The increasing growth of digital images has become significant in the areas of healthcare, real-time governance, and social media activities to validate identity to offer services without any inconvenience. However, the rapid advancement of artificial intelligence and security-related concepts has led to an increase in real-time security attacks, such as deepfakes on digital images. Advanced technologies have been used by attackers to exploit sensitive data, necessitating the proposal of novel approaches to counter them. Numerous studies have evaluated tamper detection using watermarking techniques such as LSB, DCT, DWT, correlation-based methods, and hybrid approaches. The drawbacks of these existing techniques include low embedding density, memory inefficiency, and insufficient integrity checks, which impact tamper detection efficiency. To address these limitations, a novel watermarking technique is proposed that uses a Multi-task Cascaded Convolutional Neural Network (MTCNN) and a Penultimate Least Significant Bit (PLSB) approach to secure digital human images against unlawful activities such as deep fakes and tampering. The proposed framework outperforms existing methodologies in key performance metrics, including SVD-DWT, DCT, DNN, PB-DMFB, and PCA-DCT, achieving a 2 % increase in SSIM, a 12 % enhancement in PSNR, and a 4 % reduction in MSE, indicating superior quality of the watermarked image and enhanced resistance to manipulation and tampering.

Underwater communication systems are vital for applications such as environmental monitoring, military surveillance, and offshore exploration. However, existing authentication protocols for underwater networks are often inefficient, vulnerable to replay and impersonation attacks, and lack resilience to node failures, a gap not fully addressed by current standards. The proposed study presents the design and implementation of a novel authentication protocol tailored for underwater communication systems. The approach leverages pentatope elliptic curve cryptography for efficient key generation and secure data exchange, ensuring robust protection against common cyber threats. Formal security analysis using BAN logic and the Scyther tool verifies resistance to replay, impersonation, and eavesdropping attacks, with no successful attacks detected in over 60 test cases. The resulting design demonstrates significant improvements in computational efficiency and resilience to adversarial attacks, ensuring scalable and reliable underwater communications. Thus, it represents a critical advancement in securing underwater networks, paving the way for practical deployment in mission-critical applications. The proposed protocol reduces total communication overhead to 2,112 bits (a 30–34% reduction) and lowers computational cost to 0.4 ms per entity, significantly improving efficiency compared to existing schemes. Furthermore, the protocol incorporates fallback authentication peers to ensure resilience under partial node outages.

Maritime data security plays a crucial role in in Navy and Coastal Areas, where the detection of vessels is sensitive as well as boundless and demands privacy preservation and accurate identification. While manual identification of vessels can be challenging, advancements in Cryptographic hash functions, Deep Learning technology, and image processing have simplified the task. However, existing techniques like YOLOv3, with its struggles in handling unusual aspect ratios, YOLOv5’s low mean average precision, and R-CNN’s increased complexity and lack of privacy preservation, motivate the need for an improved approach. In lieu of this, we propose an Extended-YOLOv7 model as a more effective detection solution due to its favorable characteristics like CSPNet, Feature Fusion Module (FFM), Spatial Pyramid Pooling (SPP), and Non-Maximum Suppression (NMS). Additionally, utilization of the gradient descent algorithm aims to optimize system performance. To ensure privacy preservation, our work employs the widely recognized and secure hashing algorithm SHA-256, which is extensively used for data security. The proposed system facilitates detecting vessel traffic in designated areas such as ports and harbours as well as enables real-time vessel detection and tracking for enhanced security and safety purposes. In addition to safeguarding sensitive data, our research addresses compliance with privacy regulations, mitigates the risks of data breaches, and upholds ethical considerations. With the integration of these driving factors, this work strives to elevate the security analysis of detected maritime vessels, foster a sense of trust and assurance, and promote the use of ethical data management techniques. The proposed model provides better performance than other state-of-the-art methods. Specifically, this is accomplished by achieving a 9.3% increase in Precision over YOLOv7.

Telemedicine and online consultations with doctors has become very popular during the pandemic and involves the transmission of medical data through the internet. Thus this raises concern about the security of the medical data of the patient as the records to contain sensitive and confidential information. A Secure multimedia transformation approach is proposed in this paper using a deep learning-based chaotic logistic map. The proposed work achieves novelty by the integration of a lightweight encryption function using a chaotic logistic map. It also uses the ResNet model to perform classification for identifying the fake medical multimedia data. A linear feedback shift register operations and an interactive user interface facilitate ease of usage to secure the medical multimedia data. The chaotic map provides the security properties such as confusion and diffusion necessary for the encryption ciphers. At the same time, they are highly sensitive to input conditions, thus making the proposed encryption algorithm more secure and robust. The proposed encryption mechanism helps in securing the medical image and video data. On the receiver side, Multilayer perceptions (MLP) of the deep learning approach are used to classify the medical data according to the features required to make other processes. When tested, the proposed work proves efficient in securing medical data against various cyber-attacks and exhibits high entropy levels.

