The Internet of Things (IoT) is one of the promising technologies of the future. It offers many attractive features that we depend on nowadays with less effort and faster in real-time. However, it is still vulnerable to various threats and attacks due to the obstacles of its heterogeneous ecosystem, adaptive protocols, and self-configurations. In this paper, three different 6LoWPAN attacks are implemented in the IoT via Contiki OS to generate the proposed dataset that reflects the 6LoWPAN features in IoT. For analyzed attacks, six scenarios have been implemented. Three of these are free of malicious nodes, and the others scenarios include malicious nodes. The typical scenarios are a benchmark for the malicious scenarios for comparison, extraction, and exploration of the features that are affected by attackers. These features are used as criteria input to train and test our proposed hybrid Intrusion Detection and Prevention System (IDPS) to detect and prevent 6LoWPAN attacks in the IoT ecosystem. The proposed hybrid IDPS has been trained and tested with improved accuracy on both KoU-6LoWPAN-IoT and Edge IIoT datasets. In the proposed hybrid IDPS for the detention phase, the Artificial Neural Network (ANN) classifier achieved the highest accuracy among the models in both the 2-class and N-class. Before the accuracy improved in our proposed dataset with the 4-class and 2-class mode, the ANN classifier achieved 95.65% and 99.95%, respectively, while after the accuracy optimization reached 99.84% and 99.97%, respectively. For the Edge IIoT dataset, before the accuracy improved with the 15-class and 2-class modes, the ANN classifier achieved 95.14% and 99.86%, respectively, while after the accuracy optimized up to 97.64% and 99.94%, respectively. Also, the decision tree-based models achieved lightweight models due to their lower computational complexity, so these have an appropriate edge computing deployment. Whereas other ML models reach heavyweight models and are required more computational complexity, these models have an appropriate deployment in cloud or fog computing in IoT networks.

Applied results of scientific analysis should be the key focus of modern security research. A comparative analysis of research results obtained using different methods, as an applied task, forms a broader basis for interpreting the results and substantiating the conclusions. A social survey and expert opinion research were conducted to implement the general concept of strategic analysis of cybersecurity in Ukraine. Using the method based on determining the average value in a certain set of estimates, as well as the method based on the theory of fuzzy sets, the risks of spreading certain cyber threats in Ukraine were assessed. The results were compared. Although the use of different measurement methods led to some differences in quantitative risk indicators, the comparative analysis of the ratio of the level of different cyber threats did not change significantly. At the same time, the fuzzy set method provided more flexible interpretation of the results to characterize cyber threats in terms of their upward or downward trend. In general, the combined approach to cyber threat risk assessment can become an important risk management tool, as it takes advantage of different methods and allows for a deeper understanding of the current situation and the formation of more informed management decisions.

Cloud computing is an emerging concept that makes better use of a large number of distributed resources. The most significant issue that affects the cloud computing environment is resource provisioning. Better performance in the shortest amount of time is an important goal in resource provisioning. Create the best solution for dynamically provisioning resources in the shortest time possible. This paper aims to perform resource provisioning with an optimal performance solution in the shortest time. Hybridization of two Meta-heuristics techniques, such as HSMOSA (Hybrid Spider Monkey Optimization with Simulated Annealing), is proposed in resource provisioning for cloud environment. Finds the global and local value using Spider Monkey Optimization's (SMO) social behavior and then utilizes Simulated Annealing (SA) to search around the global value in each iteration. As a result, the proposed approach aids in enhancing their chances of improving their position. The CloudSimPlus Simulator is used to test the proposed approach. The fitness value, execution time, throughput, mean, and standard deviation of the proposed method were calculated over various tasks and execution iterations. These performance metrics are compared with the PSO-SA algorithm. Simulation results validate the better working of the proposed HSMOSA algorithm with minimum time compared to the PSO-SA algorithm.

Cloud Computing has been the most popular approach of computing due to faster access to folders and files at a low cost. Hence, many organizations are shifting the old long database folders and files over the cloud which may be text, audio, video or in the other formats. Due to large size of the database with multiple storages of folders and files over the cloud, there may be chances of duplicate access of the database folders and files which may cause the loss of time of execution or accessing the database files. In the present work, a technique is developed to remove duplicate files in the form of .txt, .doc, .jpg, .pdf as well as duplicate folders after applying a well-known ElGamal algorithm later on converted as fuzzy ElGamal technique, for faster retrieval of files in a very secure manner. For this purpose, Unified Modelling Language (UML) model is developed which has been implemented through Python programming language. The computed results towards the model’s efficiency have been depicted through tables and graphs, on a large database in the form of folders and files of Indian railway reservation system. The present work is significant for the large organizations and also useful for the users working over the cloud for faster accessing of the folders and files.

For the implementation of error-correcting codes, cryptographic algorithms, and the construction of homomorphic methods for privacy-preserving, there is a need for methods of performing operations on elements GF(2m) that have low computational complexity. This paper analyzes the existing methods of performing operations on the elements GF(2m) and proposes a new method based on the use of a sparse table of elements of this field. The object of research is the processes of operations in information security systems. The subject of research is methods and algorithms for performing operations on elements GF(2m). The purpose of this research is to develop and improve methods and algorithms for performing operations on elements GF(2m) to reduce their computational complexity. Empirical methods and methods of mathematical and software modeling are used in the research. Existing and proposed algorithms are implemented using the C# programming language in the Visual Studio 2015 development environment. Experimental research of existing and developed algorithms was carried out according to the proposed method, which allows to level the influence of additional parameters on the results of the research. The conducted research on methods for performing operations on the elements GF(2m) shows the expediency of using a sparse table of field elements. This approach makes it possible to reduce the amount of RAM required for the software and hardware implementation of the developed method compared to the classical tabular method, which requires storage of a full table of correspondence of the polynomial and index representation of the field elements. In addition, the proposed method gives an increase in speed of more than 4 times for the operations of calculating the multiplicative inverse element and exponentiation. As a result, the proposed method allows to reduce the computational complexity of error-correcting codes, cryptographic algorithms, and the homomorphic methods for privacy-preserving.

