In the era of cloud computing and large-scale distributed systems, ensuring uninterrupted service and operational reliability is crucial. Conventional fault tolerance techniques usually take a reactive approach, addressing problems only after they arise. This can result in performance deterioration and downtime. With predictive machine learning models, this research offers a proactive approach to fault tolerance for distributed systems, preventing significant failures before they arise. Our research focuses on combining cutting-edge machine learning algorithms with real-time analysis of massive streams of operational data to predict abnormalities in the system and possible breakdowns. We employ supervised learning algorithms such as Random Forests and Gradient Boosting to predict faults with high accuracy. The predictive models are trained on historical data, capturing intricate patterns and correlations that precede system faults. Early defect detection made possible by this proactive approach enables preventative remedial measures to be taken, reducing downtime and preserving system integrity. To validate our approach, we designed and implemented a fault prediction framework within a simulated distributed system environment that mirrors contemporary cloud architectures. Our experiments demonstrate that the predictive models can successfully forecast a wide range of faults, from hardware failures to network disruptions, with significant lead time, providing a critical window for implementing preventive measures. Additionally, we assessed the impact of these pre-emptive actions on overall system performance, highlighting improved reliability and a reduction in mean time to recovery (MTTR). We also analyse the scalability and adaptability of our proposed solution within diverse and dynamic distributed environments. Through seamless integration with existing monitoring and management tools, our framework significantly enhances fault tolerance capabilities without requiring extensive restructuring of current systems. This work introduces a proactive approach to fault tolerance in distributed systems using predictive machine learning models. Unlike traditional reactive methods that respond to failures after they occur, this work focuses on anticipating faults before they happen.

The increasing relevance of anomaly detection in attributed networks is gaining traction in fields such as cybersecurity, finance, and healthcare. However, large-scale attributed networks often exhibit noisy and inconsistent node properties, which negatively affect anomaly detection accuracy and disrupt the network’s structure. A key challenge is maintaining the integrity of both network and node feature structures during the embedding process. To address this, we propose a novel approach that combines a Graph Convolution Auto encoder (GCAE) with self-supervised learning, proximity preservation, and adversarial training using Generative Adversarial Networks (GAN). First, Laplacian smoothing is applied to reduce noise in node properties, followed by Laplacian sharpening to highlight important features. These enhanced features are then fed into the GCAE, which encodes node attributes into a latent space using graph convolutional layers. Self-supervised tasks like attribute masking and edge prediction further enhance the GCAE’s ability to capture the graph’s structure. Additionally, proximity preservation ensures that the latent space reflects both first order and high-order proximity. The inclusion of GAN refines the embedding’s, aligning them closer to the true distribution of the graph data. This method effectively preserves both node features and network structure, making the embedding robust and distinguishable. Empirical evaluations on four real-world datasets demonstrate that our approach surpasses state-of-the-art methods, setting a new benchmark for anomaly detection in attributed networks. Our framework has significant potential to advance both research and practical applications in anomaly detection.

Phishing attacks are a form of social engineering technique used for stealing private information from users through emails. A general approach for phishing susceptibility analysis is to profile the user's personality using personality models such as the Five Factor Model (FFM) and find out the susceptibility for a set of phishing attempts. The FFM is a personality profiling system that scores participants on five separate personality traits: openness to experience (O), conscientiousness (C), extraversion (E), agreeableness (A), and neuroticism (N). However, existing approaches don't take into account the fact that based on the content, for example, a phishing email offering an enticing free prize might be very effective on a dominant O-personality (curious, open to new experience), but not to an N-personality (tendency of experiencing negative emotion). Therefore, it is necessary to consider the personality bias of the phishing email contents during the susceptibility analysis. In this paper, we have proposed a method to construct a dictionary based on the semantic similarity of prospective words describing the FFM. Words generated through this dictionary can be used to label the phishing emails according to the personality bias and serve as the key component of a personality bias classification system of phishing emails. We have validated our dictionary construction using a large public corpus of phishing email data which shows the potential of the proposed system in anti-phishing research.

This paper presents a high gain, low noise Cascoded LNA using T-matching network applicable for wireless applications. The amplifier use FHX76LP Low Noise SuperHEMT FET. The LNA designed used T-matching network consisting of lump reactive element at the input and the output terminal. The cascode low noise amplifier (LNA) produced gain of 18.5 dB and noise figure (NF) of 1.30 dB. The input reflection (S₁₁) and output return loss (S₂₂) are -11.5 dB and -12.3 dB respectively. The bandwidth of the amplifier recorded is 1.4 GHz. The input sensitivity is compliant with the IEEE 802.16 standards.