The fast growth of the Internet of Multimedia Things (IoMulT) has led to an unprecedented surge in real-time multimedia data generated by heterogeneous devices such as smart cameras, LiDAR sensors, and UAVs. Efficient data aggregation across edge-fog-cloud (EFC) infrastructures remains a critical challenge due to the high dimensionality, redundancy, heterogeneity, and dynamic scene complexity. To address these issues, this paper proposes a novel Deep Q-Network (DQN)-based EFC aggregation framework specifically designed for real-time multimedia surveillance applications in IoMulT environments. The framework integrates Convolutional neural network (CNN)-driven feature extraction and adaptive frame selection at the edge layer, You-Only-Look-Once (YOLO)v5-based object detection aggregation with DQN-guided fine-tuning at the fog layer, and behavior analysis with hierarchical aggregation at the cloud layer. A dynamic DQN-based feedback mechanism is integrated across layers to optimize aggregation quality, resource consumption, and system responsiveness in real-time. The proposed system is validated through a real-world IoMulT experimental setup comprising Arduino Internet of Things (IoT) devices, edge processors, fog servers, and cloud infrastructure. Extensive experimental evaluations demonstrate significant improvements in Peak Signal-to-Noise ratio (PSNR), Structural Similarity Index Measure (SSIM), mean Average Precisison (mAP), prediction confidence, resource utilization, load balancing, and reward optimization compared to existing aggregation methods such as histogram estimation, grid hashing, and spatio-temporal fusion techniques. The framework outperforms prior approaches by achieving up to 18% higher mAP, 22% lower resource consumption, and 25% cumulative reward improvement, affirming its efficacy for next-generation smart surveillance systems.

The Mission-critical IIoT (MC-IIoT) networks are highly interconnected and distributed to ensure real-time monitoring and control, where security, reliability, and availability are paramount. These features make MC-IIoT networks vulnerable to Distributed Denial of Service (DDoS) attacks that must be mitigated while combating single-point of failure of attack detection systems, limited scalability of industrial control systems, high latency and low reliability. In this paper, we propose a Blockchain-based DDoS attack mitigation framework for MC-IIoT networks which persuades these requirements. Initially, a machine learning-based cascaded model consisting of an autoencoder-enhanced decision tree model and a lightweight isolation forest algorithm performs attack detection and trust score evaluation of IIoT devices. A consortium Blockchain network of IIoT gateways on which this cascaded model is installed performs attack mitigation using a chaincode based on previously evaluated trust scores. The proposed model is evaluated on Hyperledger Fabric and the performance is analyzed with Hyperledger caliper benchmarking. The comparative analysis of the proposed framework with state-of-the-art proves its effectiveness in terms of attack detection accuracy (99.93%), CPU utilization (30%), decreased latency (12.5 ms), increased throughput (21%), chaincode execution time (3 ms) and attack detection rate (99.6%).

In today's digital age, mobile phones are vital for communication, navigation, and productivity, but their growing role as data repositories makes them prime targets for theft, leading to financial loss, privacy breaches, and operational disruptions. Traditional prevention methods often lack robust data protection and are vulnerable to unauthorized access. To address these challenges, this paper proposes a blockchain based mobile theft detection system using Hyper ledger Fabric, a permissioned blockchain platform. The system assigns each mobile device a unique identifier (UID) upon registration and securely stores metadata, such as location and status, on the blockchain. In case of theft, the owner can report the incident, triggering smart contracts to update the device's status and notify network participants in real time. This decentralized approach ensures that stolen devices are flagged and blocked across participating networks. Additionally, the system supports secure ownership transfer, reducing fraud and enhancing trust in the mobile ecosystem.

This paper presents the design and implementation of a Lorenz chaotic system-based Ring Oscillator Physically Unclonable Function (RO PUF) on Field Programmable Gate Arrays (FPGAs). PUFs have evolved as an important component of hardware security, providing unique IDs derived from inherent process variations during integrated circuit fabrication. The chaotic dynamics of the Lorenz system, which is recognised for its unpredictability and sensitivity to initial conditions, give a unique technique to creating random and secure challenges for PUF. The present research demonstrates the viability of incorporating chaotic systems into PUF designs to enhance security. The proposed system offers a reliable and adaptable way to generate secure and distinctive IDs that can be applied to a range of embedded systems, IoT, and cybersecurity applications. The results emphasise the potential of chaotic dynamics to considerably enhance the security features of hardware-based cryptographic primitives. By utilising the intrinsic variability of the Lorenz system, the proposed design optimises the security features of the PUF, guaranteeing a high degree of unpredictability and uniqueness. The experimental results indicate that the design is relatively low in hardware overhead, has a reliability of 97.79%, and a uniqueness of$\mathbf{4 8. 9 1 \%}$. Additionally, the design parameters can be adjusted to alter the output response by the design parameters.

