Ashfaq Hussain Farooqi

The rapid evolution of digital communication demands a paradigm shift in image steganography, moving beyond conventional data embedding to fully reversible concealment that ensures seamless integration while preserving the original cover. However, spatial-domain techniques remain inherently flawed—repetitive carrier image usage exposes hidden data to statistical detection, while deterministic extraction mechanisms violate Kerckhoff’s principle, enabling unauthorized recovery. Reliance on encryption, flawed key management, and misplaced emphasis on imperceptibility metrics further compromise true undetectability. This research introduces a provably robust steganographic framework that advances spatial-domain security through three foundational innovations: (1) Cryptographic reversibility—unlike conventional LSB methods that permanently alter cover media, our SHA-256–modulated entropy embedding enables lossless payload extraction and perfect cover recovery without auxiliary data; (2) Statistical undetectability—by integrating inverse modulo-5 arithmetic (E(x, k) = (x + k) mod 5) with dual-channel pixel diffusion, we eliminate detectable artifacts in co-occurrence matrices and histograms, achieving zero discriminability (AUC = 0.5) against ML-based steganalysis; and (3) Adaptive capacity hardening—a dynamic payload distribution system that simultaneously maximizes embedding density (1–100% pixel utilization) while resisting known-cover attacks through key-driven randomization. These mechanisms collectively establish mathematically verifiable robustness: reversibility is guaranteed by the bijective properties of our modular transformations (E⁻¹(y, k) = (y−k) mod 5), undetectability is proven via entropy preservation (ΔH < 0.02), and security is enforced through Kerckhoffs-compliant key derivation. Empirical validation confirms superiority over existing methods in PSNR (> 53 dB), structural fidelity (SSIM > 0.999), and attack resilience—redefining the theoretical limits of secure data hiding.

Deep learning has significantly advanced medical imaging, particularly computed tomography (CT), which is vital for diagnosing heart and cancer patients, evaluating treatments, and tracking disease progression. High-quality CT images enhance clinical decision-making, making image reconstruction a key research focus. This study develops a framework to improve CT image quality while minimizing reconstruction time. The proposed four-step medical image analysis framework includes reconstruction, preprocessing, segmentation, and image description. Initially, raw projection data undergoes reconstruction via a Radon transform to generate a sinogram, which is then used to construct a CT image of the pelvis. A convolutional neural network (CNN) ensures high-quality reconstruction. A bilateral filter reduces noise while preserving critical anatomical features. If required, a medical expert can review the image. The K-means clustering algorithm segments the preprocessed image, isolating the pelvis and removing irrelevant structures. Finally, the FuseCap model generates an automated textual description to assist radiologists. The framework’s effectiveness is evaluated using peak signal-to-noise ratio (PSNR), normalized mean square error (NMSE), and structural similarity index measure (SSIM). The achieved values—PSNR 30.784, NMSE 0.032, and SSIM 0.877—demonstrate superior performance compared to existing methods. The proposed framework reconstructs high-quality CT images from raw projection data, integrating segmentation and automated descriptions to provide a decision-support tool for medical experts. By enhancing image clarity, segmenting outputs, and providing descriptive insights, this research aims to reduce the workload of frontline medical professionals and improve diagnostic efficiency. Graphical Abstract

Vehicle-to-everything (V2X) communication systems are increasingly susceptible to cyber-physical threats that exploit trust assumptions, coordination latency, and semantic inconsistencies across agents. These vulnerabilities, particularly in dense or adversarial environments, undermine the reliability of cooperative perception, anomaly detection, and safety-critical maneuver execution. This paper presents CR-V2XR, a cross-layer, federated, and trust-aware coordination framework designed to enhance resilience in connected and autonomous vehicular networks. CR-V2XR integrates multi-modal anomaly detection with delay-sensitive trust estimation using features extracted from basic safety messages (BSMs), behavioral deviations, entropy shifts, and inter-vehicle trust validation. The architecture employs federated learning for distributed anomaly detection without centralized aggregation and uses a control layer that supports delay-aware trajectory selection. A multi-objective NSGA-III optimizer enables online trade-off adaptation across detection accuracy (DA), collision probability, and communication overhead. Simulations across eleven adversarial scenarios, including Sybil, wormhole, falsification, and replay attacks, demonstrate that CR-V2XR achieves 95% detection accuracy under worst-case attacks, reduces collision probability from 0.61 to 0.27 at 300 vehicles, maintains bounded delay, typically 45–60 ms under nominal load, and remains resilient under high-stress conditions with delays up to 180 ms and communication overhead (≤ 6.1 MB/s). Compared to centralized IDS and stateless baselines, CR-V2XR improves detection fidelity, scalability, and robustness under non-IID data and partial synchronization. These results establish CR-V2XR as a viable architecture for delay-constrained, trust-centric coordination in federated V2X environments subject to persistent adversarial threats.

The Internet of Medical Things (IoMT) enables real-time healthcare monitoring but faces challenges in energy efficiency, latency, security, and scalability. This paper presents a novel and comprehensive hierarchical data aggregation framework for the Internet of Medical Things (IoMT), optimized using a constrained multi-objective approach. Unlike existing methods that treat objectives in isolation, the model simultaneously minimizes energy consumption, latency, security risks, and authentication overhead under real-world constraints (500 mJ energy, 200 ms latency, 0.9 Sybil detection rate, 0.95 data integrity). Utilizing the Non-dominated Sorting Genetic Algorithm II (NSGA-II), a diverse Pareto front of solutions is generated, allowing adaptability to different healthcare scenarios. The framework is further validated through dual-environment simulations (MATLAB and NS-3) under realistic attack conditions (Sybil, spoofing, and DoS), demonstrating superior performance and robustness compared to centralized and fog-only baselines.s For instance, the system achieves up to 43% energy savings and 84% latency reduction. These results illustrate the framework’s practical viability for scalable, secure, and efficient IoMT deployments.

