An important task of designing complex computer systems is to ensure high reliability. Many authors investigate this problem and solve it in various ways. Most known methods are based on the use of natural or artificially introduced redundancy. This redundancy can be used passively and/or actively with (or without) restructuring of the computer system. This article explores new technologies for improving fault tolerance through the use of natural and artificially introduced redundancy of the applied number system. We consider a non-positional number system in residual classes and use the following properties: independence, equality, and small capacity of residues that define a non-positional code structure. This allows you to: parallelize arithmetic calculations at the level of decomposition of the remainders of numbers; implement spatial spacing of data elements with the possibility of their subsequent asynchronous independent processing; perform tabular execution of arithmetic operations of the base set and polynomial functions with single-cycle sampling of the result of a modular operation. Using specific examples, we present the calculation and comparative analysis of the reliability of computer systems. The conducted studies have shown that the use of non-positional code structures in the system of residual classes provides high reliability. In addition, with an increase in the bit grid of computing devices, the efficiency of using the system of residual classes increases. Our studies show that in order to increase reliability, it is advisable to reserve small nodes and blocks of a complex system, since the failure rate of individual elements is always less than the failure rate of the entire computer system.

Blockchain technology unarguably has over a decade gained widespread attention owing to its often-tagged disruptive nature and remarkable features of decentralization, immutability and transparency among others. However, the technology comes bundled with challenges. At center-stage of these challenges is privacy-preservation which has massively been researched with diverse solutions proposed geared towards privacy protection for transaction initiators, recipients and transaction data. Dual-key stealth address protocol for IoT (DkSAP-IoT) is one of such solutions aimed at privacy protection for transaction recipients. Induced by the need to reuse locally stored data, the current implementation of DkSAP-IoT is deficient in the realms of data confidentiality, integrity and availability consequently defeating the core essence of the protocol in the event of unauthorized access, disclosure or data tampering emanating from a hack and theft or loss of the device. Data unavailability and other security-related data breaches in effect render the existing protocol inoperable. In this paper, we propose and implement solutions to augment data confidentiality, integrity and availability in DkSAP-IoT in accordance with the tenets of information security using symmetric encryption and data storage leveraging decentralized storage architecture consequently providing data integrity. Experimental results show that our solution provides content confidentiality consequently strengthening privacy owing to the encryption utilized. We make the full code of our solution publicly available on GitHub.

In wireless sensor networks (WSN), sensor nodes are expected to operate autonomously in a human inaccessible and the hostile environment for which the sensor nodes and communication links are therefore, prone to faults and potential malicious attacks. Sensor readings that differ significantly from the usual pattern of sensed data due to faults in sensor nodes, unreliable communication links, and physical and logical malicious attacks are considered as outliers. This paper presents an outlier detection technique based on deep learning namely, generative adversarial networks (GANs) with autoencoder neural network. The two-level architecture proposed for WSN makes the proposed technique robust. The simulation result indicates improvement in detection accuracy compared to existing state-of-the-art techniques applied for WSNs and increase of the network lifetime. Robustness of outlier detection algorithm with respect to channel fault and robustness concerning different types of distribution of faulty communication channel is analyzed.

Social media provides a free space to users to post their information, opinions, feelings, etc. Also, it allows users to easily and simultaneously communicate with each other. As a result, threat detection in social media is critical for ensuring the user’s safety and preventing suspicious activities such as criminal behavior, hate speech, ethnic conflicts and terrorist plots. These suspicious activities have a negative impact on the community’s life and cause tension and social unrest among individuals in both inside and outside of cyberspace. Furthermore, with the recent popularity of social networking sites, the number of discussions containing threats is increasing, causing fear in various parties, whether at the individual or state level. Moreover, these social networking service providers do not have complete control over the content that users post. In this paper, we propose to design a threat detection model on Twitter using a semantic network. To achieve this aim, we designed a threat semantic network, named, ThrNet that will be integrated in our proposed threat detection model called, DetThr. We compared the performance of our model (DetThr) with a set of well-known Machine Learning algorithms. Results show that the DetThr model achieves an accuracy of 76% better than Machine Learning algorithms. It works well with an error rate of forecasting threatening tweet messages as non-threatening (false negatives) is about 29%, while the error rate of forecasting non-threatening tweet messages as threatening (false positives) is about 19%.

Vehicular Ad-hoc Network (VANET) is a growing technology that utilizes moving vehicles as mobile nodes for exchanging essential information between users. Unlike the conventional radio frequency based VANET, the Visible Light Communication (VLC) is used in the VANET to improve the throughput. However, the road safety is considered as a significant issue for users of VANET. Therefore, congestion-aware routing is required to be developed for enhancing road safety, because it creates a collision between the vehicles that causes packet loss. In this paper, the Multi Objective Congestion Metric based Artificial Ecosystem Optimization (MOCMAEO) is proposed to enhance road safety. The MOCMAEO is used along with the Ad hoc On-Demand Distance Vector (AODV) routing protocol for generating the optimal routing path between the source node to the Road Side Unit (RSU). Specifically, the performance of the MOCMAEO is improved using the multi-objective fitness functions such as congestion metric, residual energy, distance, and some hops. The performance of the MOCMAEO is analyzed by means of Packet Delivery Ratio (PDR), throughput, delay, and Normalized Routing Load (NRL). The PSO based geocast routing protocols such as LARgeoOPT, DREAMgeoOPT, and ZRPgeoOPT are used to evaluate the performance of the MOCMAEO method. The PDR of the MOCMAEO method is 99.92 % for 80 nodes, which is high when compared to the existing methods.

The huge availability and prosperity of net technology results in raised on-line media sharing over the cloud platform which has become one of the important resources and tools for development in our societies. So, in the epoch of enormous data great amount of sensitive information and transmission of different media transmitted over the net for communication. And recently, fog computing has captured the world's attention due to their inherent features relevant compared to the cloud domain, But this push to head for many issues related to data security and privacy in fog computing which it's still under studied in their initial juncture. Therefore, in this paper, we will review a security system that relies on encryption as a kind of effective solution to secure image data. We use an approach of using chaotic map plus space curve techniques moreover the confusion and diffusion strategies are carried out utilizing Hilbert curvature and chaotic map such as two-dimensional Henon map (2D-HM) to assert image confusion with pixel level permutation .Also we relied in our system the way of shuffling the image with blocks and use a key for each block, which is chooses randomly to have a high degree of security. The efficiency of the proposed technique has been tested utilizing different investigations like analysis of entropy [7.9993], NPCR [99.6908%] and finally UACI [33.6247%]. Analysis of results revealed that the proposed system of image encryption technique has favorable effects, and can achieve a good results moreover it fights different attacks and by comparing with another techniques denote that our proposed fulfills high security level with high quality.