As the Internet of Things (IoT) continues to grow, ensuring secure and trustworthy communication among distributed, resource-constrained devices remains a critical challenge. This paper introduces a Blockchain Based Distributed Trust (BDT) model designed to address the limitations of conventional trust mechanisms such as Public Key Infrastructure (PKI) and Proof of Work (PoW). Unlike PKI, which suffers from centralized trust bottlenecks, and PoW, which imposes excessive computational and energy burdens, BDT employs a lightweight, decentralized consensus mechanism optimized for IoT environments. By integrating an Exponentially Weighted Moving Average (EWMA) based trust score and edge level anomaly detection, BDT supports real time trust updates without reliance on central authorities. Experimental evaluation demonstrates that BDT offers substantial performance gains over PKI and PoW across key metrics. Specifically, BDT reduces communication overhead by 82.5% compared to PKI and 99.97% compared to PoW. It also achieves over 90% reduction in latency overhead relative to both alternatives. In terms of storage efficiency, BDT lowers storage requirements by 3.6% compared to PKI and 6.9% compared to PoW. Moreover, BDT maintains a false positive rate (FPR) below 3%, representing a reduction of approximately 40% compared to PKI and over 65% compared to PoW, even under high-intensity threats such as Sybil, MitM, and Replay attacks. These findings confirm that BDT offers a scalable, privacy preserving, and energy efficient trust model, delivering robust security without compromising performance making it a superior alternative to traditional PKI and PoW systems for real-time IoT applications.

Reliable channel state information (CSI) is a critical enabler for low-power Internet of Things (IoT) links and emerging 6G edge devices, where receivers must operate under tight energy/latency budgets and in the presence of non-ideal noise and malicious interference. Deep learning (DL)-based channel estimators can surpass classical LS/MMSE baselines; however, they remain vulnerable to distribution shifts and adversarial attacks targeting pilot observations. This paper proposes a lightweight and robust micro-channel estimation (μ-CE) framework based ondefensive distillation, where a compact student convolutional neural network (CNN) is trained under a higher-capacity teacher estimator using a regression-oriented distillation loss. The resulting μ-CE learns a smoother input–output mapping with reduced gradient sensitivity, improving trustworthiness without sacrificing accuracy or computational efficiency. Using MATLAB-and DeepMIMO-generated 5G NR TDL-C channels, we evaluate robustness under diverse non-adversarial noise types and four white-box gradient-based attacks (Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), Momentum Iterative Method (MIM), and Projected Gradient Descent (PGD)). Compared with an undefended CNN, the proposed μ-CE improves normalized mean squared error (NMSE) by approximately 0.5–1 dB under the considered non-adversarial noise conditions (including additive white Gaussian noise (AWGN) at a signal-to-noise ratio (SNR) of 15 dB in the default test setting), limits adversarial NMSE degradation to within 1–2 dB of the clean baseline for moderate perturbation budgets, and reduces attack success rate (ASR) by about 25–40%. Moreover, the distilled μ-CE requires roughly 14× fewer parameters and multiply-accumulate (MAC) operations than the teacher model, supporting practical deployment for robust CSI acquisition in resource-constrained IoT and 6G edge receivers.

Cloud computing faces more security threats, requiring better security measures.This paper examines the various classification and categorization schemes for cloud computing security issues, including the widely known CIA trinity (confidentiality, integrity, and availability), by considering critical aspects of the cloud, such as service models, deployment models, and involved parties.A comprehensive comparison of cloud security classifications constructs an exhaustive taxonomy.ISO27005, NIST SP 800-30, CRAMM, CORAS, OCTAVE Allegro, and COBIT 5 are rigorously compared based on their applicability, adaptability, and suitability within a cloud-based hosting methodology.The findings of this research recommend OCTAVE Allegro as the preferred cloud hosting paradigm.With many security models available in management studies, it is imperative to identify those suitable for the rapidly expanding and dynamically evolving cloud environment.This study underscores the significant methods for securing data on cloud-hosting platforms, thereby contributing to establishing a robust cloud security taxonomy and hosting methodology.