With the growing demand for delay-sensitive voice, video, and data-based applications, the challenge of efficient spectrum utilization is becoming more critical. To address the challenges of spectrum scarcity, the Federal Communications Commission (FCC) has authorized the sharing of the federally held Citizen Broadband Radio Service (CBRS) band, also known as the 3.5 GHz spectrum band, with commercial use. The 3.5 GHz spectrum band controlled by the spectrum access system (SAS) is shared with three types of users i.e., Incumbent users, Primary access licensee (PAL) users, and general authorized access (GAA) users as opportunistic users. In this paper we proposed the SAS-based channel allocation (SAS-CA) algorithm to optimize spectrum utilization and system performance, considering the potential risks of adding opportunistic GAA users. The proposed algorithm ensures low outage probability for PAL users and provides an optimized channel allocation strategy for GAA users when PAL users are inactive in PAL reserved channels. Additionally, a security module integrated with SAS enhances spectrum utilization and reduces interference levels. The algorithm’s efficiency is validated through multiple test sets, showing rapid convergence within 100 iterations. The integration of the security module results in a median spectrum utilization increase of 16 points and a notable reduction in interference levels, enhancing execution efficiency by approximately 37% compared to traditional methods. These findings demonstrate the substantial benefits of advanced allocation algorithms and robust security mechanisms in ensuring equitable access, stabilizing system performance, and optimizing spectrum utilization within the CBRS band.

In the context of 6G technology, the Internet of Everything aims to create a vast network that connects both humans and devices across multiple dimensions. The integration of smart healthcare, agriculture, transportation, and homes is incredibly appealing, as it allows people to effortlessly control their environment through touch or voice commands. Consequently, with the increase in Internet connectivity, the security risk also rises. However, the future is centered on a six-fold increase in connectivity, necessitating the development of stronger security measures to handle the rapidly expanding concept of IoT-enabled metaverse connections. Various types of attacks, often orchestrated using botnets, pose a threat to the performance of IoT-enabled networks. Detecting anomalies within these networks is crucial for safeguarding applications from potentially disastrous consequences. The voting classifier is a machine learning (ML) model known for its effectiveness as it capitalizes on the strengths of individual ML models and has the potential to improve overall predictive performance. In this research, we proposed a novel classification technique based on the DRX approach that combines the advantages of the Decision tree, Random forest, and XGBoost algorithms. This ensemble voting classifier significantly enhances the accuracy and precision of network intrusion detection systems. Our experiments were conducted using the NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets. The findings of our study show that the DRX-based technique works better than the others. It achieved a higher accuracy of 99.88% on the NSL-KDD dataset, 99.93% on the UNSW-NB15 dataset, and 99.98% on the CIC-IDS2017 dataset, outperforming the other methods. Additionally, there is a notable reduction in the false positive rates to 0.003, 0.001, and 0.00012 for the NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets.

Computed tomography (CT) is used in a wide range of medical imaging diagnoses. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy. In order to address these challenges, deep learning developments have the potential to improve the reconstruction of computed tomography images. In this regard, our research aim is to determine the techniques that are used for 3D deep learning in CT reconstruction and to identify the training and validation datasets that are accessible. This research was performed on five databases. After a careful assessment of each record based on the objective and scope of the study, we selected 60 research articles for this review. This systematic literature review revealed that convolutional neural networks (CNNs), 3D convolutional neural networks (3D CNNs), and deep learning reconstruction (DLR) were the most suitable deep learning algorithms for CT reconstruction. Additionally, two major datasets appropriate for training and developing deep learning systems were identified: 2016 NIH-AAPM-Mayo and MSCT. These datasets are important resources for the creation and assessment of CT reconstruction models. According to the results, 3D deep learning may increase the effectiveness of CT image reconstruction, boost image quality, and lower radiation exposure. By using these deep learning approaches, CT image reconstruction may be made more precise and effective, improving patient outcomes, diagnostic accuracy, and healthcare system productivity.

Cloud computing is perhaps the most enticing innovation in the present figuring situation. It gives an expense-effective arrangement by diminishing the enormous forthright expense of purchasing equipment foundations and processing power. Fog computing is an additional help to cloud infrastructure by utilizing a portion of the less-registered undertaking at the edge devices, reducing the end client’s reaction time, such as IoT. However, most of the IoT devices are resource-constrained, and there are many devices that cyber attacks could target. Cyber-attacks such as bottleneck, Dos, DDoS, and botnets are still significant threats in the IoT environment. Botnets are currently the most significant threat on the internet. A set of infected systems connected online and directed by an adversary to carry out malicious actions without authorization or authentication is known as a botnet. A botnet can compromise the system and steal the data. It can also perform attacks, like Phishing, spamming, and more. To overcome the critical issue, we exhibit a novel botnet attack detection approach that could be utilized in fog computing situations to dispense with the attack using the programmable nature of the software-defined network (SDN) environment. We carefully tested the most recent dataset for our proposed technique, standard and extended performance evaluation measures, and current DL models. To further illustrate overall performance, our findings are cross-validated. The proposed method performs better than previous ones in correctly identifying 99.98% of multi-variant sophisticated bot attacks. Additionally, the time of our suggested method is 0.022(ms), indicating good speed efficiency results.