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A Mobile Ad-Hoc Network (MANET) is a self-configuring network that provides temporary connections to several wireless nodes. Trust mechanisms are employed in routing protocols to quickly locate a safe path. Because of its openness and complexity, MANET can be attacked in a number of ways. To begin mitigating potential security risks, a number of different cryptographic key generation strategies are explored. A key management system for MANET security is available with different encryption techniques. Identity with Trust Level based Cryptography Model (ITLCM) is used to generate multiple keys and distribute these to particular targets. At this stage, key management protocols are essential to any secure group architecture of communication. Because of its dynamic topology which extensively affects its application, the multi key management is an essential task. When compared to more conventional methods of protecting a network, MANET security is entirely novel. Security routing protocol implementation is difficult since it requires the production and distribution of multiple keys. To provide both connection and message protection without relying on third parties, the Neighbor Trust Weight based Routing Model (NTWRM) is designed. In the proposed model, a trusted node is selected to monitor all of the nodes in the routing process to create a stable multi-key distribution environment that enhances MANET performance. In comparison with traditional methods, the proposed model shows that its findings are better than the existing ones.

DNA (Deoxyribonucleic Acid) Cryptography has revolutionized information security by combining rigorous biological and mathematical concepts to encode original information in terms of a DNA sequence. Such schemes are crucially dependent on corresponding DNA-based cryptographic keys. However, owing to the redundancy or observable patterns, some of the keys are rendered weak as they are prone to intrusions. This paper proposes a Genetic Algorithm inspired method to strengthen weak keys obtained from Random DNA-based Key Generators instead of completely discarding them. Fitness functions and the application of genetic operators have been chosen and modified to suit DNA cryptography fundamentals in contrast to fitness functions for traditional cryptographic schemes. The crossover and mutation rates are reducing with each new population as more keys are passing fitness tests and need not be strengthened. Moreover, with the increasing size of the initial key population, the key space is getting highly exhaustive and less prone to Brute Force attacks. The paper demonstrates that out of an initial 25 × 25 population of DNA Keys, 14 keys are rendered weak. Complete results and calculations of how each weak key can be strengthened by generating 4 new populations are illustrated. The analysis of the proposed scheme for different initial populations shows that a maximum of 8 new populations has to be generated to strengthen all 500 weak keys of a 500 × 500 initial population.

Key generation for data cryptography is vital in wireless communications security. This key must be generated in a random way so that can not be regenerated by a third party other than the intended receiver. The random nature of the wireless channel is utilized to generate the encryption key. However, the randomness of wireless channels deteriorated over time due to channel aging which casing security threats, particularly for spatially correlated channels. In this paper, the effect of channel aging on the ciphering key generations is addressed. A proposed method to randomize the encryption key each coherence time is developed which decreases the correlation between keys generated at consecutive coherence times. When compared to the conventional method, the randomness improvement is significant at each time interval. The simulation results show that the proposed method improves the randomness of the encrypting keys.

The fifth generation (5G) and beyond wireless communications will transform many exciting applications and trigger massive data connections with private, confidential, and sensitive information. The security of wireless communications is conventionally established by cryptographic schemes and protocols in which the secret key distribution is one of the essential primitives. However, traditional cryptography-based key distribution protocols might be challenged in the 5G and beyond communications because of special features such as device-to-device and heterogeneous communications, and ultra-low latency requirements. Channel reciprocity-based key generation (CRKG) is an emerging physical layer-based technique to establish secret keys between devices. This article reviews CRKG when the 5G and beyond networks employ three candidate technologies: duplex modes, massive multiple-input multiple-output (MIMO) and mmWave communications. We identify the opportunities and challenges for CRKG and provide corresponding solutions. To further demonstrate the feasibility of CRKG in practical communication systems, we overview existing prototypes with different IoT protocols and examine their performance in real-world environments. This article shows the feasibility and promising performances of CRKG with the potential to be commercialized.

The increasing demand for decentralized and user-controlled cryptographic key management in blockchain ecosystems has created interest in alternative entropy sources that do not rely on dedicated hardware. This study investigates whether commercial smartwatches can generate sufficient entropy for secure local key generation by utilizing their onboard sensors. An open-source Wear OS application was developed to harvest sensor data in two acquisition modes: still mode, where the device remains stationary, and shake mode, where data collection is triggered by motion events exceeding a predefined acceleration threshold. A total of 4800 still-mode and 4800 shake-mode samples were collected, each producing 11,400 bits of sensor-generated data. Entropy was evaluated using statistical metrics commonly employed in entropy analysis, including Shannon entropy, min-entropy, Markov dependency analysis, and compression-based redundancy estimation. The shake mode achieved Shannon entropy of 0.997 and min-entropy of 0.918, outperforming the still mode (0.991 and 0.851, respectively) and approaching the entropy levels of software-based random number generators. These results demonstrate that smartwatches can act as practical entropy sources for cryptographic applications, provided that appropriate post-processing, such as cryptographic hashing, is applied. The method offers a low-cost, transparent, and user-friendly alternative to specialized hardware wallets, aligning with the principles of decentralization and self-sovereign identity.

