Malware injection attacks are among the most sophisticated and elusive threats in cybersecurity, characterised by their capacity for privilege escalation, obfuscation, and the ability to deceive antivirus software. This paper introduces a multi-layer architecture, featuring innovative deep neural networks, fast Fourier convolution , and association rule mining strategies, designed for the early detection and defusal of malware injection attacks. We then propose a proactive AI-enabled malware detection platform, DeepRadar , as a novel real-world defence mechanism. This early warning functionality capable of anticipating the attack a few cycles before occurrence represents a novel idea and unique approach to detecting malware injection attacks. The experimental results validate DeepRadar’s superior performance compared to not only previous related studies but also a standard benchmark of well-reputed antivirus applications under various scenarios and accredited datasets, including heavily obfuscated emerging malware variants and adversarial samples. It demonstrates higher Accuracy, F-score, ROC, and AUC metrics in early detection and classification of malware injection attacks while DeepRadar consumes significantly fewer system resources, including processor and memory during long-term scalable operation. The proposed early warning system succeeded in repelling up to 97.2% of attacks before malware could complete their malicious sequence. Lastly, the evaluation results were substantiated by formal statistical analysis using Friedman and Wilcoxon tests. The findings of this research and DeepRadar’s runtime scanner provide vital early warnings against stealthy malware and injection attacks, offering robust protection for sensitive systems and critical infrastructure.

With an estimated five million fatal cases each year, lung cancer is one of the significant causes of death worldwide. Lung diseases can be diagnosed with a Computed Tomography (CT) scan. The scarcity and trustworthiness of human eyes is the fundamental issue in diagnosing lung cancer patients. The main goal of this study is to detect malignant lung nodules in a CT scan of the lungs and categorize lung cancer according to severity. In this work, cutting-edge Deep Learning (DL) algorithms were used to detect the location of cancerous nodules. Also, the real-life issue is sharing data with hospitals around the world while bearing in mind the organizations' privacy issues. Besides, the main problems for training a global DL model are creating a collaborative model and maintaining privacy. This study presented an approach that takes a modest amount of data from multiple hospitals and uses blockchain-based Federated Learning (FL) to train a global DL model. The data were authenticated using blockchain technology, and FL trained the model internationally while maintaining the organization's anonymity. First, we presented a data normalization approach that addresses the variability of data obtained from various institutions using various CT scanners. Furthermore, using a CapsNets method, we classified lung cancer patients in local mode. Finally, we devised a way to train a global model cooperatively utilizing blockchain technology and FL while maintaining anonymity. We also gathered data from real-life lung cancer patients for testing purposes. The suggested method was trained and tested on the Cancer Imaging Archive (CIA) dataset, Kaggle Data Science Bowl (KDSB), LUNA 16, and the local dataset. Finally, we performed extensive experiments with Python and its well-known libraries, such as Scikit-Learn and TensorFlow, to evaluate the suggested method. The findings showed that the method effectively detects lung cancer patients. The technique delivered 99.69 % accuracy with the smallest possible categorization error.

k-NN, as one of the well-employed classification algorithms, severely suffers from a computationally intensive nature. This paper exploits the parallelism and digit level pipelining opportunities via FPGA devices and Online arithmetic to offer an efficient k-NN FPGA implementation. All the required operations for computing distances and sorting are applied to serially coming data. Moreover, we dynamically terminate the unnecessary computations once they are detected. To the best of our knowledge, the proposed k-NN implementation is the first one that used FPGA and Online arithmetic effectively. It provides up to 34% speedup compared to the best state-of-the-art design.

K-means clustering is the most well-known unsupervised learning method that partitions the input dataset into$K$clusters based on the similarity between the data samples. In this paper, to achieve an energy-efficient implementation without sacrificing performance, we take advantage of massive parallelism and digit-level pipelining via FPGA and the most-significant digit first arithmetic. Having the result of the most-significant digits in advance provides the possibility of early termination for unnecessary computations and fetching just the required most-significant part of data points from memory. This early termination technique significantly increases performance and decreases energy consumption. Our experimental results from various datasets and comparisons with the state-of-the-art FPGA accelerators indicate that our proposed design has effectively reduced energy consumption without any performance loss.

Abstract Cloud computing is a rapidly evolving computational technology. It is a distributed computational system that offers dynamically scaled computational resources, such as processing power, storage, and applications, delivered as a service through the Internet. Virtual machines (VMs) allocation is known as one of the most significant problems in cloud computing. It aims to find a suitable location for VMs on physical machines (PMs) to attain predefined aims. So, the main purpose is to reduce energy consumption and improve resource utilization. Because the VM allocation issue is NP‐hard, meta‐heuristic and heuristic methods are frequently utilized to address it. This paper presents an energy‐aware VM allocation method using the improved grey wolf optimization (IGWO) algorithm. Our key goals are to decrease both energy consumption and allocation time. The simulation outcomes from the MATLAB simulator approve the excellence of the algorithm compared to previous works.