Towards integrated anti-microbial capabilities: Novel bio-fouling resistant membranes by high velocity embedment of silver particles

Biofilm formation on membranes during water desalination operation and pre-treatments limits performance and causes premature membrane degradation. Here, we apply a novel surface modification technique to incorporate anti-microbial metal particles into the outer layer of four types of commercial polymeric membranes by cold spray. The particles are anchored on the membrane surface by partial embedment within the polymer matrix. Although clear differences in particle surface loadings and response to the cold spray were shown by SEM, the hybrid micro-filtration and ultra-filtration membranes were found to exhibit excellent anti-bacterial properties. Poly(sulfone) ultra-filtration membranes were used as for cross-flow filtration of Escherichia coli bacteria solutions to investigate the impact of the cold spray on the material[U+05F3]s integrity. The membranes were characterized by SEM-EDS, FT-IR and TGA and challenged in filtration tests. No bacteria passed through the membrane and filtrate water quality was good, indicating the membranes remained intact. No intact bacteria were found on hybrid membranes, loaded with up to 15. wt% silver, indicating the treatment was lysing bacteria on contact. However, permeation of the hybrid membranes was found to be reduced compared to control non-modified poly(sulfone) membranes due to the presence of the particles across the membrane material. The implementation of cold spray technology for the modification of commercial membrane products could lead to significant operational savings in the field of desalination and water pre-treatments.

Control flow-based malware variant detection

Static detection of malware variants plays an important role in system security and control flow has been shown as an effective characteristic that represents polymorphic malware. In our research, we propose a similarity search of malware to detect these variants using novel distance metrics. We describe a malware signature by the set of control flowgraphs the malware contains. We use a distance metric based on the distance between feature vectors of string-based signatures. The feature vector is a decomposition of the set of graphs into either fixed size k-subgraphs, or q-gram strings of the high-level source after decompilation. We use this distance metric to perform pre-filtering. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flowgraphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms. © 2013 IEEE.