LSTM successfully captured temporal dependencies, it required significantly longer training times. RMSE and MSE results indicate that XGBoost outperformed Bi-LSTM at Anholt by 6.3% and 12.2%, respectively, whereas both models showed higher errors at Westermost Rough, likely due to data dependency. The residual analysis confirmed tighter error distribution for Anholt, whereas Westermost Rough exhibited higher prediction uncertainties. Wind speed loss analysis revealed that turbines in the selected rows experienced variations, highlighting the impact of local turbulence on wind flow characteristics
Mobilising for effective redress: the role of international human rights complaints in police accountability in Victoria.
Get PDFThis article analyses the role of international human rights complaints mechanisms in the police accountability landscape in Australia, focusing on individual complaints to the United Nations Human Rights Committee (HRC) under the First Optional Protocol to the International Covenant on Civil and Political Rights (ICCPR), to which Australia is a signatory. We do so through a case study of Horvath v Australia, which involved 'a disgraceful and outrageous display of police force' against Corinna Horvath in 1996 (State of Victoria v Horvath [2002] VSCA 177, 9-10 [15]). The HRC found that Australia had violated Horvath's rights under article 2(3) of the ICCPR to an 'effective remedy' for substantive rights violations, given the difficulties she faced in accessing compensation through domestic mechanisms. Horvath's complaint was successful in spurring individual compensation and a formal apology, as well as shaping legislative reforms to civil liability for police harms. However, given the limits of these reforms and the ongoing failures of Victoria's police complaints and disciplinary processes, Australia remains in breach of its human rights obligations. Ultimately, our analysis demonstrates how the potential of human rights complaints for systemic change relies on mobilization by civil society, in the face of enduring political inertia
Performance characterization of VARI-processed plain-woven glass/jute hybrid epoxy composites for renewable energy infrastructures: experimental–numerical synergy.
Get PDFThis study investigates the mechanical behavior and structural viability of hybrid woven glass–jute fiber-reinforced epoxy composites fabricated using the vacuum-assisted resin infusion (VARI) technique for potential use in renewable energy infrastructure. The objective is to evaluate the synergistic performance enhancement achievable through hybridization of synthetic and natural fibers in a layered architecture. Experimental characterization of laminates with varying ply counts (2, 6, 8, and 12) were conducted to assess the composites' mechanical, thermal, and microstructural properties. Finite element analysis (FEA) using ANSYS was performed to simulate tensile and bending behaviors, employing a gradual mesh refinement strategy to ensure numerical accuracy. Results showed that the 8-ply laminate achieved optimal mechanical performance, with tensile and flexural strength improvements of 18.5 % and 53.89 %, respectively, compared to the 2-ply configuration. The 12-ply composite exhibited superior impact resistance, absorbing up to 2.70 J of energy, representing a 67.8 % increase over lower-ply variants. The 6-ply system yielded the highest hardness, attributed to enhanced compaction and surface stiffness. Thermogravimetric analysis (TGA) revealed an onset degradation temperature of 315 °C and maximum thermal stability at 455 °C, supporting the material's suitability for elevated-temperature applications. FEA simulations closely matched experimental results, confirming precise alignment between simulated and observed tensile and flexural stresses. The study highlights the potential of stacked plain-woven glass/jute hybrid composites as sustainable material development, combining lightweight, high-strength, and thermally resilient hybrid composites for renewable energy infrastructure such as wind turbine blades, solar panel module supports, and other structural components
Legal and policy gaps in Nigeria's renewable energy framework: a case for wave and tidal energy.
Get PDFNigeria's wave and tidal energy resources remain underdeveloped, unlike other renewable energy resources such as bioenergy, hydropower, solar and wind, which have witnessed limited development. In essence, the research problem is how to justify the necessity for harnessing wave and tidal energy resources in Nigeria and ultimately provide a legal framework to integrate wave and tidal energy into Nigeria's overall renewable energy enactments. This thesis aimed to search for any gaps or inconsistencies in Nigeria’s RE enactments and its governance strategy concerning wave and tidal energy. The objective of the thesis was to identify and critically analyse Nigeria's renewable energy enactments, related policies, and laws, with a specific focus on how they deal with wave and tidal energy. The research methodology and design utilised qualitative, doctrinal, and comparative approaches for desk-based data collection and data analysis of primary and secondary legal sources. The thesis also critically examined the enactments in Scotland, Canada (British Columbia and Nova Scotia), and South Korea for their best practices and lessons. This study found that Nigeria's current enactments did not specifically address wave and tidal energy resources, and, as a result, Nigeria's wave and tidal energy sector has not contributed towards electricity generation. Further, the study found gaps and inconsistencies within the renewable energy legal framework. Finally, this research highlighted lessons from jurisdictions with established ocean energy systems, specifically in wave and tidal energy. This research contributed to knowledge by filing the legislative and policy gaps of Nigeria's renewable energy sector. It also identified some critical gaps in both literature and law, and proposed a legal framework for integrating ocean energy into Nigeria's existing energy regime. This thesis laid a foundation for future academic, legal, and policy developments in Nigeria and similar coastal states in West Africa
Where are we? Where next? An exploration into the development and implementation of practice-based interprofessional education for student pharmacists in Scotland.
