Babatunde Ojetunde

Recent advances in quantum computing have prompted urgent consideration of the migration of classical cryptographic systems to post-quantum alternatives. However, it is impossible to fully understand the impact that migrating to current Post-Quantum Cryptography (PQC) algorithms will have on various applications without the actual implementation of quantum-resistant cryptography. On the other hand, PQC algorithms come with complexity and long processing times, which may impact the quality of service (QoS) of many applications. Therefore, PQC-based protocols with practical implementations across various applications are essential. This paper introduces a new framework for PQC standalone and PQC–AES (Advanced Encryption Standard) hybrid public-key encryption (PKE) protocols. Building on prior results, we focus on securing applications such as file transfer, video streaming, and chat-based communication using enhanced PQC-based protocols. The extended PQC-based protocols use a sequence number-based mechanism to effectively counter replay and man-in-the-middle attacks and mitigate standard cybersecurity attack vectors. Experimental evaluations examined encryption/decryption speeds, throughput, and processing overhead for the standalone PQC and the PQC–AES hybrid schemes, benchmarking them against traditional AES-256 in an existing client–server environment. The results demonstrate that the new approaches achieve a significant balance between security and system performance compared to conventional deployments. Furthermore, a comprehensive security analysis confirms the robustness and effectiveness of the proposed PQC-based protocols across diverse attack scenarios. Notably, the PQC–AES hybrid protocol demonstrates greater efficiency for applications handling larger data volumes (e.g., 10–100 KB) with reduced latency, underscoring the practical necessity of carefully balancing security and operational efficiency in the post-quantum migration process.

Recent advances in quantum computing have prompted urgent consideration for the migration of classical cryptographic systems to post-quantum alternatives. However, it is impossible to fully understand the impact that migrating to current Post-Quantum Cryptography (PQC) algorithms will have on various applications without actual implementation of quantum-resistant cryptography. On the other hand, PQC algorithms come with complexity and long processing times, which may impact the Quality of Service (QoS) of many applications. Therefore, PQC-based protocols with practical implementation across various applications are essential. This paper introduces a new framework for PQC standalone and PQC-AES (Advanced Encryption Standard) hybrid Public Key Encryption (PKE) protocols. Building on prior results, we focus on securing applications such as file transfer, video streaming, and chat-based communication using enhanced PQC-based protocols. The extended PQC-based protocols use a sequence number-based mechanism to effectively counter replay and man-in-the-middle attacks and mitigate standard cybersecurity attack vectors. Experimental evaluations examined encryption/decryption speeds, throughput, and processing overhead for these standalone PQC and PQC-AES hybrid schemes, benchmarking them against traditional AES-256 in an existing client-server environment. The results demonstrate that the new approaches achieve a significant balance between security and system performance compared to conventional deployments. Furthermore, the comprehensive security analysis confirms the robustness and effectiveness of the proposed PQC-based protocols across diverse attack scenarios. Notably, the PQC-AES hybrid protocol demonstrates greater efficiency for applications handling larger data volumes (e.g., 10–100 KB) with reduced latency, underscoring the practical necessity of carefully balancing security and operational efficiency in the post-quantum migration process.

The rapid growth in the IoT network comes with a huge security threat. Network scanning is considered necessary to identify vulnerable IoT devices connected to IP networks. However, most existing network scanning tools or system do not consider the burden of scan packet traffic on the network, especially in the IoT network where resources are limited. It is necessary to know the status of the communication environment and the reason why network scanning failed. Therefore, this paper proposes a multimodel-based approach which can be utilized to estimate the cause of failure/delay of network scanning over wireless networks where a scan packet or its response may sometimes be dropped or delayed. Specifically, the factors that cause network scanning failure/delay were identified and categorized. Then, using a machine learning algorithm, we introduced a multimodel linear discriminant analysis (MM-LDA) to estimate the cause of scan failure/delay based on the results of network scanning. In addition, a one-to-many model and a training data filtering technique were adopted to ensure that the estimation error was drastically reduced. The goal of our proposed method was to correctly estimate the causes of scan failure/delay in IP-connected devices. The performance of the proposed method was evaluated using computer simulation assuming a cellular (LTE) network as the targeted IoT wireless network and using LTE-connected devices as the targeted IoT devices. The proposed MM-LDA correctly estimates the cause of failure/delay of the network scan at an average probability of 98% in various scenarios. In comparison to other conventional machine learning classifiers, the proposed MM-LDA outperforms various classification methods in the estimation of the cause of scan failure/delay.