The reduction of energy consumption in elastic optical networks is of major interest to the research community. As a result, several methods for solving this problem in combination with existing classical problems have been proposed. Elastic optical networks are subject to disturbance phenomena that degrade their quality and performance. To optimize resources, operators must recalculate new routes and plan the displacement of established connections towards these new routes to cope with these phenomena, it’s the reconfiguration. The problem addressed in this article is to reconfigure a set of unicast protected connections without interruption to a new routing calculated during the process. Knowing that the use of backup paths solves the interruption problem, but has an impact on the overall energy consumption, the goal is to find a good compromise between the two sub-problems when switching from old routes to new ones. To the best of our knowledge, there is no work on reconfiguration that uses energy-aware backup paths. In this work, we proposed an energy-aware EERA_EON rerouting algorithm using the backup paths. Simulations have shown the performance of this approach in terms of energy consumption compared to the work of our predecessors. Subsequently, we proposed a classical BRA_EON rerouting algorithm in elastic optical networks. Simulation results show that we perform BRA_EON in terms of the number of steps.

Containers have newly emerged as a potential way to encapsulate and execute programs. In contrast to virtual machines, each container does not have its own kernel and instead shares the host systems. Containers on the other hand are more lightweight, need fewer data to be sent between network nodes and boot up faster than VM. This makes containers a feasible choice, particularly for hosting and extending the services across the fog computing architecture. The major purpose of this paper is to describe the Distributed Intelligent Scheduling based Lightweight Container algorithm (DIS-LC), which is a revolutionary way for container to fog-services integration and resource optimization. In this proposed algorithm is compared to the least connection algorithm, round-robin algorithm and Ant Colony Optimization-based Light Weight Container (ACO-LWC). Operating cost and traffic cost are used to validate the suggested algorithm. Fog node running costs are divided into two categories: CPU and memory. When compared to current algorithms, quantitative research demonstrates that the proposed DIS-LC scheme gets the greatest performance in terms of all metrics. This demonstrate the algorithm is efficient. Finally, the performance of containerized services and resource management systems is evaluated using the iFogSim simulator.

This article considers the method of analyze potentially vulnerable places during development of topology for fault-tolerant systems based on using betweenness coefficient. Parameters of different topological organizations using De Bruijn code transformation are observed. This method, assessing the risk for possible faults, is proposed for other topological organizations that are analyzed for their fault tolerance and to predict the consequences of simultaneous faults on more significant fragments of this topology.

In general, Vehicular Ad hoc Networks (VANETs) are permitting the communication between one vehicle with neighboring vehicles, infrastructure, and Road-Side Unit (RSU). In this, vehicle platoon is commonly known as the vehicle driving pattern it categorizes the batching of the vehicle in the on the trot fashion. It has been reviewed as an effective resolution to mitigate the reduction in traffic blockage and to widen the opulence of the travel. However, the malicious activities of any unauthorized person in VANET are increased the damage to authorized vehicles. In this manuscript, the Identity based Conditional Privacy-Preserving Authentication (ID-CPPA) signature scheme is proposed to detect the malignant command vehicle very efficiently by the consumer vehicle. In this, the proposed ID-CPPA method uses one-way hash functions for improving the efficiency of Road-Side Unit (RSU) signing and verification of a messages. In order to provide better concealment to the vehicle, Phase Truncated Fourier Transform based asymmetric encryption algorithm (PTFT-AE) is proposed. Thus, the proposed ID-CPPA-PTFT-AE approach has achieved 28.96%, 37.58%, 31.36% higher security rate and 25.8%, 37.9%, 42.6% lower delay than the existing MPDC-LPNS, PPSR-GS, and WCAA-TST methods respectively.

Mobile ad hoc network (MANET) routing is a generous tactic used for allocating packets to the base station (BS). During the operations of routing, occurrence of malicious node embellishes the mobile ad hoc network operations. For that reason, a trusted distributed routing protocol is obliged that maintains the routing buttressing and the proficiency of mobile ad hoc network. To overcome these challenging issues, Auto-Metric Graph Neural Network based Blockchain technology is proposed in this manuscript for Secured Dynamic Optimal Routing in MANET (BC-SDOR-MANET-AGNN). The proposed approach is simulated in NS-2 tool. The proposed BC-SDOR-MANET-AGNN approach attains 76.26%, 65.57%, 42.9% minimal delay during 25% malicious routing environment, 73.06%, 63.82%, 38.84% less delay during 50% malicious routing environment when analyzed to the existing models, like enhanced hybrid secure multipath routing protocol for MANET (BC-SDOR-MANET-GAHC), an improved ad hoc on-demand distance vector routing security approach based on BC technology in MANET (BC-SDOR-MANET-AODV-MQS) and block chain-based better approach for the mobile ad-hoc networking protocol using ensemble algorithm (BC-SDOR-MANET-E-BATMAN) methods.