The rapid increase of internet of things (IoT) devices in our daily lives has highlighted the critical need for strong security measures to protect the integrity and confidentiality of IoT communications. This paper presents a novel solution to this growing problem using a secure and lightweight DNA-based encryption method, elliptic curve encryption (ECC), to secure IoT communications. The research explains how DNA-LWCS (DNA-based lightweight cryptography system) utilizes basic encryption methods to secure data transmission against system complexity while maintaining security effectiveness. The security key ensures enough protection for achieving the necessary level of confidentiality. Three fundamental keys are extracted from publicly accessible DNA sequences to start the procedure during its first phase. When employed together with ECC these keys generate a private key during the second stage of development. During the second stage the keys generate a private key based on ECC (elliptic curve cryptography) protocol. The encryption and decryption of IoT device messages requires this private key during the last operational phase. The combination of intuitive DNA sequences together with ECC generates better security and decreases the strain on systems. Practical evaluations demonstrate that the proposed encryption method offers better security and efficiency compared to existing methods while maintaining weightless operational performance. This makes it an ideal solution to secure IoT data exchange. The encryption method we investigated received detailed study which focused on both security and efficiency criteria during our research timeframe. The research demonstrates our security method outperforms other solutions by maintaining low resource requirements. Our proposed DNA-based encryption system shows potential as a suitable security measure for protecting IoT connections through its lightweight design capabilities.

Effective accident management acts as a vital part of emergency and traffic control systems. In such systems, accident data can be collected from different sources (unmanned aerial vehicles, surveillance cameras, on-site people, etc.) and images are considered a major source. Accident site photos and measurements are the most important evidence. Attackers will steal data and breach personal privacy, causing untold costs. The massive number of images commonly employed poses a significant challenge to privacy preservation, and image encryption can be used to accomplish cloud storage and secure image transmission. Automated severity estimation using deep-learning (DL) models becomes essential for effective accident management. Therefore, this article presents a novel Privacy Preserving Image Encryption with Optimal Deep-Learning-based Accident Severity Classification (PPIE-ODLASC) method. The primary objective of the PPIE-ODLASC algorithm is to securely transmit the accident images and classify accident severity into different levels. In the presented PPIE-ODLASC technique, two major processes are involved, namely encryption and severity classification (i.e., high, medium, low, and normal). For accident image encryption, the multi-key homomorphic encryption (MKHE) technique with lion swarm optimization (LSO)-based optimal key generation procedure is involved. In addition, the PPIE-ODLASC approach involves YOLO-v5 object detector to identify the region of interest (ROI) in the accident images. Moreover, the accident severity classification module encompasses Xception feature extractor, bidirectional gated recurrent unit (BiGRU) classification, and Bayesian optimization (BO)-based hyperparameter tuning. The experimental validation of the proposed PPIE-ODLASC algorithm is tested utilizing accident images and the outcomes are examined in terms of many measures. The comparative examination revealed that the PPIE-ODLASC technique showed an enhanced performance of 57.68 dB over other existing models.

The Internet of Things refers to networks of physical, technological devices connected via the Internet, allowing them to communicate and exchange data. Such environments face security issues like verifying users using Internet of Things devices, for example, in a company or hospital or properly securing user communication. Communication security largely relies on the security of symmetric keys, which we use to encrypt messages. This paper introduces a novel approach to verifying the identity of users of Internet of Things environments and generating symmetric keys between two users to communicate between them. The verification system identifies users using images captured from the camera. Thus, the proposed symmetric keys generation method uses biometric parameters representing the coordinates of a triangle from the two users’ faces biometry and the time factors. The triangle coordinates are located between the corners of the left and right eyes and the chin. Then, these coordinates and time factors undergo mathematical processing to obtain an alphanumeric symmetric session key. We tested the entire system, obtaining a high precision score regarding user identity verification, and additionally examined the possibility of breaking the generated symmetric keys. All keys were characterised by high entropy and resistance to brute-force attacks.