Intellectual Feature Ranking Model with Correlated Feature Set based Malware Detection in Cloud environment using Machine Learning

Malware detection for cloud systems has been studied extensively, and many different approaches have been developed and implemented in an effort to stay ahead of this ever-evolving threat. Malware refers to any programme or defect that is designed to duplicate itself or cause damage to the system's hardware or software. These attacks are designed specifically to cause harm to operational systems, but they are invisible to the human eye. One of the most exciting developments in data storage and service delivery today is cloud computing. There are significant benefits to be gained over more conventional protection methods by making use of this fast evolving technology to protect computer-based systems from cyber-related threats. Assets to be secured may reside in any networked computing environment, including but not limited to Cyber Physical Systems (CPS), critical systems, fixed and portable computers, mobile devices, and the Internet of Things (IoT). Malicious software or malware refers to any programme that intentionally compromises a computer system in order to compromise its security, privacy, or availability. A cloud-based intelligent behavior analysis model for malware detection system using feature set is proposed to identify the ever-increasing malware attacks. The suggested system begins by collecting malware samples from several virtual machines, from which unique characteristics can be extracted easily. Then, the malicious and safe samples are separated using the features provided to the learning-based and rule-based detection agents. To generate a relevant feature set for accurate malware detection, this research proposes an Intellectual Feature Ranking Model with Correlated Feature Set (IFR-CFS) model using enhanced logistic regression model for accurate detection of malware in the cloud environment. The proposed model when compared to the traditional feature selection model, performs better in generation of feature set for accurate detection of malware

Genome-wide non-CpG methylation of the host genome during M. tuberculosis infection

A mammalian cell utilizes DNA methylation to modulate gene expression in response to environmental changes during development and differentiation. Aberrant DNA methylation changes as a correlate to diseased states like cancer, neurodegenerative conditions and cardiovascular diseases have been documented. Here we show genome-wide DNA methylation changes in macrophages infected with the pathogen M. tuberculosis. Majority of the affected genomic loci were hypermethylated in M. tuberculosis infected THP1 macrophages. Hotspots of differential DNA methylation were enriched in genes involved in immune response and chromatin reorganization. Importantly, DNA methylation changes were observed predominantly for cytosines present in non-CpG dinucleotide context. This observation was consistent with our previous finding that the mycobacterial DNA methyltransferase, Rv2966c, targets non-CpG dinucleotides in the host DNA during M. tuberculosis infection and reiterates the hypothesis that pathogenic bacteria use non-canonical epigenetic strategies during infection

SARS-CoV-2 B.1.617.2 Delta variant replication and immune evasion

Abstract: The B.1.617.2 (Delta) variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first identified in the state of Maharashtra in late 2020 and spread throughout India, outcompeting pre-existing lineages including B.1.617.1 (Kappa) and B.1.1.7 (Alpha)1. In vitro, B.1.617.2 is sixfold less sensitive to serum neutralizing antibodies from recovered individuals, and eightfold less sensitive to vaccine-elicited antibodies, compared with wild-type Wuhan-1 bearing D614G. Serum neutralizing titres against B.1.617.2 were lower in ChAdOx1 vaccinees than in BNT162b2 vaccinees. B.1.617.2 spike pseudotyped viruses exhibited compromised sensitivity to monoclonal antibodies to the receptor-binding domain and the amino-terminal domain. B.1.617.2 demonstrated higher replication efficiency than B.1.1.7 in both airway organoid and human airway epithelial systems, associated with B.1.617.2 spike being in a predominantly cleaved state compared with B.1.1.7 spike. The B.1.617.2 spike protein was able to mediate highly efficient syncytium formation that was less sensitive to inhibition by neutralizing antibody, compared with that of wild-type spike. We also observed that B.1.617.2 had higher replication and spike-mediated entry than B.1.617.1, potentially explaining the B.1.617.2 dominance. In an analysis of more than 130 SARS-CoV-2-infected health care workers across three centres in India during a period of mixed lineage circulation, we observed reduced ChAdOx1 vaccine effectiveness against B.1.617.2 relative to non-B.1.617.2, with the caveat of possible residual confounding. Compromised vaccine efficacy against the highly fit and immune-evasive B.1.617.2 Delta variant warrants continued infection control measures in the post-vaccination era

Transmission of B.1.617.2 Delta variant between vaccinated healthcare workers

AbstractBreakthrough infections with SARS-CoV-2 Delta variant have been reported in doubly-vaccinated recipients and as re-infections. Studies of viral spread within hospital settings have highlighted the potential for transmission between doubly-vaccinated patients and health care workers and have highlighted the benefits of high-grade respiratory protection for health care workers. However the extent to which vaccination is preventative of viral spread in health care settings is less well studied. Here, we analysed data from 118 vaccinated health care workers (HCW) across two hospitals in India, constructing two probable transmission networks involving six HCWs in Hospital A and eight HCWs in Hospital B from epidemiological and virus genome sequence data, using a suite of computational approaches. A maximum likelihood reconstruction of transmission involving known cases of infection suggests a high probability that doubly vaccinated HCWs transmitted SARS-CoV-2 between each other and highlights potential cases of virus transmission between individuals who had received two doses of vaccine. Our findings show firstly that vaccination may reduce rates of transmission, supporting the need for ongoing infection control measures even in highly vaccinated populations, and secondly we have described a novel approach to identifying transmissions that is scalable and rapid, without the need for an infection control infrastructure.</jats:p