N-gram density based malware detection

N-gram analysis is an approach that investigates the structure of a program using bytes, characters or text strings. This research uses dynamic analysis to investigate malware detection using a classification approach based on N-gram analysis. The motivation for this research is to find a subset of Ngram features that makes a robust indicator of malware. The experiments within this paper represent programs as N-gram density histograms, gained through dynamic analysis. A Support Vector Machine (SVM) is used as the program classifier to determine the ability of N-grams to correctly determine the presence of malicious software. The preliminary findings show that an N-gram size N=3 and N=4 present the best avenues for further analysis.

Lipopolysaccharide modification in Gram-negative bacteria during chronic infection

The Gram-negative bacterial lipopolysaccharide (LPS) is a major component of the outer membrane that plays a key role in host-pathogen interactions with the innate immune system. During infection, bacteria are exposed to a host environment that is typically dominated by inflammatory cells and soluble factors, including antibiotics, which provide cues about regulation of gene expression. Bacterial adaptive changes including modulation of LPS synthesis and structure are a conserved theme in infections, irrespective of the type or bacteria or the site of infection. In general, these changes result in immune system evasion, persisting inflammation, and increased antimicrobial resistance. Here, we review the modifications of LPS structure and biosynthetic pathways that occur upon adaptation of model opportunistic pathogens (Pseudomonas aeruginosa, Burkholderia cepacia complex bacteria, Helicobacter pylori and Salmonella enterica) to chronic infection in respiratory and gastrointestinal sites. We also discuss the molecular mechanisms of these variations and their role in the host-pathogen interaction.

Potential strategies for the eradication of multi-drug resistant Gram-negative bacterial infections

Antimicrobial resistance is one of the leading threats to society. The increasing burden of multidrug-resistant Gram-negative infection is particularly concerning as such bacteria are demonstrating resistance to nearly all currently licensed therapies. Various strategies have been hypothesized to treat multidrug-resistant Gram-negative infections including: targeting the Gram-negative outer membrane; neutralization of lipopolysaccharide; inhibition of bacterial efflux pumps and prevention of protein folding. Silver and silver nanoparticles, fusogenic liposomes and nanotubes are potential strategies for extending the activity of licensed, Gram-positive selective, antibiotics to Gram-negatives. This may serve as a strategy to fill the current void in pharmaceutical development in the short term. This review outlines the most promising strategies that could be implemented to solve the threat of multidrug-resistant Gram-negative infections