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Social Engineering Threat Analysis Using Large-Scale Synthetic Data
No full textWe frequently hear news about compromised systems, virus attacks, spam emails, stolen bank account numbers, and loss of money. Safeguarding and protecting digital assets against these and other cyber-attacks are extremely important in our digital connected world today. Many organizations spend substantial amounts of money to protect their digital assets. One type of cyber threat that is rampant these days is social engineering attacks that work on human psychology. These attacks typically persuade, convince, trick and threaten naïve and innocent individuals to divulge sensitive information to the attackers. Consequently, traditional approaches have not been effective or successful in preventing these attack types. In this paper, we propose a machine learning model to detect these types of threats. The model is trained using a large synthetic dataset of 10,000 samples to simulate various types of real-world social engineering threats such as phishing, spear phishing, whaling, vishing, smishing, baiting, and pretexting. Our analysis on attack types, patterns, and characteristics revealed interesting insights. Our model achieved an accuracy of 0.8984 and an F1 score of 0.9253, demonstrating its effectiveness in detecting social engineering attacks. The use of synthetic data overcomes the problem of lack of availability of real-world data due to privacy issues, and is demonstrated in thiswork to be safe, scalable, ethics friendly and effective
Creating an Android-based Calisthenics Application to Assist Students in Improving Their Physical Fitness
No full textCollege students, particularly those heavily involved in coursework, frequently do not prioritize physical fitness. Regular exercise is essential for preserving physical fitness and facilitating demanding academic tasks. Although there are other fitness programs available, such as GYM, jogging, CrossFit, and Yoga, users sometimes fail to make full use of them because the content is confined to exercise videos and descriptions. This matter highlights the progress of the Android application Kali Tech, which specifically concentrates on organizing and documenting calisthenics workouts.This program employs the concept of gamification by utilizing student achievement levels to encourage students to be diligent in doing exercise.The application was developed via the Software Development Life Cycle (SDLC)Prototyping methodology in order to fulfill user requirements by incorporating features that promote regular utilization. Kali Tech underwent a one-month testing period, during which data on the responders' blood pressure was also gathered. The data analysis demonstrated that the utilization of the Kali Tech application resulted in an enhancement of the participants' physical fitness, as seen by the blood pressure graphs nearing the standard levels when using the app. The conclusion was further supported by the results of an Independent Sample T-Test analysis and the visual representations of blood pressure graphs, which demonstrated the consistent levels of respondents' blood pressure
Machine Learning Model for Predicting Net Environmental Effects
No full textEnvironmental sustainability is a global challenge in the face of increasing incidences of disasters affecting communities worldwide. This requires predicting net environmental effects accurately. While various approaches exist, we need more sophisticated prediction models that account for both environmental and social factors. This study presents a proof-of-concept machine learning model for predicting net environmental effects using synthetic data. We developed a multiple linear regression model incorporating nine key features: renewable energy usage, carbon emissions, air quality index, water usage, biodiversityimpact, land use, public awareness, and environmental attitudes. We generated a synthetic dataset of 1000 samples using probability distributions and correlation structures derived from environmental literature and expert knowledge. Our model achieved an R-squared value of 0.67, demonstratingmoderate predictive power. Feature importance analysis revealed renewable energy usage (coefficient = 0.71) and public awareness (coefficient = 0.44) as significant positive factors influencing environmental outcomes. Model validation included residual analysis and feature importance assessment, with results suggesting reasonable performance within linear regression constraints. Limitations of our study include reliance on synthetic data, assumption of linear relationships between variables, and limited environmental factors. Notwithstanding, our findings provide insights for environmental policymaking, particularly regarding renewable energy adoption and public awareness campaigns. Future work could focus on incorporating real-world data, exploring non-linear modeling approaches, and expanding the feature set to capture more complex environmental interactions. Our research contributes to data-driven environmental assessment by demonstrating the feasibility of combining both physical and social factors in predictive modelin
Climate Change Analysis in Malaysia Using Machine Learning
