169 research outputs found

    Lightweight identity based online/offline signature scheme for wireless sensor networks

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    Data security is one of the issues during data exchange between two sensor nodes in wireless sensor networks (WSN). While information flows across naturally exposed communication channels, cybercriminals may access sensitive information. Multiple traditional reliable encryption methods like RSA encryption-decryption and Diffie–Hellman key exchange face a crisis of computational resources due to limited storage, low computational ability, and insufficient power in lightweight WSNs. The complexity of these security mechanisms reduces the network lifespan, and an online/offline strategy is one way to overcome this problem. This study proposed an improved identity-based online/offline signature scheme using Elliptic Curve Cryptography (ECC) encryption. The lightweight calculations were conducted during the online phase, and in the offline phase, the encryption, point multiplication, and other heavy measures were pre-processed using powerful devices. The proposed scheme uniquely combined the Inverse Collusion Attack Algorithm (CAA) with lightweight ECC to generate secure identitybased signatures. The suggested scheme was analyzed for security and success probability under Random Oracle Model (ROM). The analysis concluded that the generated signatures were immune to even the worst Chosen Message Attack. The most important, resource-effective, and extensively used on-demand function was the verification of the signatures. The low-cost verification algorithm of the scheme saved a significant number of valued resources and increased the overall network’s lifespan. The results for encryption/decryption time, computation difficulty, and key generation time for various data sizes showed the proposed solution was ideal for lightweight devices as it accelerated data transmission speed and consumed the least resources. The hybrid method obtained an average of 66.77% less time consumption and up to 12% lower computational cost than previous schemes like the dynamic IDB-ECC two-factor authentication key exchange protocol, lightweight IBE scheme (IDB-Lite), and Korean certification-based signature standard using the ECC. The proposed scheme had a smaller key size and signature size of 160 bits. Overall, the energy consumption was also reduced to 0.53 mJ for 1312 bits of offline storage. The hybrid framework of identity-based signatures, online/offline phases, ECC, CAA, and low-cost algorithms enhances overall performance by having less complexity, time, and memory consumption. Thus, the proposed hybrid scheme is ideally suited for a lightweight WSN

    Secure Equality Test Technique Using Identity-Based Signcryption for Telemedicine Systems

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    For telemedicine, wireless body area network (WBAN) offers enormous benefits where a patient can be remotely monitored without compromising the mobility of remote treatments. With the advent of high capacity and reliable wireless networks, WBANs are used in several remote monitoring systems, limiting the COVID-19 spread. The sensitivity of telemedicine applications mandates confidentiality and privacy requirements. In this article, we propose a secure WBAN-19 telemedicine system to overcome the pervasiveness of contagious deceases utilizing a novel aggregate identity-based signcryption scheme with an equality test feature. We demonstrate a security analysis regarding indistinguishable adaptive chosen-ciphertext attack (IND-CCA2), one-way security against adaptive chosen-ciphertext attack (OW-CCA2), and unforgeability against adaptive chosen-message attack (EUF-CMA) under the random oracle model. The security analysis of the scheme is followed by complexity evaluations where the computation cost and communication overhead are measured. The evaluation demonstrates that the proposed model is efficient and applicable in telemedicine systems with high-performance capacities

    A full privacy-preserving distributed batch-based certificate-less aggregate signature authentication scheme for healthcare wearable wireless medical sensor networks (HWMSNs)

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    The dynamic connectivity and functionality of sensors has revolutionized remote monitoring applications thanks to the combination of IoT and wireless sensor networks (WSNs). Wearable wireless medical sensor nodes allow continuous monitoring by amassing physiological data, which is very useful in healthcare applications. These text data are then sent to doctors via IoT devices so they can make an accurate diagnosis as soon as possible. However, the transmission of medical text data is extremely vulnerable to security and privacy assaults due to the open nature of the underlying communication medium. Therefore, a certificate-less aggregation-based signature system has been proposed as a solution to the issue by using elliptic curve public key cryptography (ECC) which allows for a highly effective technique. The cost of computing has been reduced by 93% due to the incorporation of aggregation technology. The communication cost is 400 bits which is a significant reduction when compared with its counterparts. The results of the security analysis show that the scheme is robust against forging, tampering, and man-in-the-middle attacks. The primary innovation is that the time required for signature verification can be reduced by using point addition and aggregation. In addition, it does away with the reliance on a centralized medical server in order to do verification. By taking a distributed approach, it is able to fully preserve user privacy, proving its superiority

    Prospectiva de seguridad de las redes de sensores inalámbricos

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    En las Redes de Sensores Inalámbricos (WSN), los nodos son vulnerables a los ataques de seguridad porque están instalados en un entorno difícil, con energía y memoria limitadas, baja capacidad de procesamiento y transmisión de difusión media; por lo tanto, identificar las amenazas, los retos y las soluciones de seguridad y privacidad es un tema candente hoy en día. En este artículo se analizan los trabajos de investigación que se han realizado sobre los mecanismos de seguridad para la protección de las WSN frente a amenazas y ataques, así como las tendencias que surgen en otros países junto con futuras líneas de investigación. Desde el punto de vista metodológico, este análisis se muestra a través de la visualización y estudio de trabajos indexados en bases de datos como IEEE, ACM, Scopus y Springer, con un rango de 7 años como ventana de observación, desde 2013 hasta 2019. Se obtuvieron un total de 4.728 publicaciones, con un alto índice de colaboración entre China e India. La investigación planteó desarrollos, como avances en los principios de seguridad y mecanismos de defensa, que han llevado al diseño de contramedidas en la detección de intrusiones. Por último, los resultados muestran el interés de la comunidad científica y empresarial por el uso de la inteligencia artificial y el aprendizaje automático (ML) para optimizar las medidas de rendimiento.In Wireless Sensor Networks (WSN), nodes are vulnerable to security attacks because they are installed in a harsh environment with limited power and memory, low processing power, and medium broadcast transmission. Therefore, identifying threats, challenges, and solutions of security and privacy is a talking topic today. This article analyzes the research work that has been carried out on the security mechanisms for the protection of WSN against threats and attacks, as well as the trends that emerge in other countries combined with future research lines. From the methodological point of view, this analysis is shown through the visualization and study of works indexed in databases such as IEEE, ACM, Scopus, and Springer, with a range of 7 years as an observation window, from 2013 to 2019. A total of 4,728 publications were obtained, with a high rate of collaboration between China and India. The research raised developments, such as advances in security principles and defense mechanisms, which have led to the design of countermeasures in intrusion detection. Finally, the results show the interest of the scientific and business community in the use of artificial intelligence and machine learning (ML) to optimize performance measurements