The Internet of Things (IoT), with the ease of access of Automated Vehicles, is the most reliable technology in the 21stcentury making every possible thing within seconds. The people in this era are blessed with all the new technology, and new gadgets, due to which many things which used to take longer are done within nanoseconds. IoT combines computing devices, mechanical devices, digital machines, objects, and people which possess the ability to transfer data over the network with Unique identifiers (UIDs) without human intervention. Such IoT-enabled Automated Guided Vehicles (AGV) are more reliable on networks for every action. Deep learning and machine learning techniques are pivotal for the successful implementation of IoT-based applications including AGVs. A botnet refers to the attacks which come from robot Network attacks. Recently, many organizations have been compromised using this attack. Most affected devices are connected to IoT as it uses automatically generated data. The ideology of this study is to propose an intrusion prediction system which can predict botnet attacks in AGVs. In this study, N – Balo dataset is used for classification, clustering and prediction. The technique implemented in this paper may provide some roots to develop the most reliable and highly secured AGV network.

Abstract In the Industry 4.0 era, an inherited featured technology, blockchain, plays a vital role in knowledge engineering applications. Blockchain provides privacy to sensitive data as an intelligent agent, so its adoption rate increases in all the advanced domains. Especially in the health care department, blockchain technology usage helps avoid attacks like the Wannacry ransomware attack during 2017. Therefore, this paper described a decentralised application (DApp) expert system using public blockchain to create and maintain official health documents, especially medical certificates. Current existing systems, either paper‐based or database or clouds to save the medical certificates, have more scope to do attacks. Hence, proposed a blockchain‐based DApp that acts as an interface between intelligent agents, blockchains, and system related to the medical certificates. The main strength of this paper is implementation results, which are not among the maximum literary works currently available. The associate cost for conducting distributed application operations on the blockchain in terms of Gas comprehensively presented here. Furthermore, it consists of comparing the system's non‐functional functions by considering blockchain and non‐blockchain environments. Also, presented the simulation results with the performance results compared with the existed systems.

Malicious websites predominantly promote the growth of criminal activities over the Internet restraining the development of web services. Furthermore, we see different types of devices being equipped with WiFi capabilities, that allow web traffic to pass through the device’s data systems with ease. The proposed framework in the present study analyzes the Uniform Resource Locator (URL) through which malicious users can gain access to the content of the websites. It thus eliminates issues of run-time latency and possibilities of users being subjected to browser oriented vulnerabilities. The primary objective of this paper is to detect malicious links on the web using a machine learning classification technique that would help users defend against cyber-crime attacks and related threats of the real world. This may be helpful in the newly expanding Intelligent Infrastructures, where we see more data availability almost daily. The embedding of malicious URLs is a predominant web threat faced by the Internet community in the present day and age. Attackers falsely claim of being a trustworthy entity and lure users to click on compromised links to extract confidential information, victimizing them towards identity theft. The present work explores the various ways of detecting malicious links from the host-based and lexical features of the URL in order to protect users from being subjected to identity theft attacks.

Mobile ad hoc network (MANET) is a miscellany of versatile nodes that communicate without any fixed physical framework. MANETs gained popularity due to various notable features like dynamic topology, rapid setup, multihop data transmission, and so on. These prominent features make MANETs suitable for many real-time applications like environmental monitoring, disaster management, and covert and combat operations. Moreover, MANETs can also be integrated with emerging technologies like cloud computing, IoT, and machine learning algorithms to achieve the vision of Industry 4.0. All MANET-based sensitive real-time applications require secure and reliable data transmission that must meet the required QoS. In MANET, achieving secure and energy-efficient data transmission is a challenging task. To accomplish such challenging objectives, it is necessary to design a secure routing protocol that enhances the MANET’s QoS. In this paper, we proposed a trust-based multipath routing protocol called TBSMR to enhance the MANET’s overall performance. The main strength of the proposed protocol is that it considers multiple factors like congestion control, packet loss reduction, malicious node detection, and secure data transmission to intensify the MANET’s QoS. The performance of the proposed protocol is analyzed through the simulation in NS2. Our simulation results justify that the proposed routing protocol exhibits superior performance than the existing approaches.