Message Queuing Telemetry Transport (MQTT) is a lightweight publish–subscribe protocol widely deployed in Internet of Things (IoT) systems. Although MQTT defines authentication and authorization mechanisms, their enforcement accuracy, configuration sensitivity, and operational cost under controlled misconfiguration conditions remain insufficiently quantified. This study experimentally quantifies authentication enforcement behavior and Access Control List (ACL) misconfiguration impact within a standards-compliant MQTT deployment under controlled laboratory conditions. Rather than benchmarking a specific software product, the work measures protocol-defined security behavior—including authentication success rate, false acceptance rate (FAR), false rejection rate (FRR), privilege-boundary preservation, authentication latency, and broker CPU utilization—across systematically constructed operational and failure scenarios. Username/password and mutual TLS authentication were evaluated under valid and stress-induced connection conditions, alongside structured ACL policies incorporating wildcard over-permission. Across repeated trials, username/password authentication achieved higher observed connection reliability (≈0.95), while TLS-based authentication provided stronger cryptographic identity assurance at the cost of increased authentication latency (≈42.6 ms vs. 14.8 ms) and higher CPU utilization (≈23.7% vs. 9.4%). No false acceptances were observed within 100 unauthorized trials per configuration, corresponding to a 95% confidence upper bound of <3% for FAR under a binomial model. Under controlled ACL misconfiguration, 22 of 100 evaluated authorization operations accessed topics beyond the originally intended least-privilege scope, yielding a reproducible privilege expansion rate of 0.22. This expansion resulted from wildcard policy semantics rather than an enforcement malfunction. The results provide controlled empirical quantification of reliability–security trade-offs and configuration-driven privilege-boundary behavior within a standards-compliant MQTT deployment. While the findings reflect enforcement behavior as realized in the evaluated implementation and laboratory environment, the proposed measurement framework establishes reproducible criteria for assessing MQTT security enforcement accuracy under controlled conditions.

The integration of blockchain technology into Cyber–Physical Systems (CPS) offers decentralized resilience against data manipulation. This also introduces stochastic consensus latencies that threaten real-time control stability. We present a Stochastic-Aware Blockchain Predictive Control (SAB-PC) framework, which models blockchain-induced jitter as a state-dependent Markovian process, and embeds it within a Markovian Jump Linear System (MJLS) formulation. Using mode-dependent Linear Matrix Inequalities (LMIs), we derive Mean Square Stability (MSS) conditions, which capture the interaction between decentralized consensus dynamics and closed-loop control behavior. The framework is validated on the Tennessee Eastman Process (TEP) benchmark, using a calibrated stochastic delay model that reflects realistic blockchain congestion patterns. Our results show that standard blockchain-mediated control architectures become unstable under Practical Byzantine Fault Tolerance (PBFT)-induced quadratic latency growth, whereas SAB-PC maintains stable operation across decentralized networks up to 60 validator nodes. The predictive Safety Runway effectively masks long-tail delay distributions, ensuring real-time feasibility and preserving safe Reactor Pressure trajectories. Under coordinated False Data Injection (FDI) attacks, SAB-PC limits pressure deviations to only 1.2 kPa despite an 8.0 kPa adversarial bias, demonstrating cryptographic and control-theoretic resilience.

MQTT is widely used in Internet of Things (IoT) systems because of its lightweight publish–subscribe architecture and efficient support for resource-constrained devices. Although wildcard subscriptions simplify topic management, their impact on broker performance, stability, and security under authenticated high-load conditions has not been comprehensively investigated. Existing studies typically evaluate routing performance, security mechanisms, or broker scalability independently, leaving a limited understanding of their combined effects. This paper presents a systematic experimental evaluation of wildcard subscription behavior in an authenticated MQTT v5 environment. A controlled testbed employing TLS-based authentication and role-based access control was used to compare exact-topic subscriptions with single-level (+) and multi-level (#) wildcard subscriptions under progressively increasing workloads. Performance was evaluated using end-to-end latency, CPU utilization, throughput, delivery success rate, broker stability, and authorization exposure. The experimental results demonstrate that increasing wildcard-subscription complexity significantly increases routing overhead, resulting in higher latency and CPU utilization while reducing throughput and broker service capacity. Multi-level wildcard subscriptions consistently exhibited the greatest performance degradation and reached broker saturation at lower workload levels than exact-topic subscriptions, demonstrating that wildcard density compresses the broker’s operational stability region. The experiments also show that broad wildcard-based access control policies increase the risk of authorization leakage when improperly configured. These findings demonstrate that wildcard-subscription complexity is a critical determinant of MQTT scalability, broker stability, and security, and provide practical guidance for designing efficient and secure IoT messaging infrastructures.