    Aggregatable Certificateless Designated Verifier Signature

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    In recent years, the Internet of Things (IoT) devices have become increasingly deployed in many industries and generated a large amount of data that needs to be processed in a timely and efficient manner. Using aggregate signatures, it provides a secure and efficient way to handle large numbers of digital signatures with the same message. Recently, the privacy issue has been concerned about the topic of data sharing on the cloud. To provide the integrity, authenticity, authority, and privacy on the data sharing in the cloud storage, the notion of an aggregatable certificateless designated verifier signature scheme (ACLDVS) was proposed. ACLDVS also is a perfect tool to enable efficient privacy-preserving authentication systems for IoT and or the vehicular ad hoc networks (VANET). Our concrete scheme was proved to be secured underling of the Computational Diffie-Hellman assumption. Compared to other related schemes, our scheme is efficient, and the signature size is considerably short

    APEX2S: A Two-Layer Machine Learning Model for Discovery of host-pathogen protein-protein Interactions on Cloud-based Multiomics Data

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    Presented by the avalanche of biological interactions data, computational biology is now facing greater challenges on big data analysis and solicits more studies to mine and integrate cloud-based multiomics data, especially when the data are related to infectious diseases. Meanwhile, machine learning techniques have recently succeeded in different computational biology tasks. In this article, we have calibrated the focus for host-pathogen protein-protein interactions study, aiming to apply the machine learning techniques for learning the interactions data and making predictions. A comprehensive and practical workflow to harness different cloud-based multiomics data is discussed. In particular, a novel two-layer machine learning model, namely APEX2S, is proposed for discovery of the protein-protein interactions data. The results show that our model can better learn and predict from the accumulated host-pathogen protein-protein interactions

    Reliable Bidirectional Data Transfer Approach for the Internet of Secured Medical Things Using ZigBee Wireless Network

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    [EN] Nowadays, the Internet of Things (IoT) performs robust services for real-time applications in monitoring communication systems and generating meaningful information. The ZigBee devices offer low latency and manageable costs for wireless communication and support the process of physical data collection. Some biosensing systems comprise IoT-based ZigBee devices to monitor patient healthcare attributes and alert healthcare professionals for needed action. However, most of them still face unstable and frequent data interruption issues due to transmission service intrusions. Moreover, the medical data is publicly available using cloud services, and communicated through the smart devices to specialists for evaluation and disease diagnosis. Therefore, the applicable security analysis is another key factor for any medical system. This work proposed an approach for reliable network supervision with the internet of secured medical things using ZigBee networks for a smart healthcare system (RNM-SC). It aims to improve data systems with manageable congestion through load-balanced devices. Moreover, it also increases security performance in the presence of anomalies and offers data routing using the bidirectional heuristics technique. In addition, it deals with more realistic algorithm to associate only authorized devices and avoid the chances of compromising data. In the end, the communication between cloud and network applications is also protected from hostile actions, and only certified end-users can access the data. The proposed approach was tested and analyzed in Network Simulator (NS-3), and, compared to existing solutions, demonstrated significant and reliable performance improvements in terms of network throughput by 12%, energy consumption by 17%, packet drop ratio by 37%, end-to-end delay by 18%, routing complexity by 37%, and tampered packets by 37%.This research is supported by Artificial Intelligence & Data Analytics Lab (AIDA) CCIS Prince Sultan University, Riyadh, Saudi Arabia. Authors are thankful for the support.Rehman, A.; Haseeb, K.; Fati, SM.; Lloret, J.; Peñalver Herrero, ML. (2021). Reliable Bidirectional Data Transfer Approach for the Internet of Secured Medical Things Using ZigBee Wireless Network. Applied Sciences. 11(21):1-16. https://doi.org/10.3390/app11219947S116112

    Security analysis of two lightweight certificateless signature schemes

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    Certificateless cryptography can be considered as an intermediate solution to overcome the issues in traditional public key infrastructure (PKI) and identity-based public key cryptography (ID-PKC). There exist a vast number of certificateless signature (CLS) schemes in the literature; however, most of them are not efficient enough to be utilized in limited resources environments such as Internet of things (IoT) or Healthcare Wireless Sensor Networks (HWSN). Recently, two lightweight CLS schemes have been proposed by Karati et al. and Kumar et al. to be employed in IoT and HWSNs, respectively. While both schemes are claimed to be existentially unforgeable, in this paper, we show that both these signatures can easily be forged. More specifically, it is shown that 1) in Karati et al.\u27s scheme, a type 1 adversary, considered in certificateless cryptography, can generate a valid partial private key corresponding to any user of its choice and as a consequence, it can forge any users\u27 signature on any message of its choice, and 2) in Kumar et al.\u27s scheme, both types of adversaries which are considered in certificateless cryptography are able to forge any signer\u27s signature on an arbitrary message
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