This article presents a comparative performance analysis of two primary Cascading Style Sheets (CSS) animation methods — transition and animation — under conditions involving large-scale DOM rendering. Despite widespread beliefs among developers that transitions generally offer better performance, there is a lack of quantitative research validating this assumption in practical scenarios. To address this gap, we developed a web application capable of rendering and animating from 50 up to 10,000 elements simultaneously. Performance metrics were collected in real-time using built-in browser APIs, including average and minimum FPS, frame delta (time between frames), JavaScript heap memory usage, and total animation duration. Tests were conducted on the latest versions of Chrome and Firefox browsers. Results demonstrate that the choice of animation method heavily influences performance, the specific CSS property being animated (e.g., opacity, transform, blur, background-color), and the number of animated elements. While transitions showed slightly better efficiency in terms of JavaScript resource consumption, other metrics such as frame rate and rendering stability were comparable between the two methods. Notably, animations consistently require more memory, which may affect scalability in high-load interfaces. This research provides valuable quantitative data to guide front-end developers in selecting appropriate animation techniques for complex interfaces. It also emphasizes the importance of selecting GPU-friendly CSS properties to enhance rendering performance. Overall, the findings suggest that while the difference in performance between transitions and animations is modest, understanding their behavior under load is crucial for building smooth and responsive web experiences.
Many computer networks in operation today currently use both IPv4 and IPv6 stacks. On the other hand, there is a transition towards IPv6-only networks as a result of the limited availability of IPv4 addresses. The primary protocol for link-local IPv6 communication is the Neighbor Discovery Protocol (NDP). Regrettably, its insecure design and basic scope-based security mechanisms make the local network susceptible to insider threats. The Internet Engineering Task Force’s recommended security mechanism for NDP, which is Secure Neighbor Discovery (SEND), is well documented but complex and unsuitable for resource-constrained devices and networks. Trust-ND was positioned as an alternative to SEND as a lightweight trust-based distributed approach using the NDP extension headers. However, its timestamp design and utilization render it susceptible to temporal DoS vulnerabilities. Therefore, this research proposes eTrustND to improve the Trust-ND mechanism for securing IPv6 link-local networks from insider attacks by addressing the existing vulnerabilities by modifying the timestamp reference, format, precision, and validation rules. This paper documents the methodology, the experimentation, and the resulting outcome that show eTrustND eliminates Trust-ND’s temporal DoS vulnerabilities without adding computational and protocol overhead. It also highlights the challenges and best practices of timestamp design and usage in security mechanisms and protocols.
Commonly used file-sharing systems suffer from impermissible security risks such as impersonation, credential theft, and QR code exploitation, which can expose sensitive information. This paper describes and evaluates an AI-driven secure file-sharing system designed to alleviate these risks through a multi-layered authentication strategy. The complete system involves AES-128 encryption, unique QR code referencing, live facial recognition, and One-Time Password (OTP) confirmation. In our proposed workflow, the sender encrypts a reference to the file and links it to facial data of the recipient, if applicable, and embeds this information into a QR code, that is dynamically generated during the send operation. To gain access to the file, the user must scan the QR code, undergo live biometric access verification, and subsequently enter an OTP sent to their email address. Experimental results give confidence in the high level of efficacy and robustness of the system: The facial recognition component achieved 99.0% facial recognition accuracy under optimal conditions and achieved 100% rejection against static image spoofing attacks. The average end-to-end server-side latency was about 3.25 seconds, confirming the system would be viable for file sharing. This method combines strong encryption with two-factor identity verification to guarantee confidentiality, mitigate the use of reusable tokens, and provide a secure way to share sensitive documents across a range of sectors, such as healthcare, education, and legal services. The next iteration will be enhanced with liveness detection, dynamic QR codes, and an app to facilitate the entire process of verification, tokenization, and share sensitive documents.
Healthcare Machine Learning (ML) applications face significant challenges in accessing high-quality training data due to stringent privacy regulations, institutional data silos, and concerns over patient confidentiality. This paper explores synthetic data generation as a viable and privacy-preserving alternative to real patient data for developing ML models in healthcare settings. We present techniques for generating realistic vital signs data including body temperature, blood pressure, heart rate, respiratory rate, and oxygen saturation according to appropriate statistical distributions. In addition, we demonstrate how synthetic datasets generated can be used to train diagnostic prediction models. The generated datasets were applied to multiple diagnostic prediction tasks such as hypertension, fever, Chronic Obstructive Pulmonary Disease, atrial fibrillation, and diabetes mellitus. Experimental results reveal that ML models trained solely on synthetic data achieved comparable predictive performance to those trained on real datasets for conditions with explicit physiological manifestations. In particular, gradient boosting classifiers attained an Area Under the Curve (AUC) of up to 0.89 in predicting hypertension. We also illustrate that augmenting sparse real patient data with artificial samples preserves model accuracy at the expense of decreased reliance on sensitive data. This method has great potential to satisfy healthcare organizations who are interested in creating stable ML applications without compromising on privacy standards like Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR).
In the evolving landscape of cybersecurity, Intrusion Detection Systems (IDS) play a vital role in safeguarding computer networks against malicious activity. Traditional signature-based IDS approaches are increasingly ineffective in detecting novel, complex, or zero-day attacks due to their reliance on predefined rules. To overcome these limitations, this study proposes an AI-driven IDS that integrates both classical machine learning and modern deep learning techniques. The framework introduces and compares three models such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), and a hybrid CNN-SVM model. The system is designed to analyze network traffic patterns and classify them as either benign or malicious, enhancing detection capabilities through intelligent feature learning and classification. Two widely recognized benchmark datasets are used to train and validate the models, ensuring the system's applicability to a variety of network environments. Furthermore, the research includes the development of a real-time detection component that incorporates live packet capture, feature extraction, and dynamic visualization via a dashboard interface. This paper contributes to the field by demonstrating how hybrid AI models can effectively address the challenges of network intrusion detection. The study emphasizes the importance of combining traditional and deep learning approaches to build scalable, adaptive, and accurate intrusion detection systems for modern network infrastructures.