Migrating to Post-Quantum Cryptography (PQC) is critical for securing resource-constrained Internet of Things (IoT) devices against the “harvest-now, decrypt-later” threat. While ML-KEM (CRYSTALS-Kyber) has been standardized under FIPS 203 for general encryption, these devices often operate on unreliable networks suffering from high latency and packet loss. Our recent systematic review identified a critical gap that existing research overwhelmingly focuses on Transport Layer Security (TLS). This leaves the resilience of lightweight protocols like MQTT and CoAP under challenging network conditions largely unexplored. This paper introduces PQC-IoTNet, a novel Software-in-the-Loop (SITL) framework to address this gap. Our three-tier architecture integrates a Python-based IoT client with kernel-level emulation to test the full protocol stack. Validation results comparing Kyber and ECC demonstrate the framework’s ability to capture critical performance cliffs caused by TCP retransmissions. Notably, the framework revealed that while Kyber maintained an 18% speed advantage over ECC at 5% packet loss, both protocols experienced nonlinear latency spikes. This work provides a reproducible blueprint to identify operational boundaries and select resilient protocols for secure IoT systems.

Ransomware represents a critical and evolving cybersecurity threat that often evades traditional defenses during its early stages. We present a novel intelligent sensing framework (ISF) designed for proactive, early-stage ransomware detection, centered on a Multi-Head Self-Attention Long Short-Term Memory (MHSA-LSTM) sensor model. The core innovation of this sensor is its self-attention mechanism, which is augmented to autonomously prioritize the most discriminative behavioral features by incorporating a relevance coefficient derived from information gain (μ), thereby filtering out noise and overcoming data scarcity inherent in initial attack phases. The framework was validated using a comprehensive dataset derived from the dynamic analysis of 39,378 ransomware samples and 9732 benign applications. The MHSA-LSTM sensor achieved superior performance, recording a peak accuracy of 98.4%, a low False Positive Rate (FPR) of 0.089, and an F1 score of 0.972 using an optimized 25-feature set. This performance consistently surpassed established sequence models, including CNN-LSTM and Stacked LSTM, confirming the significant potential of the ISF as a robust and scalable solution for enhancing defenses against modern, stealthy threats. Most significantly, integration of μ as a statistical anchor resulted in a 49% reduction in False Positive Rates (FPRs) compared to standard attention-based models. This addresses the main operational barrier to deploying deep learning sensors in live environments.

The rollout of 5G Standalone networks introduces unprecedented flexibility and performance through service-based architecture (SBA), virtualization, open APIs, and network slicing, while simultaneously expanding the attack surface across control, user, and cross-plane interfaces. This article provides a systematic, vulnerability-prioritized, selective characterization of the current state of weaknesses specific to the 5G control and user planes and transparent risk scoring. Using a PRISMA-aligned methodology, vulnerabilities are mapped explicitly to 3GPP network functions and interfaces (e.g., AMF, SMF, UPF; N2, N4, SBA APIs) and categorized by operational evidence level ranging from theoretical analysis to documented live-network exploitation. A normalized criticality scoring model integrates likelihood, impact, exploitability, and CVSS-derived severity. The analysis shows that control-plane signaling floods, PFCP misuse, and container escapes stand out as the most pressing risks. It also exposes how little attention has been given to securing the user plane and strengthening slice isolation. The paper wraps up with clear, evidence-based hardening priorities for each plane, along with research areas that matter for today’s 5G networks and the shift toward 6G.