The safety and reliability of semantic segmentation networks face a major threat from adversarial perturbations which create problems in autonomous driving and medical imaging systems. The research develops a strong model-agnostic system which identifies and counteracts segmentation model attacks. The method applies uncertainty-based post-processing methods through pixel-wise entropy and dispersion metrics to detect adversarial inputs across different models without changing their internal structure. The method of adversarial robust knowledge distillation enables the transfer of defense capabilities from a high-capacity teacher network to an efficient student model which maintains segmentation accuracy and resource-limited deployment resilience. The integrated pipeline achieves state-of-the-art detection accuracy ($84.20 \%$) and segmentation robustness under various attack scenarios through empirical evaluation on standard benchmarks using convolutional and transformerbased segmentation architectures which outperform conventional baselines. The results demonstrate that robust distillation when used with uncertainty analysis leads to the development of reliable semantic segmentation networks for safety applications.

The proliferation of sensory applications has led to the development of the Internet of Things (IoT), which extends connectivity beyond traditional computing platforms and connects all kinds of everyday objects. Marine Ad Hoc Networks are expected to be an essential part of this connected world, forming the Internet of Marine Things (IoMaT). However, marine IoT systems are often highly distributed, and spread across large sparse areas which makes it challenging to implement and manage centralized security measures. Despite some ongoing efforts to establish network connectivity in such environment, securing these networks remains an unreached goal. The use of Certificate-Less Digital Signatures (CLDS) with Elliptic Curve Cryptography (ECC) shows great promise in providing secure communication in these networks and achieving zero trust IoMaT security. By eliminating the need for certificates and associated key management infrastructure, CLDS simplifies the key management process. ECC also enables secure communication with smaller key sizes and faster processing times, which is crucial for resource-limited IoMaT devices. In this paper, we introduce CLDS using ECC as a means of securing IoT networks in a marine environment, creating a zero trust security framework for Internet of Marine Things (IoMaT). To increase security and robustness of the framework, we optimize the ECC parameters using two vital artificial intelligence algorithms, namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Evaluation results demonstrate a reduction in ECC parameter generation time by over 40% with GA optimization and 20% with PSO optimization. Additionally, the computational cost and memory usage for major ECC attacks increased significantly by up to 40% and 67% for Rho attacks, 34% and 53% for brute-force attacks, and 30% and 67% for improved hybrid attacks, respectively.

Social engineering is hacking and manipulating people's minds to obtain access to networks and systems in order to acquire sensitive data. A social engineering attack happens when victims are unaware of the strategies utilised and how to avoid them. Although rapid developments in communication technology made communication between individuals easier and faster, on the other hand, individuals' personal and private information is likely to be available online via social networking or other services without adequate security measures to protect such sensitive data. Hackers can use social engineering to target them no matter the technology they use to protect themselves. The methods differ, and the goal is the same, which is to manipulate and deceive organisations, companies, and individuals to obtain sensitive and private in-formation that attackers can benefit from, perhaps to sell it on the dark web or steal the payment card information of victims. The current research presents the attack techniques used in social engineering, as well as ways for pre-venting social engineering assaults. The major purpose of this study is to systematically and impartially conduct a systematic review of previous research on current social engineering attacks and the methods used to reduce these attacks.

This paper focuses on the challenges and issues of detecting malware in to-day's world where cyberattacks continue to grow in number and complexity. The paper reviews current trends and technologies in malware detection and the limitations of existing detection methods such as signature-based detection and heuristic analysis. The emergence of new types of malware, such as file-less malware, is also discussed, along with the need for real-time detection and response. The research methodology used in this paper is presented, which includes a literature review of recent papers on the topic, keyword searches, and analysis and representation methods used in each study. In this paper, the authors aim to address the key issues and challenges in detecting malware today, the current trends and technologies in malware detection, and the limitations of existing methods. They also explore emerging threats and trends in malware attacks and highlight future directions for research and development in the field. To achieve this, the authors use a research methodology that involves a literature review of recent papers related to the topic. They focus on detecting and analyzing methods, as well as representation and extraction methods used in each study. Finally, they classify the literature re-view, and through reading and criticism, highlight future trends and problems in the field of malware detection.

