Introducing a Machine Learning Password Metric Based on EFKM Clustering Algorithm

we introduce a password strength metric using Enhanced Fuzzy K-Means clustering algorithm (EFKM henceforth). The EFKM is trained on the OWASP list of 10002 weak passwords. After that, the optimized centroids are maximized to develop a password strength metric. The resulting meter was validated by contrasting with three entropy-based metrics using two datasets: the training dataset (OWASP) and a dataset that we collected from github website that contains 5189451 leaked passwords. Our metric is able to recognize all the passwords from the OWASP as weak passwords only. Regarding the leaked passwords, the metric recognizes almost the entire set as weak passwords. We found that the results of the EFKM-based metric and the entropy-based meters are consistent. Hence the EFKM metric demonstrates its validity as an efficient password strength checker

Enhancing Steganography by Image Segmentation and Multi-level Deep Hiding

In this paper, we present Modify Deep Hiding Extraction Algorithm (MDHEA) that is a steganography algorithm with Multi-Level Steganography (MLS) and color image segmentation. Through experimental results, MDHEA shows improvement in the results of previous works by securing encrypted secret data against attacks. We use segmentation to choose the appropriate segment, pass it on the cover image, calculate the value of the change at the pixel of the segment and select the best segment and its location in the cover image based on the least effect. MDHEA applies multi-level steganography to hide the confidential data in color images to ensure the integrity of the hidden data and obtain the largest volume of hidden data without distorting the image of the stego image. To reduce distortion in the cover image due to hiding a large amount of secret data and obtaining a high-quality stego image after hiding the secret data, we implement the Blue Smoothing Algorithm (BSA) to achieve smoothing the largest possible number of pixels in the image

Reducing Power Consumption in Hexagonal Wireless Sensor Networks Using Efficient Routing Protocols

Power consumption and network lifetime are vital issues in wireless sensor network (WSN) design. This motivated us to find innovative mechanisms that help in reducing energy consumption and prolonging the lifetime of such networks. In this paper, we propose a hexagonal model for WSNs to reduce power consumption when sending data from sensor nodes to cluster heads or the sink. Four models are proposed for cluster head positioning and the results were compared with well-known models such as Power Efficient Gathering In Sensor Information Systems (PEGASIS) and Low-Energy Adaptive Clustering Hierarchy (LEACH). The results showed that the proposed models reduced WSN power consumption and network lifetime

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population