No full textClimate change presents significant challenges to ecosystems, economies, and societies globally. In Malaysia, a tropical country highly dependent on itsnatural resources, the impacts are evident in altered rainfall patterns, rising temperatures, and extreme weather events. Despite these challenges, many studies still predominantly rely on traditional statistical methods, which limit their capacity for making accurate climate predictions and developing effective policy solutions.Thisstudy effectively addresses the existing gap in research by analyzing extensive historical climate data using advanced machine learning (ML) techniques. The primary focus is on accurately forecasting trends in both precipitation patterns and surface air temperature fluctuations.Performance measures like Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are used to assess three MLmodels: Support Vector Regression (SVR), Random Forest Regression (RFR) and Linear Regression(LR). The findings demonstrate that LR performs better than the other models in forecasting patterns of precipitation and temperature. The results suggest a significant increase in temperature and unpredictable patterns of precipitation, and that posesmajor implications for agriculture, infrastructure resilience, and water management.Malaysia's climate resilience is improved by this research, which promotes data-driven policymaking by assessing current climate adaptationmethods and offering practical idea
Anomaly Detection in Network Traffic for Insider Threat Identification: A Comparative Study of Unsupervised and Supervised Machine Learning Approaches
No full textInsider threats pose a significant and growing risk to organizational cybersecurity, with recent studies indicating a 47% increase in insider incidents from 2018 to 2022. This paper presents a comparative analysis of unsupervised and supervised machine learning approaches for detecting potential insider threats through network traffic anomaly identification. We develop and evaluate an Isolation Forest (unsupervised) and a Random Forest (supervised) model, training them on a simulated dataset representing six months of network logs from a mid-sized company. Our study introduces a unique feature set combining traditional network metrics with temporal and behaviouralindicators, enhancing the models' detection capabilities. Results show that the Random Forest classifier outperforms the Isolation Forest, with F1-scores of 0.6425 and 0.4624, respectively. However, the unsupervised approach shows promise in scenarios lacking labelleddata. Key findings reveal that increased connection frequency and data transfer volume are critical indicators of potential threats, with temporal patterns also playing a significant role. This study provides valuable insights into the strengths and limitations of each approach, offering practical implications for real-world digital forensics investigations. We contribute to the field by proposing a hybrid approach that leverages the strengths of both methods, potentially improving the accuracy and adaptability of insider threat detection systems. These findings pave the way for more robust, context-aware cybersecurity measures in the digital age.Keywords—Insider Threat Detection, Network Security, Machine Learning, Anomaly Detection, Digital ForensicsReceived:29August2024; Accepted: 28December2024; Published:16 June 2025This is an open access article under the CC BY-NC-ND 4.0license.1.INTRODUCTIONCybersecurity threats come from both within and without organizations. Unlike external attacks, insider threats originate from within the organization's network, and this makes threat detection more challenging because they have legal access to corporate resources. According to the 2023 Insider Threat Report by Cybersecurity Insiders, 74% of organizations feel vulnerable to insider threats, with 39% reporting an increase in insider incidents over the past 1
Inline Bandpass Filtering Waveguide Antenna with Two Transmission Zeros Based on All-resonator Structures
No full textNowadays, a significant amount of effort is being devoted to miniaturizing the design of filtering antennas for future wireless communication systems. However, there are numerous design strategies documented in the literature, most of which are relevant tospecific microwave circuit configurations. Moreover, they often require additional matching circuits, which increase the overall size of the filtering antenna. This study introduces a generic coupling matrix approach for developing a second-order filtering antenna with two transmission zeros operating at cross-band frequencies. The last resonator's output is physically connected to free space, and two inline coupled rectangular waveguide cavity resonators operating in the TE101mode are employed. Impressive, simulatedresults have been achieved, demonstrating a fractional bandwidth (FBW) exceeding 5% at a reflection coefficient S11of −9.98 dB. Additionally, the gain response remains remarkably flat, spanning from 8.9 to 9.3 GHz, with a negligible variation of ±0.07 dB around 3.77 dB.The tinyand low-profile design of the suggestedfiltering antenna offers significant advantages for radar application
Design and Fabrication of An Automated Glass Bottle Cutter for Reuse and Recycling Bottle Glass Products
