Review of Computer Engineering Research

HLPMM–GRSPHTRU: An explainable cross-layer temporal framework for multi-stage attack detection in IoT-CPS

Mohan Kumar — 2026-04-22

The high growth rate of Internet of Things (IoT)-powered Cyber-Physical Systems (CPS) has led to advanced, multi-level cyber-attacks such as ransomware, distributed denial-of-service (DDoS), and malware, which tend to spread across various system levels over time. Current intrusion detection systems often fail to detect these cross-layer temporal dependencies and offer weak interpretability, limiting their trustworthiness in safety-critical CPS environments. To address these issues, this paper proposes an explainable cross-layer temporal correlation system for detecting multi-stage cyber-attacks in IoT-enabled CPS. The framework combines the Hidden Laguerre Polynomial Markov Model (HLPMM), a probabilistic sequence model that enables flexible state transition learning, with a Gated Rastrigin Sphere Penalized Hyperbolic Tangent Recurrent Unit (GRSPHTRU), an improved gated recurrent neural network designed for healthy temporal feature learning. Principal Griewank Component Analysis is employed for dimensionality reduction, while an adaptive density-based clustering mechanism groups behavioral patterns. Model transparency is achieved through a Shapley-based explainability module, and system integrity is maintained via blockchain-based tamper-resistant logging. The federated learning structure decentralizes training across multiple distributed CPS nodes, reducing raw data sharing and enhancing privacy. Experimental analysis using benchmark ransomware, malware, and CIC-DDoS2019 datasets demonstrates high performance, with detection accuracy and explainability fidelity reaching up to 99 percent compared to conventional RNN, LSTM, BiLSTM, and GRU models. Additionally, feature compression and federated aggregation significantly impact computational load and communication overhead, facilitating scalable deployment.

Reference-based super-resolution for remote sensing images using a hybrid edge-aware loss function

Rajalaxmi Padhy — 2026-04-23

Acquiring high-resolution images is crucial for accurate remote sensing analysis; however, such data are often limited by sensor constraints, atmospheric conditions, and acquisition costs. Reference-based super-resolution (RefSR) addresses this limitation by using auxiliary high-resolution images, but the presence of domain mismatches due to changes in illumination, viewpoint, and sensor characteristics severely limits its performance, often resulting in blurred edges and structural distortions. To overcome these problems, this paper presents a reference-based super-resolution framework that integrates a hybrid edge-aware loss function into a domain-adaptive transfer super-resolution architecture. The proposed method first employs grayscale transformation for domain matching, followed by Whitening and Coloring Transform and Phase Replacement for efficient domain adaptation and texture alignment. To supervise the edges and overall structure more closely during image reconstruction, the authors have combined Sobel and Laplacian edge constraints in a new hybrid loss function. Experiments on the DIV2K dataset using a 4× scaling factor reveal that the method presented in this paper consistently generates better results than the baseline DATSR model, with substantial improvements in PSNR and SSIM metrics and visually sharper, more structurally coherent images. Furthermore, qualitative analyses verify that the images obtained from super-resolution preserve better edges with less boundary blurring. This approach serves as an efficient and computationally feasible solution for improving image quality in situations of domain mismatch, making it suitable for high-resolution remote sensing applications such as urban monitoring, environmental analysis, and industrial innovation in line with sustainable development goals.