An Overview of Hacking as a Service (HaaS) in Africa
Transforming VLSI Design with AI: Pioneering the Future of Chip Technology
A Wristwatch-Based Real-Time Monitoring,Tracking and Emergency Response System for Fishermen: By Integrating Sensor Technologies and Dual Signal Communications
Robust Phishing URL Detection using Deep Learning with hybrid model
Etendering-System-using-Blockchain
This study presents a comprehensive analysis of the landscape of Hacking as a Service (HaaS) in Africa, examining its emergence, operational mechanisms, and impact. HaaS is a burgeoning phenomenon in the cybercrime ecosystem that involves the provision of hacking tools, expertise, and services through online platforms and underground markets. This study explores the socioeconomic factors driving the demand for HaaS in Africa, including connectivity, economic inequality, and inadequate cybersecurity infrastructure. Additionally, it investigates the role of HaaS in facilitating various cyber threats, such as data breaches, ransomware attacks, and identity theft. This study highlights the challenges faced by law enforcement agencies and cybersecurity professionals by elucidating the modus operandi of HaaS operators and their evasion strategies. This underscores the necessity for collaborative efforts among policymakers, industry stakeholders, and civil society to develop robust strategies for mitigating HaaS-related risks and enhancing cybersecurity resilience in Africa.
The use of Artificial Intelligence (AI) in VLSI design has greatly improved the design process. AI techniques like machine learning, deep learning, and neural networks can be applied to various tasks such as optimisation, placement, routing, and verification. The implementation of these techniques can enhance the efficiency and accuracy of products while reducing the amount of manual labour required and the design time. AI tools can also identify faults and suggest solutions effectively. The major benefit of AI is its ability to analyse vast amounts of data. Machine Learning algorithms are used for this purpose. Different AI techniques are used for processes like logic design, logic synthesis, physical design and fabrication. This paper examines the challenges of the VLSI design process and the impact of AI on it. We will explore the AI/ML techniques and tools used in VLSI design, as well as the evolution of AI in this field. Additionally, we will discuss the challenges faced in integrating AI into VLSI design.
Fishermen face significant risks at sea without effective safety measures, including harsh conditions, unpredictable weather, and health emergencies. This system uses RFID technology integrated into a wristwatch to offer real-time monitoring and support, enhancing fishermen's safety and well-being. By addressing these challenges, the system aims to reduce the risks associated with their perilous occupation. Method: This safety system employs GSM communication along with various environmental and health monitoring sensors to keep track of the fisherman's environment and health conditions. The RFID wristwatch is equipped with sensors that continuously monitor conditions such as temperature, humidity, water quality, heart rate, body temperature, and consciousness based on body movement. Data is transmitted via GSM to a central monitoring station, allowing for timely interventions and alerts when anomalies are detected. An intelligent switching mechanism optimizes communication by automatically switching from RF signals to acoustic signals depending on whether the fisherman is on the surface or underwater. Acoustic signals travel longer distances without disturbance underwater but are easily affected by objects in the air, while RF signals travel easily in the air but fade underwater due to water constituents. This switching ensures robust connectivity, with signals reaching the central station through a buoyant relay unit regardless of the fisherman's location. Results: The system facilitates real-time monitoring of environmental conditions and vital signs, enabling timely interventions and alerts. The intelligent switching mechanism ensures that communication is effectively maintained, allowing signals to reach the central monitoring station in various environments, thereby safeguarding the fisherman's safety. Conclusion: This comprehensive safety system, with its robust connectivity and AI-powered assistance module, significantly enhances the safety and survival chances of fishermen. By continuously monitoring their health and surroundings, advanced technological solutions ensure the well-being of fishermen both at sea and underwater.
Phishing is a major cybersecurity threat affecting individuals and organizations worldwide. Attackers use malicious URLs to trick users and steal sensitive information like login credentials, financial details, etc. Traditional detecting systems such as blacklist and heuristic-based methods were struggling to keep up with the rapid evolution of phishing techniques. This study presents a deep learning-based approach for phishing URL detection, with multiple deep learning architectures which includes an Artificial neural network (ANN). Conventional neural network (CNN), recurrent neural network (RNN), and hybrid models like CNN+ANN, CNN+RNN, and CNN+ANN+RNN. This model were trained and evaluated using a dataset, which consisting of legitimate and phishing URL features, which are represented as 0 and 1 for the classification. The model's performances were assessed using key metrics such as accuracy, precision, recall, F1-score. The results demonstrated that the hybrid model gives good accuracy, compared to individual deep learning models, achieving higher accuracy and robustness in detecting phishing attempts. The study highlights that the effectiveness of deep learning techniques in detecting phishing threats and continuous model improvement to prevent emerging attack strategies.
Blockchain technology is set to revolutionize e-tendering by enhancing security, transparency, and cost-effectiveness. Traditional tendering methods, often reliant on paper-based processes or centralized digital systems, are prone to fraud, fake documentation, lack of transparency, and bureaucratic delays. Blockchain-based e-tendering addresses these challenges by eliminating intermediaries, reducing corruption, and fostering trust in the bidding process. By leveraging Distributed Ledger Technology (DLT) and smart contracts, blockchain ensures that all transactions are immutably recorded, tamper-proof, and verifiable. This technology mitigates risks associated with data manipulation, unauthorized alterations, and biased decision-making. Automated procurement through smart contracts streamlines workflows, minimizes manual intervention, reduces operational costs, and expedites decision-making. This research explores the fundamentals, benefits, and challenges of blockchain applications in e- tendering, analysing various consensus mechanisms, cryptographic security models, and interoperability issues. By utilizing blockchain- powered e-tendering solutions, organizations can cut costs, minimize fraudulent risks, and ensure a transparent and fair bidding process. The study serves as a foundation for future research on innovative procurement models driven by blockchain and provides insights for government agencies, enterprises, and technology innovators seeking to modernize their procurement systems
