Detecting End-Point (EP) Man-In-The-Middle (MITM) Attack based on ARP Analysis: A Machine Learning Approach

End-Point (EP) Man-In-The-Middle (MITM) attack is a well-known threat in computer security. This attack targets the flow of information between endpoints. An attacker is able to eavesdrop on the communication between two targets and can either perform active or passive monitoring; this affects the confidentiality and integrity of the data flow. Several techniques have been developed by researchers to address this kind of attack. With the current emergence of machine learning (ML) models, we explore the possibility of applying ML in EP MITM detection. Our detection technique is based on Address Resolution Protocol (ARP) analysis. The technique combines signal processing and machine learning in detecting EP MITM attack. We evaluated the accuracy of the proposed technique using linear-based ML classification models. The technique proved itself to be efficient by producing a detection accuracy of 99.72%

Assessing the Feasibility of RF Fingerprinting for Security in Unmanned Aerial Vehicles

The wireless network of consumer drones is particularly vulnerable to remote attacks due to the weak encryption scheme involving the exchange of a Global Unique Identifier (GUID) between transceiver pairs using the binding process, thus exposing the technology to a host of attack vectors such as data spoofing and malicious authentication, among others, leading to security breaches that threaten the prospects of the consumer drone. This study assesses the feasibility of RF fingerprinting as a complementary layer of security devoid of cryptography in the wireless network of unmanned aerial vehicles for enhanced resilience. We evaluate the feature performance of the toy-grade and the universal-grade drone RC transmitters to discern the prospects for device identification in inexpensive, low-end device and the high-end device. Instantaneous amplitude and phase features extracted from the transient phase of time-domain signals acquired off-the-air in the near-field show a high recognition rate in a support vector machine and k-Nearest Neighbour, suggestive of device classification in unmanned aerial vehicle RF hardware, irrespective of built quality

Assessing blockchain and IoT technologies for agricultural food supply chains in Africa : a feasibility analysis

This review paper delves into the global agricultural food supply chains through the lens of African perspectives, examining the role of blockchain and Internet of Things (IoT) technologies in transforming food traceability. It assesses the applicability and efficacy of these innovations in addressing critical issues such as food fraud, contamination, and systemic inefficiencies from an African viewpoint. By engaging in an in-depth analysis of relevant studies, this work dissects the technical, economic, legal, and operational facets of employing blockchain and IoT in the agri-food sector. The findings illuminate the transformative potential these technologies hold for enhancing food safety and transparency across supply chains. However, the review also brings to light significant hurdles related to scalability, cost-effectiveness, and regulatory frameworks that must be surmounted. Advocating for a context-sensitive application of blockchain and IoT, the paper highlights the importance of adapting these technologies to fit the diverse socio-economic and infrastructural realities prevalent in African countries. Offering valuable insights to stakeholders in agricultural technology and food safety, this comprehensive review outlines a roadmap for future research and strategic implementation efforts aimed at leveraging blockchain and IoT for the development of secure, sustainable food systems.The Future Africa Research Leadership Fellowship (FAR-LeaF) program with support from the Carnegie Corporation of New York. This paper was written as part of the Development of an Open Toolbox for Safe Food Monitoring project, which is financed by the FAR-LeaF Programme.https://www.cell.com/heliyonhj2024Future AfricaSDG-02:Zero Hunge

A Mathematical Modelling and Behaviour Simulation of a Smart Grid Cyber-Physical System

The significant contributions of information and communication technology (ICT) and other operational technologies (OTs) or cyber networks have had a tremendous impact on the real-time monitoring, management, and control of the power or energy system facilities. Thus, the integration of these technologies into the energy grid system created a smart, complex, and interdependent system. This system is established and referred to as a smart grid cyber physical power system (SGCPPS). The performances of cyber physical systems are achieved via computation and communication and are imperatively based on a real-time feedback mechanism. In reference to the energy system, monitoring and control of the grid systems is extremely essential in ensuring efficient power supply, quality, reliability, stability and resilience among other determinants. However, their interdependence and integrated nature exposes the grid to disturbances subsequently leading to faults in the grid. Hence, failure to know the grid conditions at a particular period subjugates it to complete system collapse. This paper focused on the development of a mathematical model for a smart gird cyber physical system. Additionally, simulations were performed to study the behaviour of the Smart grid cyber-physical power system (SGCPPS) with regards to monitoring and controlling the physical systems using MATLAB Simulink tool to facilitate system awareness

Multi-Agent Reinforcement Learning Framework in SDN-IoT for Transient Load Detection and Prevention

The fast emergence of IoT devices and its accompanying big and complex data has necessitated a shift from the traditional networking architecture to software-defined networks (SDNs) in recent times. Routing optimization and DDoS protection in the network has become a necessity for mobile network operators in maintaining a good QoS and QoE for customers. Inspired by the recent advancement in Machine Learning and Deep Reinforcement Learning (DRL), we propose a novel MADDPG integrated Multiagent framework in SDN for efficient multipath routing optimization and malicious DDoS traffic detection and prevention in the network. The two MARL agents cooperate within the same environment to accomplish network optimization task within a shorter time. The state, action, and reward of the proposed framework were further modelled mathematically using the Markov Decision Process (MDP) and later integrated into the MADDPG algorithm. We compared the proposed MADDPG-based framework to DDPG for network metrics: delay, jitter, packet loss rate, bandwidth usage, and intrusion detection. The results show a significant improvement in network metrics with the two agents

On Blockchain and IoT Integration Platforms: Current Implementation Challenges and Future Perspectives

Digitization and automation have engulfed every scope and sphere of life. Internet of Things (IoT) has been the main enabler of the revolution. There still exist challenges in IoT that need to be addressed such as the limited address space for the increasing number of devices when using IPv4 and IPv6 as well as key security issues such as vulnerable access control mechanisms. Blockchain is a distributed ledger technology that has immense benefits such as enhanced security and traceability. Thus, blockchain can serve as a good foundation for applications based on transaction and interactions. IoT implementations and applications are by definition distributed. This means blockchain can help to solve most of the security vulnerabilities and traceability concerns of IoTs by using blockchain as a ledger that can keep track of how devices interact, in which state they are and how they transact with other IoT devices. IoT applications have been mainly implemented with technologies such as cloud and fog computing, and AI to help address some of its key challenges. The key implementation challenges and technical choices to consider in making a successful blockchain IoT (BIoT) project are clearly outlined in this paper. The security and privacy aspect of BIoT applications are also analyzed, and several relevant solutions to improve the scalability and throughput of such applications are proposed. The paper also reviews integration schemes and monitoring frameworks for BIoT applications. A hybrid blockchain IoT integration architecture that makes use of containerization is proposed

On cloud-based systems and distributed platforms for smart grid integration: Challenges and prospects for Ghana's Grid Network

Advances in cloud computing and distributed systems are enhancing the advantages that computing delivers to systems that can be integrated with computers due to their multitasking ability. This is already the case when discussing Smart Grid (SG). This enhancement can be applied to drive efficiency in energy management systems in electricity generation, transmission and distribution. As electricity has become the de facto main source of energy worldwide, it is to be expected that all nations, including developing nations such as Ghana, seeks to constantly improve their electrical energy capabilities through the integration of computing and network infrastructure. The integration of SG with cloud computing however presents some complexities which need to be addressed to ensure secure implementation of the SG in the cloud. This paper presents important issues which need to be addressed to ensure security in distributed cloud-based energy systems. The paper also discusses how cloud computing and blockchain could be leveraged to improve on Ghana's current inefficient distribution and transmission networks. An architecture for achieving decentralization in Ghana's grid network is subsequently proposed