Enhancing Social Protection Through Blockchain-Based Identity Verification And Data Communication: A Systematic Review

Abstract. This study reviews blockchain’s potential to enhance social protection programmes through identity verification and data communication. Traditional systems face issues like unreliable identity verification and data vulnerability, which blockchain’s decentralized, immutable, and transparent nature can address. The study aims to analyse blockchain’s benefits, current implementations, and deployment challenges in social protection. Using a systematic

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Information Communication and Network Security System Modelling using Geometry Based Block Code in Information Theory (GBCIT) Technique

Abstract: Information are transmitted over networks between two or more communicating and co-operating parties, through an established logical information channel which defines a route from source to destination, this method of information transmission in recent time, has becomes vulnerable to third party’s threat to confidentiality, authenticity and security of such information. To alleviate these challenges,

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Secured Mobile Ad-Hoc Network (MANET) Using Lightweight Encryption Scheme

Abstract: The integration of Advanced Encryption Standard (AES) encryption into the Routing protocol known as Ad Hoc On-Demand Distance Vector (AODV) enhances the security and confidentiality of control messages exchanged between nodes. This work explores the implementation of AES-128 bits encryption in AODV called Lightweight AODV (LwAODV) to protect critical routing information from unauthorized access

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BUAS: A Blockchain-Enabled Trust-Aware Security Architecture for Scalable and Resilient UAV Networks

Abstract: The growing deployment of Unmanned Aerial Vehicles (UAVs) in critical infrastructures introduces a complex web of security, trust, and performance challenges particularly as networks scale and interact with heterogeneous edge environments. This paper presents BUAS, a novel Blockchain enabled UAV Architecture that integrates Bayesian trust m odeling, encrypted UAV to ground station communication, and

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A Hybrid Deep Learning and Symbolic AI for Anomaly Detection in Heterogeneous High-Performance Computing Systems

Abstract: Anomaly detection in heterogeneous High Performance Computing (HPC) systems is challenging because the environment of multi-component hardware is complex. Traditional detection methods are not compatible with high-dimensional data and are heterogeneous. This study proposes a new hybrid approach by combining deep learning and symbolic artificial intelligence techniques to improve the accuracy and interpretability of

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Detecting Fraudulent Financial Transactions using Deep Learning and Transaction Log Analysis

Abstract: The financial sector has seen a significant rise in fraudulent activities, costing businesses billions of naira annually. Fraudulent transactions often involve unautho rized account access, manipulation of transaction data, or theft, leaving businesses and consumers vulnerable. This study aims to enhance financial fraud detection using a deep learning approach combining Bidirectional Long Short Term

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Modelling Emotional States for Enhanced Brainwave Based Authentication Systems

Abstract A developing biometric technique called brainwave authentication uses the distinctive electrical patterns produced by the human brain to identify a person. When it comes to security and durability, brainwave authentication surpasses standard biometrics as it utilizes an individual’s own neural signatures, rather than depending on exterior physical or behavioural traits. The proposed brainwave authentication

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Minimising False Alarm Rate in Network Intrusion Detection System Model Using KNN Classifier and Chi-Square for Feature Selection

Abstract False alarms in network intrusion detection systems (NIDS) can lead to unnecessary and costly investigations and reduce the credibility of the system. Network intrusion detection systems (NIDS) are tools that monitor network traffic and detect malicious or anomalous behaviours. However, NIDS face many challenges, such as the high dimensionality of network data, class imbalance,

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Internet of Things Intrusion Detection System Using Enhanced Deep Learning-based Feature Selection

Abstract The Internet of Things (IoT) has become an integral part of our daily lives, with the increasing usage of interconnected devices. However, with this increased connectivity comes the risk of security breaches and intrusions. To address this issue, many researchers have proposed intrusion detection systems (IDS) that utilize deep learning techniques. Therefore, this study

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