In recent years, there has been a lot of research interest in analysing chaotic constructions and their associated cryptographic structures. Compared with the essential combination of encryption and signature, the signcryption scheme has a more realistic solution for achieving message confidentiality and authentication simultaneously. However, the security of such schemes is questionable when deployed in modern safety-critical systems, especially as billions of sensitive user information is transmitted over open communication channels. To address this limitation, a lightweight, provably secure certificateless technique that uses Fractional Chaotic Maps (FCM) for group-oriented signcryption (CGST) is presented. The main feature of the CGST-FCM technique is that any group signcrypter may encrypt data/information with the group manager (GM) and have it sent to the verifier. They can verify the legitimacy of the signcrypted information/data using the public conditions of the group, but they cannot link it to the conforming signcrypter. In this scenario, valid signcrypted information/data cannot be produced by the GM or any signcrypter in that category alone. The GM is allowed to reveal the identity of the signcrypter when there is a legal conflict to restrict repudiation of the signature. The CGST-FCM technique is found to be protected from the indistinguishably chosen ciphertext attack (IND-CCA). The computationally difficult problem has been used to build unlinkability, traceability, robust security, and unforgeability. The security investigation of the presented CGST-FCM technique shows commendable consistency and very high efficiency when used in real-time security applications.

This work presents an Elliptic-curve Point Multiplication (ECP) architecture with a focus on low latency and low area for radio-frequency-identification (RFID) applications over GF(2163). To achieve low latency, we have reduced the clock cycles by using: (i) three-shift buffers in the datapath to load Elliptic-curve parameters as well as an initial point, (ii) the identical size of input/output interfaces in all building blocks of the architecture. The low area is preserved by using the same hardware resources of squaring and multiplication for inversion computation. Finally, an efficient controller is used to control the inferred logic. The proposed ECP architecture is modeled in Verilog and the synthesis results are given on three different 7-series FPGA (Field Programmable Gate Array) devices, i.e., Kintex-7, Artix-7, and Virtex-7. The performance of the architecture is provided with the integration of a schoolbook multiplier (implemented with two different logic styles, i.e., combinational and sequential). On Kintex-7, the combinational implementation style of a schoolbook multiplier results in power-optimized, i.e., 161 μW, values with an expense of (i) hardware resources, i.e., 3561 look-up-tables and 1527 flip-flops, (ii) clock frequency, i.e., 227 MHz, and (iii) latency, i.e., 11.57 μs. On the same Kintex-7 device, the sequential implementation style of a schoolbook multiplier provides, (i) 2.88 μs latency, (ii) 1786 look-up-tables and 1855 flip-flops, (iii) 647 μW power, and (iv) 909 MHz clock frequency. Therefore, the reported area, latency and power results make the proposed ECP architecture well-suited for RFID applications.

The IoT sensor applications have grown in extreme numbers, generating a large amount of data, and it requires very effective data analysis procedures. However, the different IoT infrastructures and IoT sensor device layers possess protocol limitations in transmitting and receiving messages which generate obstacles in developing the smart IoT sensor applications. This difficulty prohibited existing IoT sensor implementations from adapting to other IoT sensor applications. In this article, we study and analyze how IoT sensor produces data for big data analytics, and it also highlights the existing challenges of intelligent solutions. IoT sensor applications required big data classification and analysis in a Fog computing (FC) environment using computation intelligence (CI). Our proposed Fog big data analysis model (FBDAM) and BPNN analysis model for IoT sensor application using fusion deep learning (FDL) pose new obstacles for potential machine-to-machine communication practices. We have applied our proposed FBDAM on the most significant Fog applications developed on smart city datasets (parking, transportation, security, and sensor IoT dataset) and got improving results. We compared different deep and machine learning algorithms (SVM, SVMG-RBF, BPNN, S3VM, and proposed FDL) on different smart city dataset IoT application environments.

In terms of growth, effect, and capability, the 5G-enabled Internet of Things (IoT) is incredible. The volume of data distributed and processed by IoT (Internet of Things) systems that trust connectivity and coverage raises some security problems. As IoT technology is directly used in our daily lives, the threats of present cyberspace may grow more prominent globally. Extended network life, coverage, and connectivity are all required for securing IoT-based 5G network devices. As a result of these failures, there are flaws that lead to security breaches. Because purposeful faults can quickly render the entire network dysfunctional, they are more difficult to identify than unexpected failures. Securing IoT-based 5G Network Device Connectivity and Coverage for expending Encryption and Authentication Scheme (EAS) framework is proposed in this study, which uses novel security flaws. In this research, we proposed a Boltzmann machine (BMKG)-based encryption algorithm for securing 5G-enabled IoT device network environment and compared various asymmetric algorithms for key exchange.

Twitter is one of the most popular online social networks for spreading propaganda and words in the Arab region. Spammers are now creating rogue accounts to distribute adult content through Arabic tweets that Arabic norms and cultures prohibit. Arab governments are facing a huge challenge in the detection of these accounts. Researchers have extensively studied English spam on online social networks, while to date, social network spam in other languages has been completely ignored. In our previous study, we estimated that rogue and spam content accounted for approximately three quarters of all content with Arabic trending hashtags in Saudi Arabia. This alarming rate, supported by autonomous concurrent estimates, highlights the urgent need to develop adaptive spam detection methods. In this work, we collected a pure data set from spam accounts producing Arabic tweets. We applied lightweight feature engineering based on rogue content and user profiles. The 47 generated features were analyzed, and the best features were selected. Our performance results show that the random forest classification algorithm with 16 features performs best, with accuracy rates greater than 90%.