Routing protocol for low-power and lossy network (RPL) is a routing protocol for resource-constrained Internet of Things (IoT) network devices. RPL has become a widely adopted protocol for routing in low-powered device networks. However, it lacks essential security features, including end-to-end security, robust authentication, and intrusion detection capabilities. Blockchain is a decentralized and immutable digital ledger that records transactions across multiple computers. It provides privacy, transparency, security, and trust. In this work, we proposed a blockchain-based reliable RPL protocol called reliable-RPL, which uses node reliability, link reliability, and relative trust scores of RPL-enabled IoT devices. The parent selection and network topology formulation are based on the proposed reliability-aware objective function. A lightweight ECC-based scheme performs registration, identification, and authentication of RPL-enabled IoT devices. The consistent topological updates from these authenticated IoT devices are used to secure routing paths in RPL-enabled networks. Using a modified trickle algorithm, we employed a reputation-based trust system that monitors and labels malicious nodes based on their reliable activities. The novelty of the proposed framework relies on integrating Contiki-NG (as fronted for IoT network simulation) and Hyperledger Fabric (as a backend for blockchain-based device authentication and trust-based attack resilience regarding rank, replay, sinkhole, and route poisoning attacks). The experimental evaluation of reliable-RPL has demonstrated its effectiveness compared to state-of-the-art methods regarding significant performance metrics, including packet loss, routing overhead, and throughput on Hyperledger Caliper.

The Internet of Things (IoT) refers to a complex network comprising interconnected devices that transmit their data via the Internet. Due to their open environment, limited computation power, and absence of built-in security, IoT environments are susceptible to various cyberattacks. Denial of service (DDoS) attacks are among the most destructive types of threats. The Multi-vector DDoS attack is a contemporary and formidable form of DDoS wherein the attacker employs a collection of compromised IoT devices as zombies to initiate numerous DDoS attacks against a target server. A Blockchain-based Operational Threat Intelligence framework, OTI-IoT, is proposed in this article to counter multi-vector DDoS attacks in IoT networks. A “Prevent-then-Detect” methodology was utilized to deploy the OTI-IoT framework in two distinct stages. During Phase 1, the consortium Blockchain network validators employ the IPS module, composed of a smart contract for attack prevention and access control, and Proof of Voting consensus, to thwart attacks. Validators are outfitted with deep learning-based IDS instances to detect multi-vector DDoS attacks during Phase 2. Alert messages are generated by the IDS module’s alert generation and propagation smart contract in response to identifying malicious IoT sources. The feedback loop from the IDS module to the IPS module prevents incoming traffic from malicious sources. The proposed OTI framework capabilities are realized as an outcome of combining and storing the outcomes of the IDS and IPS modules on the consortium Blockchain. Each validator maintains a shared ledger containing information regarding threat sources to ensure robust security, transparency, and integrity. The operational execution of OTI-IoT occurs on an individual Ethereum Blockchain. The empirical findings indicate that our proposed framework is most suitable for real-time applications due to its ability to lower attack detection time, decreased block validation time, and higher attack prevention rate.

The deployment of fog computing paradigms for securing IoT networks is associated with several advantages, in-cluding reduced bandwidth, latency, storage, and computational overhead at cloud servers. However, the fog layer has additional security requirements, such as the establishment of secure channels for key distribution and the overhead of repetitive device authentications. In this paper, we have addressed these issues using a permissioned blockchain-based fog network that validates the edge devices through smart contracts by establishing a mechanism for secure storage and exchange of credentials. The communication between edge devices is carried out through the M QTT protocol, and device registration is performed using a smart contract. The key pairs are generated from the secp256kl elliptic curve to ensure faster trust-based identity management and authentication of edge devices and gateways. The proposed frameworks ensures that the access to Blockchain network is given exclusively for authenticated devices The implementation of the proposed scheme is performed on private Ethereum 2.0, and the performance is evaluated in terms of node registration time, authentication time, key generation time, and throughput. The implementation results are available on GitHub(https://github.com/Aswani08/tenconresults.git).

