Enhancing Cloud Computing Analysis: A CCE-Based HTTP-GET Log Dataset

The Hypertext Transfer Protocol (HTTP) is a common target of distributed denial-of-service (DDoS) attacks in today’s cloud computing environment (CCE). However, most existing datasets for Intrusion Detection System (IDS) evaluations are not suitable for CCEs. They are either self-generated or are not representative of CCEs, leading to high false alarm rates when used in real CCEs. Moreover, many datasets are inaccessible due to privacy and copyright issues. Therefore, we propose a publicly available benchmark dataset of HTTP-GET flood DDoS attacks on CCEs based on an actual private CCE. The proposed dataset has two advantages: (1) it uses CCE-based features, and (2) it meets the criteria for trustworthy and valid datasets. These advantages enable reliable IDS evaluations, tuning, and comparisons. Furthermore, the dataset includes both internal and external HTTP-GET flood DDoS attacks on CCEs. This dataset can facilitate research in the field and enhance CCE security against DDoS attacks

IoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning

Breast cancer is often a fatal disease that has a substantial impact on the female mortality rate. Rapidly spreading breast cancer is due to the abnormal growth of malignant cells in the breast. Early detection of breast cancer can increase treatment opportunities and patient survival rates. Various screening methods with computer-aided detection systems have been developed for the effective diagnosis and treatment of breast cancer. Image data plays an important role in the medical and health industry. Features are extracted from image datasets through deep learning, as deep learning techniques extract features more accurately and rapidly than other existing methods. Deep learning effectively assists existing methods, such as mammogram screening and biopsy, in examining and diagnosing breast cancer. This paper proposes an Internet of Medical Things (IoMT) cloud-based model for the intelligent prediction of breast cancer stages. The proposed model is employed to detect breast cancer and its stages. The experimental results demonstrate 98.86% and 97.81% accuracy for the training and validation phases, respectively. In addition, they demonstrate accuracies of 99.69%, 99.32%, 98.96%, and 99.32% for detecting ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma. The results of the proposed intelligent prediction of breast cancer stages empowered with the deep learning (IPBCS-DL) model exhibits higher accuracy than existing state-of-the-art methods, indicating its potential to lower the breast cancer mortality rate