A lightweight and provable secure identity-based generalized proxy signcryption (IBGPS) scheme for Industrial Internet of Things (IIoT)

Identity-Based Proxy Signature with Message Recovery over NTRU Lattice

Proxy signature is one of the important primitives of public-key cryptography and plays an essential role in delivering security services in modern communications. However, existing post quantum proxy signature schemes with larger signature sizes might not be fully practical for some resource-constrained devices (e.g., Internet of Things devices). A signature scheme with message recovery has the characteristic that part or all of the message is embedded in the signature, which can reduce the size of the signature. In this paper, we present a new identity-based proxy signature scheme over an NTRU lattice with message recovery (IB-PSSMR), which is more efficient than the other existing identity-based proxy signature schemes in terms of the size of the signature and the cost of energy. We prove that our scheme is secure under a Short Integer Solution (SIS) assumption that is as hard as approximating several worst-case lattice problems in the random oracle model. We also discussed some application scenarios of IB-PSSMR in blockchain and Internet of Things (IOT). This paper provides a new idea for the design of lattice signature schemes in low resource constrained environments.

Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach

Effective software cost estimation significantly contributes to decision-making. The rising trend of using nature-inspired meta-heuristic algorithms has been seen in software cost estimation problems. The constructive cost model (COCOMO) method is a well-known regression-based algorithmic technique for estimating software costs. The limitation of the COCOMO models is that the values of these coefficients are constant for similar kinds of projects whereas, in reality, these parameters vary from one organization to another organization. Therefore, for accurate estimation, it is necessary to fine-tune the coefficients. The research community is now examining deep learning (DL) as a forward-looking solution to improve cost estimation. Although deep learning architectures provide some improvements over existing flat technologies, they also have some shortcomings, such as large training delays, over-fitting, and under-fitting. Deep learning models usually require fine-tuning to a large number of parameters. The meta-heuristic algorithm supports finding a good optimal solution at a reasonable computational cost. Additionally, heuristic approaches allow for the location of an optimum solution. So, it can be used with deep neural networks to minimize training delays. The hybrid of ant colony optimization with BAT (HACO-BA) algorithm is a hybrid optimization technique that combines the most common global optimum search technique for ant colonies (ACO) in association with one of the newest search techniques called the BAT algorithm (BA). This technology supports the solution of multivariable problems and has been applied to the optimization of a large number of engineering problems. This work will perform a two-fold assessment of algorithms: (i) comparing the efficacy of ACO, BA, and HACO-BA in optimizing COCOMO II coefficients; and (ii) using HACO-BA algorithms to optimize and improve the deep learning training process. The experimental results show that the hybrid HACO-BA performs better as compared to ACO and BA for tuning COCOMO II. HACO-BA also performs better in the optimization of DNN in terms of execution time and accuracy. The process is executed upto 100 epochs, and the accuracy achieved by the proposed DNN approach is almost 98% while NN achieved accuracy of up to 85% on the same datasets.

Logistic Management in the Supply Chain Market Using Bio-Inspired Models With IoT Assistance

The internet of things (IoT) is a modern generation of internet-associated embedded information and communication technology in an online environment to incorporate logistics and supply chain processes seamlessly. Automation in inventory monitoring, product control, storage, customer relationships, fleet tracking, etc. is a common issue faced by firms suggesting alternatives to the various problems. In this study, IoT-assisted bio-inspired framework (IoT-BIF) has been proposed for effective logistics management and supply chain processes. IoT with bio-inspired model sensors can track products via different supply chain units to address under-stocking and over-stocking issues. This modern technology allows the connection of numerous objects by gathering real-time data and sharing it; the resulting data can help automated decision-making in industries. The experimental results show that the proposed IoT-BIF method reduces the cost, memory utilization, average running time compared to other popular methods.

A Comprehensive Survey on Signcryption Security Mechanisms in Wireless Body Area Networks

WBANs (Wireless Body Area Networks) are frequently depicted as a paradigm shift in healthcare from traditional to modern E-Healthcare. The vitals of the patient signs by the sensors are highly sensitive, secret, and vulnerable to numerous adversarial attacks. Since WBANs is a real-world application of the healthcare system, it’s vital to ensure that the data acquired by the WBANs sensors is secure and not accessible to unauthorized parties or security hazards. As a result, effective signcryption security solutions are required for the WBANs’ success and widespread use. Over the last two decades, researchers have proposed a slew of signcryption security solutions to achieve this goal. The lack of a clear and unified study in terms of signcryption solutions can offer a bird’s eye view of WBANs. Based on the most recent signcryption papers, we analyzed WBAN’s communication architecture, security requirements, and the primary problems in WBANs to meet the aforementioned objectives. This survey also includes the most up to date signcryption security techniques in WBANs environments. By identifying and comparing all available signcryption techniques in the WBANs sector, the study will aid the academic community in understanding security problems and causes. The goal of this survey is to provide a comparative review of the existing signcryption security solutions and to analyze the previously indicated solution given for WBANs. A multi-criteria decision-making approach is used for a comparative examination of the existing signcryption solutions. Furthermore, the survey also highlights some of the public research issues that researchers must face to develop the security features of WBANs.