No full textThe glass bottle cutter has a substantial commercialisation potential, as present trends indicate a growing interest in repurposing waste materials. This apparatus enables communities to manufacture new items from discarded glass bottles, including drinking glasses, ashtrays, and vases. Disposal at recycling centres or craft stores is essential for businesses such as restaurants and bars that often produce excess glass waste. Hence, this apparatus is resilient and long-lasting, designed to handle significantly larger amounts more efficiently than manual glass bottle cutters, which require scoring the bottle and alternating between hot and cold water. The main goals of this project are to develop a prototype for a glass bottle-cutting machine and to manufacture the machine according to the designed prototype. The manufacturing process encompasses measuring, cutting, welding, and drilling, with the machine predominantly constructed from metals. It employs a DC motor to facilitatethe rotation of the diamond blade, substituting conventional wheel cutters. This design markedly diminishes the necessity for physical labour and enables bottles to be severed in under one minute. The cutter accelerates the procedure, yieldinga superior finish with reduced physical exertion. The design and analysis of the prototype have been successful. Potential enhancements may involve the integration of a safety button, applying a coolant to inhibit the dispersion of glass dust, and includinga polishing mechanism for smoother edges. These upgrades would boost the machine's efficiency and desirability
Model Based Implementation of Wireless Power Transfer System for Charging of E-Vehicles
No full textThe popularity of electric vehicles has been increasing day by day due to the effect of the pollution caused by the fossil fuels. In the present scenario the difficulty arising in the use of the electric based transportation is the unavailability of the charging stations. So wireless charging system have emerged as one of the solutions for charging of electricity based vehicles. Wireless based electric vehicles come with an opportunity of charging those vehicles that can have the priority of not having charging plugs. Wireless charging technology comes with an option of spark free operation, reliable and user friendly as compared to the plug charging option. This technology makes use of a common charger that can be used for all types of electric operated vehicles. Wireless power transfer technology requires design of inductive coils which involves proper selection of inductances values. A high frequency full bridge converter has to be tuned for wireless power transfer between the transmitter and receiver coils. This paper presents an effective way of charging electric vehicles that has been implemented in Matlab/Simulink simulations. Rigorous simulations were carried out and analysis were conducted to evaluate the system's performance
Constructing Womanhood in Fagun Haway (2019): Balancing Female Agency within a Patriarchal Narrative
No full textThe Language Movement of 1952 was a pivotal event in Bangladesh’s history, sparking nationalism that eventually led to independence from Pakistan in 1971. Despite women’s active participation in this movement, their contributions have received little acknowledgment in historical narratives. This study examines how the film Fagun Haway (2019) portrays the female protagonist in the context of the 1952 Language Movement. Using a qualitative content analysis of the film guided by Stuart Hall’s (1997) representation theory, the analysis explores whether the film challenges or reinforces gender stereotypes. The findings indicate that while the protagonist’s engagement in the Language Movement demonstrates subtle female agency and challenges some stereotypical portrayals of women, the film’s narrative ultimately centres male heroism. In Fagun Haway (2019), female participation is depicted and valued, yet the male protagonist remains the dominant figure, reflecting an underlying patriarchal bias. This tension between female agency and patriarchal framing highlights the complex negotiation of womanhood in Bangladeshi historical cinema
A microscale Monte Carlo analysis on skin dosimetry and DNA damage induced by radon-rich water exposure
No full textRadon (222Rn) significantly contributes to natural background radiation and poses well-documented health risks when inhaled or ingested. However, the effects of radon exposure on the skin, particularly through direct contact with radon-rich spring waters during activities such as bathing or spa treatments, have not been thoroughly investigated. This study investigates the dosimetric and radiobiological impacts of radon exposure on skin through Monte Carlo simulations using TOPAS and its TOPAS-nBio extension. We modeled two radon distributions: a Volume source in direct contact with the skin and a Permeated source penetrating 20 μm into the skin. Absorbed doses and direct DNA damages were evaluated in two reference cells: one on the skin surface (Cellsurf) and another at a depth of 70 μm (Cell70), following International Commission on Radiation Units and Measurements (ICRU) guidelines for skin dosimetry. The results reveal that skin surface cells receive doses 2 - 3 orders of magnitude higher than deeper reference cells across both radon distributions, with significantly more severe DNA damage, including higher yields of double-strand breaks (DSBs) and complex DSBs. Notably, alpha particle penetration extends up to 80 μm in the Permeated source scenario, potentially impacting deeper skin layers beyond the epidermis. The SSB/DSB ratio - a key indicator of damage severity - is markedly lower in surface cells, indicating a greater biological risk at the skin surface compared to deeper layers. These findings highlight the predominant impact of radon-rich water on superficial skin layers, suggesting therapeutic potential for conditions like fungal infections, while raising concerns about cumulative DNA damage in regions with thinner epidermal layers or during prolonged exposure. Our study highlights the need for a balanced approach in evaluating both the therapeutic benefits and health risks of radon exposure, particularly in high-radon environments. Future research should incorporate skin heterogeneities and indirect DNA damage mechanisms, such as reactive oxygen species (ROS) generation, to further refine risk assessments and explore therapeutic applications