R. Paper

A Two-Tier Hybrid Intrusion Detection System for IoT Networks

The rapid growth of Internet of Things (IoT) devices has made modern attacks more vulnerable to cyberattacks. Traditional signature-based Intrusion Detection Systems (IDS) are no longer enough to keep up with new and evolving threats. Although machine learning and deep learning have improved detection accuracy, many AI-driven IDS models still face major issues. They often struggle to detect zero-day attacks, produce high false-positive rates and perform poorly with imbalanced datasets. Some models are also too computationally heavy to run efficiently in real time. To address these weaknesses, this research proposes a two-tier hybrid IDS that uses a Random Forest model for quick initial detection and a Neural Network for deeper analysis of suspicious traffic. A confidence threshold of 0.8 is used to decide whether traffic should be accepted or sent for further inspection. Using the NSL-KDD dataset, the system includes preprocessing steps such as binary mapping and structured feature extraction to support both detection stages. Our comparative analysis shows that this hybrid approach can achieve better accuracy, fewer false alarms, and stronger detection of unknown attacks compared to existing Machine Learning / Deep Learning IDS methods. It is more practical for large, diverse IoT environments because it reduces computational load while maintaining strong detection capability. Overall, the proposed architecture provides a balanced and efficient solution that overcomes key limitations of existing IDS models and offers a pathway towards a more robust real-time IoT intrusion detection.

The African Journal of Information Systems The African

Systematic Review of Deterministic and Rule-Based Models for QoS-Aware 5G Network Slice Classification

The development of 5G technology has brought about network slicing as a key architectural advancement, allowing multiple virtual networks to function over a single shared physical infrastructure. Accurate classification of traffic into suitable slices enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and massive Machine-Type Communications (mMTC) is essential to ensure Quality of Service (QoS) and efficient resource utilization. While recent research has largely focused on machine learning and deep learning techniques, challenges such as lack of explainability, high computational cost, and limitations in real-time implementation have renewed attention toward deterministic, rule-based methods. This study conducts a systematic conceptual review of rule-based prediction models for 5G network slicing classification using the PRISMA framework. A comprehensive search of peer-reviewed studies published between 2022 and 2025 was performed across major academic databases. After undergoing identification, screening, eligibility evaluation, and final inclusion processes, a total of 30 relevant studies were analyzed. The results show that rule-based models offer advantages such as interpretability, low-latency decision-making, support for regulatory compliance, and strong suitability for deployment in edge computing environments. Based on these findings, a structured Rule-Based Prediction Model (RBPM) framework is proposed. The study concludes that rule-based approaches remain highly valuable for mission-critical 5G slicing applications and recommends their integration with adaptive techniques to enhance scalability and performance.

An Explainable Multi-Modal Phishing Detection Framework

The multi-modal approach improves accuracy, reduces mistakes, and adapts better to new phishing methods, and performs better than single-method systems and has strong potential for future improvement.

Artificial Intelligence and Big Data in Disaster Risk Management from a Socio-Legal Perspective

To manage complex disaster risks, it is important to develop a response that spans multiple areas of expertise. Bridging the gap between law, social sciences, and natural sciences is an important part of any disaster risk reduction. It helps systems adapt quickly as AI technologies are constantly changing and have significantly impacted where law and the natural environment intersect, influencing legal systems and environmental policies, and how legal and environmental issues can prevent AI from having a greater impact on society and the economy. As part of a participatory assessment of production, with the assistance of legal experts, social and environmentalists, the key principles of responsible data analysis are proposed. These principles focus on security, transparency, fairness, accountability, and the ability to challenge or challenge decisions. This conversation describes how different disciplines can work together to create flexible legal systems that use AI, while drawing knowledge from the environmental and social sciences. The way environmentalists and decision-makers talk about useful and accurate information leads to differences that make the use of artificial intelligence difficult due to legal issues related to the reliability, reliability, and contentiousness of disaster management systems. If social media is useful for disaster risk reduction through AI, it's important to consider legal issues related to who is responsible and how sensitive the information used in disaster management is. Ideas for a fair and responsible process focus on the environment and encourage discussions on social and economic issues related to public participation. AI is also very important in education, as it brings together the next generation of law, social and natural sciences to jointly find solutions that bring together different disciplines in a balanced way. AI tools can be very useful in emergency management, but it's important to use them fairly and responsibly. You need to think about things like possible unfair benefits, clear explanations, and protecting people's personal data. Careful use of AI techniques, considering how AI, laws, and environmental risks interact with each other, helps create equitable and sustainable ways to collect and use data.