In recent years, the growing use of biometric recognition systems in various applications has increased the need to protect the biometric templates recorded in multiple databases. Due to their consistency and uniqueness, iris recognition systems have significantly outperformed other biometrics. Directly stored Iris templates on a central server constitute a privacy and security risk. To address this, we will generate a cancelable template that can be stored instead of the original. In the event of a security breach, we will discard the stored template and generate a new iris template. This research employs the Weber Local Descriptor (WLD) technique to create a multi-instance iris biometric system. Left and right iris images are initially acquired and normalized using the USIT toolkit. We generate a feature vector from the normalized image using WLD. The obtained feature vector is then normalized using L1 normalization. The vector of normalized features is then projected onto a median filter to generate a cancelable template. Experiments are conducted on the IIT Delhi iris database, and the results are optimistic compared to previously published research.

Internet of everything (IoET) is one of the key integrators in Industry 4.0, which contributes to large-scale deployment of low-power and lossy (LLN) networks to connecting people, processes, data, and things. The RPL is one of the unique standardized routing protocols that enable efficient use of smart devices energy, compute resources to address the properties and constraints of LLN networks. The authors investigate the RPL-AODV routing protocol's performance in combining the advantages of both RPL and AODV routing protocol, which works together in a low power resource-constrained network. The main challenging issue is collaborating the AODV and RPL routing protocol in the LLN network. This paper also models the collaborative attacks such as wormhole, blackhole attack for AODV, and rank and sinkhole attacks to exploit the vulnerability of RPL protocol. Finally, the cooperative IDS combining specification-based and signature-based IDS is proposed to detect the collaborative attacks against the RPL-AODV routing protocol that effectively monitors and provides security to the LLN networks.

Abstract The Internet of Things (IoT) has become a significant technology on the internet with its widespread adoption in almost every place we could think of, like homes, hospitals, industries, companies, and so on. This adoption in virtually every device had made them smart, thereby reducing the human intervention to handle them. These devices become smart by gathering the sensed information and communicating with other devices or servers to take the appropriate decisions based on acquired data. However, these devices are deployed in batches with default usernames and passwords, making them vulnerable to attacks as seen in recent pasts like the Mirai botnet attack. Most of the attacks could have been avoided if these devices were equipped with a decent lightweight secure authentication scheme. One of the most common authentication procedures is using traditional public key infrastructure (PKI), which suffers from a single point of failure. Moreover, the complex procedures of PKI make them unfit for low‐powered IoT devices. Identity‐based cryptography (IBC), a lightweight cryptosystem, could be a good fit for these devices. But, even IBC suffers from a single point of failure and key escrow problem because of the private key generator (PKG). Blockchain has proved its mettle in eliminating a single point of failure with its robust distributed ledger technology. This article presents a novel authentication scheme for IoT devices based on identity‐based cryptography using a blockchain network. Blockchain is used as a distributed PKG, eliminating a single point of failure and key escrow problem of PKGs. Further, the proposed work is implemented in Hyperledger Fabric, which is an open‐source blockchain platform that efficiently performs the addition, updating, and deletions operation for effective authentication and communication of IoT devices.

Fake certificates pose a severe problem in today's world; they vouch for an individual's false skillset and put an organization's reputation at risk. Moreover, the existing verification process is performed in a centralized manner, often too cumbersome and time-consuming to the end-user, lacking transparency in the educational institutions' Issuance of certificates. Of-late, blockchain is a promising technology that provides transparent, secure, and reliable features, which offers solutions to the education sector. This paper provides the solution to the educational certification problem by employing the blockchain network. We proposed a permissioned blockchain network that identifies, authenticates the Issuer, adequate verification, securely shares academic records to the recipients, and stores the certificate credentials in the blockchain in a distributed manner.

Secure communication is one of the basic requirements for any network standard. Particularly, cryptographic algorithms have gained more popularity to protect the communication in a hostile environment. As the critical information that is being transferred over the wireless adhoc networks can be easily acquired and is vulnerable to many security attacks. However, several security communication threats had been detected and defended using conventional symmetric and asymmetric cryptographic mechanism, which are too difficult and resource consuming for such mobile adhoc networks. Recently, one of the severe security threats that have to be detected and defend in any type of network topology is blackhole attack and cooperative blackhole. Because of its severity, the black hole attack has attracted a great deal of attention in the research community. Comprehensively the results of the existing system conclude that the black hole attack on various mobile adhoc networks is hard to detect and easy to implement. This paper addresses to detect and defend the blackhole attack and cooperative blackhole attack using hybrid DNA-based cryptography (HDC) mechanism. Moreover, the proposed method upsurge the security issue with the underlying AODV routing protocol. Eventually, This Hybrid DNA-based Cryptography (HDC) is one of the high potential candidates for advanced wireless ad hoc networks, which require less communication bandwidth and memory in comparison with other cryptographic systems. The simulation results of this proposed method provide better security and network performances as compared to existing schemes.