Multi-tenancy is essential for scalable IoT–Cloud systems; however, it introduces complex security vulnerabilities at the intersection of shared cloud infrastructures and resource-constrained IoT environments. This systematic review evaluates next-generation security frameworks designed to enforce tenant isolation without violating the strict latency (<10 ms) and energy bounds of lightweight sensors. Adhering to PRISMA guidelines, we analyze selected high-quality studies to categorize intersectional threats, including cross-tenant data leakage, side-channel attacks, and privilege escalation. Our analysis identifies a critical, unresolved conflict: existing mitigation strategies often incur a 12% computational and communication overhead, creating a significant barrier for real-time applications. Furthermore, we critically analyze emerging technologies, including Zero Trust Architectures (ZTA), adaptive Artificial Intelligence (AI), blockchain, and Post-Quantum Cryptography (PQC). We find that direct PQC deployment is currently infeasible for LPWAN protocols due to key-size constraints (1.6 KB) that exceed typical payload limits. To address these challenges, we propose a novel multi-layer security design principle that offloads heavy isolation and cryptographic workloads to hardware-accelerated edge gateways, thereby maintaining tenant isolation without compromising real-time performance. Finally, this review serves as a roadmap for future research, highlighting federated learning and hardware enclaves as essential pathways for securing next-generation multi-tenant IoT ecosystems.

Networked Cyber–Physical Systems (CPSs), like autonomous quadrotor swarms, tightly couple continuous physical kinematics, wireless information exchange, and discrete real-time task scheduling. While conventional consensus security architectures focus exclusively on data-layer falsification, they fundamentally decouple adversarial behavior from onboard computational resource state profiles. This paper addresses a core CPS vulnerability termed Computational Weaponization, the deliberate injection of complex computational workloads (adversarial LLM token parsing or cryptographic verification) to intentionally manipulate hardware execution delays. Through this exploit, strategic cyber–physical perturbations force resource-constrained embedded microcontrollers to saturate their task execution queues, inducing real-time scheduling starvation and physical tracking divergence. To mitigate this without optimization bottlenecks, we present a state-dependent, Self-Triggered Control (STC) and Prospect Theoretic Alignment (PTA) co-design framework. The proposed protocol models the hardware microprocessor’s execution delay as an endogenous dynamic state coupled directly to continuous tracking spaces. By mapping discrete topology reconfigurations and variable task delays to a switched impulsive time-delay system, we leverage an Input-to-State Stability (ISS) to derive sufficient linear matrix inequality conditions. We prove that the coupled cyber–physical–computational loop achieves asymptotic consensus and bounded trajectory containment under adversarial actions.

Inverter-dominated microgrids are highly vulnerable to stealthy cyber-physical drift attacks, low-amplitude, slowly varying perturbations that bypass conventional statistical filters to induce voltage degradation and delayed collapse. This paper introduces a resilient, delay-aware supervisory control architecture that acts as an online safety shield at the actuator interface. By jointly modeling nonlinear power-flow interactions and directional communication topologies, we construct physics-informed Control Barrier Functions (CBFs), embedding structural electrical invariants derived from the nodal admittance matrix Ybus. The supervisor directly incorporates heterogeneous, time-varying network delays into its safety constraints and utilizes a threat-adaptive modulation loop driven by spatio-temporal residuals to dynamically scale intervention aggressiveness. Using a Lyapunov-Krasovskii functional, we prove that the closed-loop tracking error is Input-to-State Stable (ISS) under bounded drift and worst-case latencies. High-fidelity simulations on an IEEE 14-bus test feeder demonstrate that the supervisor consistently enforces non-negative safety margins and reduces time-integrated voltage violations. Under coordinated sub-threshold attacks designed to exploit network jitter, the architecture bounds trajectories to physically consistent manifolds and prevents voltage collapse, establishing a scalable cross-layer safety framework for resilient distribution systems.

Web Application Programming Interfaces (Web APIs) have become fundamental components of modern software ecosystems. At the same time, they have emerged as major attack surfaces in web applications and distributed services. Although many web API vulnerabilities are well documented, a critical gap remains in understanding how insecure development practices, usability limitations, and developer-related issues contribute to recurring API security problems. To address this gap, this study presents a systematic review of web API security research using a PRISMA-guided methodology and a taxonomy-driven analytical approach. The review synthesizes findings from 50 selected studies covering web API architectural styles, usability concerns, authentication and access-control weaknesses, and common vulnerabilities. These vulnerabilities include SQL Injection (SQLi), Cross-Site Scripting (XSS), Broken Authentication, and Denial-of-Service (DoS) attacks within the context of the OWASP API Security Top 10 framework. The findings indicate that recurring web API vulnerabilities are associated not only with technical weaknesses but also with API usability issues, insecure development practices, inconsistent security guidance, and increasing implementation complexity. The review also identifies persistent research gaps involving usability-security integration, API evolution, secure-by-design development practices, and empirical validation of security tools and frameworks. By synthesizing these dimensions into a unified conceptual perspective, this study provides researchers and practitioners with a clearer understanding of the factors contributing to web API insecurity. The study also highlights directions for developing more resilient and developer-aware API security practices.