Get PDFInterprofessional collaborative practice (IPCP) is considered essential to address the increasingly complex needs of patients. Subsequently, developing a workforce with the right competencies is a requisite to ensure safe, effective and efficient person-centred care within integrated health and social care systems. This in turn has increased focus on interprofessional education (IPE) as a necessary step in preparing a "collaborative practice-ready" workforce. The overarching aim of this research programme was to explore the development and implementation of practice-based IPE in the experiential learning (EL) curriculum of Master of Pharmacy (MPharm) programmes in Scotland. The research adopted a pragmatic worldview and included four empirical studies conducted over two phases. A case study research strategy allowed an in-depth exploration; a multimodal approach was employed. The decision to underpin the programme of research with systems theory encapsulated the complexity of IPE and allowed consideration of multiple presage, process and product factors within the teaching and learning environment. Phase 1 aimed to identify what is happening, explain why it is happening and inform future action. It included three studies and involved key stakeholder groups, allowing diverse views and experiences to be explored. Multiple methodologies were used - quantitative (cross-sectional online/paper questionnaires), qualitative (document analysis) and mixed methods (cross-sectional online questionnaire and group interviews). Key findings identified that overall, stakeholders perceived IPE as essential to prepare student pharmacists for future IPCP and improve patient outcomes. Stakeholders reported a lack of visibility of IPE in the EL curriculum and a reliance on informally planned or unplanned IPE experiences during placements; mainly involving interactions between student pharmacists and qualified health and social care professionals. Very few examples of formally planned IPE experiences involving different student groups were identified. Variation in approaches and opportunities which presented during placements, were perceived by stakeholders as potentially leading to inequitable EL experiences and missed interprofessional learning opportunities. Findings identified a need to rethink current IPE provision; including better collaboration and co-ordination between universities and placement providers and better collaboration between practice teams to overcome contextual factors – logistical, organisational and regulatory. Additional funding and a need to focus on a continuum of learning were also perceived as necessary to support practice-based IPE development and implementation. Phase 2 focused on moving forward with IPE curricular design. Its aim was to design a framework to support the development and implementation of practice-based IPE initiatives. A consensus method using a modified Delphi technique was employed; with statement development informed by key findings from the first research phase and a document analysis of international IPE frameworks. A heterogenous expert panel with 45 members from key stakeholder groups was appointed. High levels of consensus were achieved on statements relating to IPE learning outcomes and collaborative core competencies/capabilities that student pharmacists should develop through IPE experiences. Two statements relating to summative assessment did not reach consensus. This research has produced original findings; identifying challenges but also opportunities. It has provided a strong foundation that can be used to inform future development and implementation of practice-based IPE to better prepare student pharmacists for future collaborative practice. Findings from this research challenge educators and practice providers at top stakeholder level to work together to devise a strategic plan with a clear mission statement to drive forward the agenda for practice-based IPE in undergraduate health and social care curricula in Scotland
The utilization of Harmota olive oil to produce a sustainable biofuel.