This work studies the built-in multicast model in Contiki OS to provide the basis of a comparative evaluation for a new optimisation model using Radio Duty Cycling (RDC) mechanism. A significant amount of energy is consumed at the edge node executing various multicast routing protocols in Low-Power and Lossy Networks (LLN). The optimisation of the routing protocol and selection of an efficient multicast transmission model has the potential to reduce energy consumption in Edge Computing (EC) enabled LLN. With the precise objective of reducing energy consumption, this paper utilises a well-known RDC technique in multicast communication scenarios. To this end, a series of experiments are conducted to evaluate the performance of the existing RDC mechanisms proposed in the literature. The evaluation results are then utilised to develop an efficient RDC-based multicast transmission model. The comparative performance analysis reveals a 23.7% reduction in the RDC rate compared to the traditional model, consequently improving the energy consumption of EC-enabled LLN.

Efficient mobility management techniques are critical in providing seamless connectivity and session continuity between a mobile node and the network during its movement. However, current mobility management solutions generally require a central entity in the network core, tracking IP address movement, and anchoring traffic from source to destination through point-to-point tunnels. Intuitively, this approach suffers from scalability limitations as it creates bottlenecks in the network, due to sub-optimal routing via the anchor point. This is often termed “dog-leg” routing. Meanwhile, alternative anchorless, solutions are not feasible due to the current limitations of the IP semantics, which strongly tie addressing information to location. In contrast, this paper introduces a novel anchorless mobility solution that overcomes these limitations by exploiting a new path-based forwarding fabric together with emerging mechanisms from information-centric networking. These mechanisms decouple the end-system IP address from the path based data forwarding to eliminate the need for anchoring traffic through the network core; thereby, allowing flexible path calculation and service provisioning. Furthermore, by eliminating the limitation of routing via the anchor point, our approach reduces the network cost compared to anchored solutions through bandwidth saving while maintaining comparable handover delay. The proposed solution is applicable to both cellular and large-scale wireless LAN networks that aim to support seamless handover in a single operator domain scenario. The solution is modeled as a Markov-chain which applies a topological basis to describe mobility. The validity of the proposed Markovian model was verified through simulation of both random walk mobility on random geometric networks and trace information from a large-scale, city wide data set. Evaluation results illustrate a significant reduction in the total network traffic cost by 45 percent or more when using the proposed solution, compared to Proxy Mobile IPv6.

Seamless connectivity plays a key role in realizing QoS-based delivery in mobile networks. However, current handover mechanisms hinder the ability to meet this target, due to the high ratio of handover failures, packet loss and service interruption. These challenges are further magnified in Heterogeneous Cellular Networks (HCN) such as Advanced Long Term Evolution (LTE-Advanced) and LTE in unlicensed spectrum (LTE-LAA), due to the variation in handover requirements. Although mechanisms, such as Fast Handover for Proxy Mobile IPv6 (PFMIPv6), attempt to tackle these issues; they come at a high cost with sub-optimal outcomes. This primarily stems from various limitations of existing IP core networks. In this paper we propose a novel handover solution for mobile networks, exploiting the advantages of a revolutionary IP over Information-Centric Networking (IP-over-ICN) architecture in supporting flexible service provisioning through anycast and multicast, combined with the advantages of random linear coding techniques in eliminating the need for retransmissions. Our solution allows coded traffic to be disseminated in a multicast fashion during handover phase from source directly to the destination(s), without the need for an intermediate anchor as in exiting solutions; thereby, overcoming packet loss and handover failures, while reducing overall delivery cost. We evaluate our approach with an analytical and simulation model showing significant cost reduction compared to PFMIPv6.