Unmanned aerial vehicles (UAVs) are commonly used in disaster recovery efforts to confirm the extent of the damage caused by a large-scale disaster and to ensure that adequate information about the affected area is gathered. UAV usage in a disaster situation is not limited to information gathering, but its functions further extend to network communication functions such as a communication relay, mobile base station, etc. Conversely, using UAVs as communication relays or base stations (e.g., as a temporary mobile base station) is subject to some limitations such as bad weather and limited energy, which affect the timely collection and delivery of information to and from a disaster area. Therefore, an approach to improve the information collection and delivery in a disaster area while ensuring urgent network restoration is necessary. In this paper, we introduce a practical approach to deploying a drone-based message ferry to improve the timely collection and delivery of information in a disaster situation. Specifically, a locally accessible cloud system (LACS), which we previously developed for urgent local network restoration in a disaster affected area, is attached to a drone to form a message ferry. In order to improve the information collection and delivery, the message ferry is deployed from the local headquarters (i.e., local government office or disaster response coordination center) to carry information to and from a standalone LACS installed at each disaster shelter. We measure the achievable data rate of inter-LACS data transfer (i.e., message ferry and a standalone LACS) through experiment. Thereafter, we estimate and confirm the effective transmission speed to transfer data from the local headquarters to the disaster shelters using average achievable data rate obtained from the experiment in order to extend the LACS services and functions. Our experimental results and analysis suggest that the proposed message ferry is more advantageous to transfer a large amount of data (e.g., 300 MB) at a high speed in the hundred meter to ten kilometer range.

This letter proposes a practical transmission datarate adaptation (TDA) scheme using Q-learning applicable to IEEE 802.11ax wireless local area networks. In the proposed scheme, each basic service set (BSS) selects an appropriate transmission datarate according to the buffer statuses of adjacent BSSs which are periodically collected and the transmission results of DATA frames in the BSS. Then, the BSS conducts underlay transmissions based on the framework of spatial reuse defined in IEEE 802.11ax. The performance of the proposed scheme is evaluated through system-level computer simulation assuming downlink full-buffer traffic. It is confirmed that the proposed scheme can achieve higher average area throughput than conventional TDA schemes such as Robust Rate Adaptation Algorithm (RRAA) and adaptive modulation and coding (AMC) based on the received power.

This paper proposes a method of identifying the scan response state based on a similarity measure between scan response preliminary obtained in various network states and an unknown scan response data in order to estimate the cause of network scan failure/delay which is necessary in detecting compromised IoT wireless devices. Existing methods for measuring similarity between two vectors fail to find similarity between two scan response data where the distribution, dimension, or magnitude of such scan response data differs, therefore, a method that can clearly measure the similarity between scan responses data is required. In particular, a similarity measure based on the average absolute deviation with a baseline scheme that can be utilized for easy identification of the network scan response scenario is proposed. Thereafter, the estimation of cause of scan failure/delay is presented. The goal of our proposed method is to detect early the state of the target (scanned) network, in order to accurately estimate the cause of scan failure/delay from the scan response data. The performance of the proposed method is evaluated using computer simulation. The evaluation results show that the proposed similarity measure achieved high similarity ratio of all unknown scan response samples to correct scenario and outperforms other conventional similarity measure methods. Also, it is confirmed that the cause of scan failure/delay could be estimated with over 90% correct estimation accuracy in various scenarios.

Mobile payment system in a disaster area have the potential to provide electronic transactions for people purchasing recovery goods like foodstuffs, clothes, and medicine. Conversely, to enable transactions in a disaster area, current payment systems need communication infrastructures (such as wired networks and cellular networks) which may be ruined during such disasters as large-scale earthquakes and flooding and thus cannot be depended on in a disaster area. In this paper, we introduce a new mobile payment system utilizing infrastructureless mobile adhoc networks to enable transactions that permit users to shop in disaster areas. Specifically, we introduce an endorsement-based mechanism to provide payment guarantees for a customer-to-merchant transaction and a multilevel endorsement (MLE) mechanism with a lightweight scheme based on Bloom filter and Merkle tree to reduce communication overheads. Our mobile payment system achieves secure transaction by adopting various schemes such as location-based mutual monitoring scheme and blind signature, while our newly introduced event chain mechanism prevents double spending attacks. As validated by simulations, the proposed mobile payment system is useful in a disaster area, achieving high transaction completion ratio, 65%-90% for all scenario tested, and is storage-efficient for mobile devices with an overall average of 7 MB merchant message size.