Get PDFBiodiesel may be considered a renewable and clean energy source that can contribute to reducing global greenhouse emissions and global warming phenomena. Biodiesel possesses several advantages over traditional petroleum diesel fuel, such as fewer greenhouse gas emissions and environmentally friendly fuel. The local olive oil factories dispose of the olive pomace, a non-edible by-product stream from the production process, with low production costs. Olive oil and pomace oil can be considered appropriate feedstock supporting biodiesel production worldwide. In this study, biodiesel was produced from a local olive oil sample sourced from Harmota olive oil in the Koya district of Iraqi Kurdistan. The produced biodiesel was also examined by several laboratory tests, such as density and cetane value, and the results were compared well with ASTM D6751 standards. The transesterification process utilized potassium hydroxide (KOH) as a catalyst, with varying methanol-to-oil molar ratios. The optimal conditions were identified as a 7:1 methanol-to-oil ratio and 0.5 grams of KOH, achieving a high biodiesel yield of approximately 91%. The resulting biodiesel demonstrated key fuel properties—density (879 kg/m³), viscosity (5.125 mm²/s), cetane number (64), and flash point (165 °C)—which are all within the ASTM D6751 biodiesel standard limits. Furthermore, this study shows the intriguing possibilities of using Harmota olive oil and its by-product, olive pomace oil, as a sustainable and effective feedstock. But it goes beyond that: guaranteeing high-quality biodiesel that is both affordable and environmentally benign depends on process optimization
Integration in an area of increasing diversity: challenges and facilitators.
Get PDFScotland is becoming more ethnically diverse. The percentage of the population of Aberdeen with a minority ethnic background increased from 17.1% in 2011 to 24.8% in 2022. Scotland has a rich history of migration and settlement, which has significantly shaped its communities, culture and economy. The Scottish Government recognises that migration is required to overcome demographic challenges. Therefore, it is essential to understand how migrants integrate into their new communities. Torry has become one of the most ethnically diverse areas of Aberdeen. Torry is one of the priority neighbourhoods in Aberdeen, as identified using the Scottish Index of Multiple Deprivation (SIMD). This indicates that on average, local residents experience fewer socio-economic opportunities compared to those living in most other parts of the city. In the Spring and Summer of 2021, we interviewed 25 residents about their experiences of living in Torry, focusing on community and intergroup relations. Our research uncovered a mixed picture when it came to integration. We found evidence of some segregation between ethnic groups, with some people mainly interacting with members of the same ethnic group. There was also encouraging evidence of positive connections between people with different ethnic and migrant backgrounds. In this report, we summarise the key challenges to and facilitators of integration that we identified in our analysis
Interpretable decision trees to predict solution fitness.
Get PDFMetaheuristic algorithms are powerful tools for tackling complex optimization problems, but their black-box nature often hinders user trust and understanding. This paper presents a novel methodology for enhancing the explainability of metaheuristics by employing decision trees with splitting criteria based on Partial Solutions. These represent beneficial sub-structures of solutions and provide insights into the problem landscape and solution characteristics. By constructing decision trees that consider the presence or absence of specific patterns in solutions, we produce a transparent model capable of predicting solution fitness. The proposed methodology is evaluated on a diverse set of benchmark problems and metaheuristic algorithms, demonstrating its effectiveness and flexibility as a post-hoc explainability tool. Our results show that our decision trees can match and usually surpass traditional methods in predicting the fitness of candidate solutions for the tested benchmark problems, with one of our methods demonstrating an improvement between 4.4% and 16.7% in R2 predictive performance for shallower trees trained on a Genetic Algorithm's data. These trees are able to maintain competitive predictive performance while using more interpretable splitting criteria
Directed structural evolution of nickel nanoparticles into atomically dispersed sites for efficient CO2 electroreduction.
Get PDFElectrochemical CO2 reduction (CO2RR) to carbon monoxide (CO) offers a sustainable pathway for carbon utilization, yet challenges remain in terms of improving selectivity and activity. Herein, we report a Ni/NC catalyst synthesized via a milling ‐ pyrolysis method, in which Ni particles anchored on nitrogen‐doped carbon (NC) are electrochemically activated under an Ar atmosphere, leading to their structural evolution into single‐atom Ni sites. After activation in Ar atmosphere, the current density nearly doubles (from ≈30 to ≈60 mA cm−2), and concurrently, the Faradaic efficiency of CO stays at ∼90% with the potential set to ‐0.8 V vs. RHE. Comprehensive characterizations, including X‐ray photoelectron spectroscopy (XPS), aberration ‐ corrected scanning transmission electron microscopy (AC ‐ STEM), along with extended X ‐ ray absorption fine structure (EXAFS), confirm the change of Ni particles into atomically dispersed Ni‐Nx moieties during activation. Notably, in situ Raman spectroscopy identifies *COOH as the key intermediate, while electrochemical analyses reveal accelerated charge transfer and favorable kinetics for Ar‐Ni/NC. Additionally, the catalyst shows great selectivity and stability over 24 hours of non ‐ stop operation. This study emphasizes the dynamic change of Ni active sites under working conditions, offering useful ideas for designing transition metal catalysts for large ‐ scale CO2 to CO conversion
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