We propose a resilient framework for the mitigation of misinformation epidemics within dynamic information ecosystems under operational latencies and adversarial telemetry corruption. A dual-layer control architecture that balances platform-level usability with hard safety constraints is designed. The framework utilizes a polyhedral backward induction scheme to synthesize a verified controlled invariant cover. This geometric formulation guarantees that node-level infodemic penetration levels remain strictly bounded within a designated safe target set. To counter coordinated false data injection (FDI) attacks on state reporting channels, we integrate an online state observer, utilizing a private physical watermarking sequence, Δ w(t) . This mechanism creates an asymmetric information structure that exposes stealthy evasion tactics through a Chi-Squared ( χ ^2 ) tracking residual monitor. Parametric sensitivity profiling maps the operational boundaries of the network, isolating the primary destabilizing role of virality ( β ) alongside the primary stabilizing lever of intervention effectiveness ( κ ). Empirical validation conducted on synthetic networks and the ESOC COVID-19 Misinformation Dataset demonstrates that the self-triggered adaptive control law consistently outperforms baseline implementations, yielding a platform usability cost reduction between 38.5% and 53.8% while maintaining absolute safety integrity. These results establish the framework as a robust tool for securing critical information infrastructure against sophisticated, coordinated manipulation.

The confluence of the Internet of Things (IoT) and cloud computing heralds a paradigm shift in data-driven applications, promising unprecedented insights and automation across critical sectors like healthcare, smart cities, and industrial automation. However, this transformative synergy introduces a complex tapestry of security vulnerabilities stemming from the intrinsic resource limitations of IoT devices and the inherent complexities of cloud infrastructures. This survey delves into the escalating threats—from conventional data breaches and Application programming interface (API) exploits to emerging vectors such as adversarial artificial intelligence (AI), quantum-resistant attacks, and sophisticated insider threats—that imperil the integrity and resilience of IoT–cloud ecosystems. We critically evaluated existing security paradigms, including encryption, access control, and service-level agreements, juxtaposed with cutting-edge approaches like AI-driven anomaly detection, blockchain-secured frameworks, and lightweight cryptographic solutions. By systematically mapping the landscape of security challenges and mitigation strategies, this work identified the following critical research imperatives: the development of standardized, end-to-end security architectures, the integration of post-quantum cryptography for resource-constrained IoT devices, and the fortification of resource isolation in multi-tenant cloud environments. A comprehensive comparative analysis of prior research, coupled with an in-depth case study on IoT–cloud security within the healthcare domain, illuminates the practical challenges and innovative solutions crucial for real-world deployment. Ultimately, this survey advocates for the development of scalable, adaptive security frameworks that leverage the synergistic power of AI and blockchain, ensuring the secure and efficient evolution of IoT–cloud ecosystems in the face of evolving cyber threats.

Steganography conceals ”secrets” within an convenient and expedient multimedia carrier. The carrier could be text (i.e., not plain text), images, audio andor video files (i.e., carrier channels). The fact that concealed information is contained in the otherwise ordinary and mundane carrier file is known only by the sender-receiver pair. Only they share the existence of the secret. Images are the most popular (i.e., multimedia) carriers because of their inherent property that enables better obfuscation. Content adaptive image steganography is a new trend in the field for messaging secrets inside unsuspected image file transfers. As the name suggests, the embedding locations are altered adaptively depending on the image content that optimizes the decision of choosing a location inside the carrier so that an embedding is not discernible (i.e., additive distortion is minimized). Herein, we critique the various approaches used for content-adaptive image steganography which can be broadly categorized as CNNbased, GAN-based, along with minimizing additive distortion function-based. We provide a brief historical account toward better anticipating the future research opportunities in terms of properties, and evaluation metrics. A summary table of these past and future directions is provided. Moreover, we highlight trends along with their concomitant advantages and disadvantages toward identifying opportunity gaps.

