Improved Linear Cryptanalysis of Reduced-round SIMON

SIMON is a family of ten lightweight block ciphers published by Beaulieu et al.\ from U.S. National Security Agency (NSA). In this paper we investigate the security of SIMON against different variants of linear cryptanalysis techniques, i.e.\ classical and multiple linear cryptanalysis and linear hulls. We present a connection between linear- and differential characteristics as well as differentials and linear hulls in SIMON. We employ it to adapt the current known results on differential cryptanalysis of SIMON into the linear setting. In addition to finding a linear approximation with a single characteristic, we show the effect of the linear hulls in SIMON by finding better approximations that enable us to improve the previous results. Our best linear cryptanalysis employs average squared correlation of the linear hull of SIMON based on correlation matrices. The result covers 21 out of 32 rounds of SIMON32/64 with time and data complexity $2^{54.56}$ and $2^{30.56}$ respectively. We have implemented our attacks for small scale variants of SIMON and our experiments confirm the theoretical biases and correlation presented in this work. So far, our results are the best known with respect to linear cryptanalysis for any variant of SIMON

Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis

Sepsis is a major global health concern causing high morbidity and mortality rates. Our study utilized a Meningococcal Septic Shock (MSS) temporal dataset to investigate the correlation between gene expression (GE) changes and clinical features. The research used Weighted Gene Co-expression Network Analysis (WGCNA) to establish links between gene expression and clinical parameters in infants admitted to the Pediatric Critical Care Unit with MSS. Additionally, various machine learning (ML) algorithms, including Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Artificial Neural Network (ANN) were implemented to predict sepsis survival. The findings revealed a transition in gene function pathways from nuclear to cytoplasmic to extracellular, corresponding with Pediatric Logistic Organ Dysfunction score (PELOD) readings at 0, 24, and 48 h. ANN was the most accurate of the six ML models applied for survival prediction. This study successfully correlated PELOD with transcriptomic data, mapping enriched GE modules in acute sepsis. By integrating network analysis methods to identify key gene modules and using machine learning for sepsis prognosis, this study offers valuable insights for precision-based treatment strategies in future research. The observed temporal-spatial pattern of cellular recovery in sepsis could prove useful in guiding clinical management and therapeutic interventions

Advancing the Understanding of Clinical Sepsis Using Gene Expression-Driven Machine Learning to Improve Patient Outcomes

Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of Machine Learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. ML has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management

Data security techniques in cloud computing based on machine learning algorithms and cryptographic algorithms: Lightweight algorithms and genetics algorithms

Cloud computing (CC) refers to the on-demand availability of network resources, particularly data storage and processing power, without requiring special or direct administration by users. CC, which just made its debut as a collection of public and private data centers, provides clients with a unified platform throughout the Internet. Cloud computing has revolutionized the world, opening up new horizons with bright potential due to its performance, accessibility, low cost, and many other benefits. Due to the exponential rise of cloud computing, systems based on cloud computing now require an effective data security mechanism. Comprehensive security policies, corporate security culture, and cloud security solutions are used to ensure the level of cloud data security. Many techniques exist to protect data communication in the cloud environment, including encryption. Encryption algorithms play an important role in information security systems and various cloud computing-based systems. Current researchers have focused on lightweight cryptography, genetics-based cryptography, and machine learning (ML) algorithms for security in CC. This review study analyses CC security threats, problems, and solutions that use one or more algorithms. The work discusses several lightweight cryptographies, genetics-based cryptography and different ML algorithms that are used to overcome cloud security issues, including supervised, unsupervised, semi-supervised, and reinforcement learning. Moreover, we enlist future research directions to secure CC models

Access to information and support for health: Some potential issues and solutions for an ageing population

Computer illiteracy is diminishing as a new generation of retirees become the younger old and display more up-to-date knowledge and skills. However, there are questions about whether this group will be able to continue to update their skills as they get older, and whether it is appropriate to develop technology solutions specifically for this age group or to concentrate on accessible designs for the whole population. We propose that older people may be empowered through involvement in the design and provision of accessible information and technology solutions and through training opportunities in information seeking skills. Access, involvement and training need to be provided in everyday locations, and training needs to be closely related to people's physical, cognitive and information needs and those of the particular communities where they live. These issues are explored using evidence from a number of research projects conducted by the authors