The rapid advancement of quantum computing poses significant threats to classical cryptographic methods, such as Rivest–Shamir–Adleman (RSA) and Elliptic Curve Cryptography (ECC), which currently secure Internet of Things (IoT) and cloud communications. Post-Quantum Cryptography (PQC), particularly lattice-based schemes, has emerged as a promising alternative. CRYSTALS-Kyber, standardized by the National Institute of Standards and Technology (NIST) as ML-KEM, has shown efficiency and practicality for constrained IoT devices. Most existing research has focused on PQC within the Transport Layer Security (TLS) protocol. Consequently, a critical gap exists in understanding PQC’s performance in lightweight IoT protocols. These are Message Queuing Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP), particularly under adverse network conditions. To address this gap, this paper provides a systematic review of the literature on the network resilience and performance of CRYSTALS-Kyber when integrated into these protocols operating over lossy and high-latency networks. Additional challenges include non-standardized integration, resource limitations, and side-channel vulnerabilities. This review provides a structured synthesis of current knowledge, highlights unresolved trade-offs between security and efficiency, and outlines future research directions, including protocol-level optimization, lightweight signature schemes, and resilience testing of PQC-secured IoT protocols under realistic conditions.

Ransomware, a significant cybersecurity threat, encrypts files and causes substantial damage, making early detection crucial yet challenging. This paper introduces a novel multi-phase framework for early ransomware detection, designed to enhance accuracy and minimize false positives. The framework addresses the limitations of existing methods by integrating operational data with situational and threat intelligence, enabling it to dynamically adapt to the evolving ransomware landscape. Key innovations include (1) data augmentation using a Bi-Gradual Minimax Generative Adversarial Network (BGM-GAN) to generate synthetic ransomware attack patterns, addressing data insufficiency; (2) Incremental Mutual Information Selection (IMIS) for dynamically selecting relevant features, adapting to evolving ransomware behaviors and reducing computational overhead; and (3) a Deep Belief Network (DBN) detection architecture, trained on the augmented data and optimized with Uncertainty-Aware Dynamic Early Stopping (UA-DES) to prevent overfitting. The model demonstrates a 4% improvement in detection accuracy (from 90% to 94%) through synthetic data generation and reduces false positives from 15.4% to 14%. The IMIS technique further increases accuracy to 96% while reducing false positives. The UA-DES optimization boosts accuracy to 98.6% and lowers false positives to 10%. Overall, this framework effectively addresses the challenges posed by evolving ransomware, significantly enhancing detection accuracy and reliability.

The rapid convergence of the Internet of Things (IoT) and cloud computing has intensified reliance on multi-tenancy, a model that enables resource sharing to enhance scalability and reduce costs. However, this shared infrastructure introduces significant security vulnerabilities, particularly at the intersection of IoT's resource-constrained devices and the cloud shared environment. While existing literature has addressed IoT or cloud security separately, a significant research gap exists in analyzing the specific risks of multi-tenancy in these integrated systems. This review synthesizes recent research on mitigation techniques to address security and privacy challenges in multi-tenant IoT-cloud environments. We provide a comprehensive classification of threats, including inter-tenant data leakage, side-channel vulnerabilities, and privilege escalation. Our analysis reveals a persistent security-performance trade-off that limits the widespread adoption of robust defenses in resource-constrained IoT environments. Current mitigation techniques, including access control models and AI-driven detection systems, incur significant computational overhead. This makes them impractical for numerous IoT applications with constrained processing and energy resources. This review analyzes the limitations of existing approaches and identifies key architectural gaps. In this paper, we present a roadmap of emerging solutions to resolve this security-performance trade-off. This work emphasizes the integration of Zero Trust Architectures (ZTA) for continuous verification, adaptive AI for real-time threat detection, blockchain for immutable audit trails, and the adoption of Post-Quantum Cryptography (PQC) as essential strategies to secure the next generation of mul-ti-tenant IoT-cloud infrastructures.

There has been a significant increase in the inclusion of the Internet of Things (IoT) into systems that affect everyone daily. The lessons learned from past experiences (i.e., case studies) can give a clearer understanding of the various options useful for potential improvements. Moreover, given the diversity of projects across privately funded versus government-funded, we took an independent, objective and scientific approach to understanding the differences comparatively. Starting with the funding sources and feature rationale, a clearer picture has emerged. Consequently, the composite picture enables a different, perhaps more valuable, baseline from which to develop future smart city strategies. Moreover, there can be many positives and negatives to the way these projects were implemented and carried out (i.e., commercial and political stakes). This study was limited from the post-implementation perspective, which emphasizes success stories, an inherent bias, as well as a disparity in level-of-funding available for each of the projects. Nevertheless, we have provided an original and consolidated perspective of the project outcomes and results. This represents a useful opportunity to explore an array of Smart City implementations that depend on evolving IoT technology solutions. Unfortunately, not all solutions being employed today are interoperable and/or extensible in an open systems sense. Modification, extensions, and support toward addressing emerging needs and achieving better more sustainable community mobility therefore may be disadvantaged and/or impeded; Thus, to better manage the expectations of Smart City planners and developers, this article has assessed and organized the outcomes of the eight projects posthumously and comparatively.

Early detection of ransomware attacks is critical for minimizing the potential damage caused by these malicious attacks. Feature selection plays a significant role in the development of an efficient and accurate ransomware early detection model. In this paper, we propose an enhanced Mutual Information Feature Selection (eMIFS) technique that incorporates a normalized hyperbolic function for ransomware early detection models. The normalized hyperbolic function is utilized to address the challenge of perceiving common characteristics among features, particularly when there are insufficient attack patterns contained in the dataset. The Term Frequency–Inverse Document Frequency (TF–IDF) was used to represent the features in numerical form, making it ready for the feature selection and modeling. By integrating the normalized hyperbolic function, we improve the estimation of redundancy coefficients and effectively adapt the MIFS technique for early ransomware detection, i.e., before encryption takes place. Our proposed method, eMIFS, involves evaluating candidate features individually using the hyperbolic tangent function (tanh), which provides a suitable representation of the features’ relevance and redundancy. Our approach enhances the performance of existing MIFS techniques by considering the individual characteristics of features rather than relying solely on their collective properties. The experimental evaluation of the eMIFS method demonstrates its efficacy in detecting ransomware attacks at an early stage, providing a more robust and accurate ransomware detection model compared to traditional MIFS techniques. Moreover, our results indicate that the integration of the normalized hyperbolic function significantly improves the feature selection process and ultimately enhances ransomware early detection performance.

The distributed denial of service (DDoS) attack is one of the most pernicious threats in cyberspace. Catastrophic failures over the past two decades have resulted in catastrophic and costly disruption of services across all sectors and critical infrastructure. Machine-learning-based approaches have shown promise in developing intrusion detection systems (IDSs) for detecting cyber-attacks, such as DDoS. Herein, we present a solution to detect DDoS attacks through an ensemble-based machine learning approach that combines supervised and unsupervised machine learning ensemble frameworks. This combination produces higher performance in detecting known DDoS attacks using supervised ensemble and for zero-day DDoS attacks using an unsupervised ensemble. The unsupervised ensemble, which employs novelty and outlier detection, is effective in identifying prior unseen attacks. The ensemble framework is tested using three well-known benchmark datasets, NSL-KDD, UNSW-NB15, and CICIDS2017. The results show that ensemble classifiers significantly outperform single-classifier-based approaches. Our model with combined supervised and unsupervised ensemble models correctly detects up to 99.1% of the DDoS attacks, with a negligible rate of false alarms.

Content-adaptive image steganography based on minimizing the additive distortion function and Generative Adversarial Networks (GAN) is a promising trend. This approach can quickly generate an embedding probability map and has a higher security performance than hand-crafted methods. However, existing works have ignored the semantic information between neighbouring pixels and the NaN-loss scenarios, which leads to improper convergence. Such cases will degrade the generated Stego images' quality, decreasing the secret payload's security. FT_GAN performance, which incorporates feature reuse in generator architecture, has been investigated by proposing the FC_DenseNet-based generator herein. This investigation explores the superior semantic segmentation capabilities of FC_DenseNet, including feature reuse, implicit deep supervision, and the vanishing gradient problem alleviation of DenseNet, toward enhancing visual results, increasing security performance, and accelerating training. The ability to maintain high-quality visual characteristics and robust security even in resource-constrained environments, such as Internet of Things (IoT) contexts, demonstrates the practical benefits of this approach. The qualitative analysis of the visual results regarding the texture regions' localization and intensity exhibited augmented visual quality. Moreover, an improvement in the security attribute of 0.66% has also been demonstrated regarding average detection errors made by the SRM_EC Steganalyzer across all target payloads.