Transmission electron microscopy investigation of substitution reactions from carbon nanotube template to silicon carbide nanowires

Substitution reactions between multiwalled carbon nanotubes and silicon monoxide vapour have been investigated using transmission electron microscopy. Different reactions occurred inside the multiwalled nanotubes and on the nanotube external surfaces, resulting in the formation of silicon carbide nanowires with a core–shell structure. The substitution reaction process and end products are strongly affected by nanotube structures and a ball milling treatment of the starting materials.

Boron carbide nanowires with uniform CNχ coatings

Boron carbide nanowires with uniform carbon nitride coating layers were synthesized on a silicon substrate using a simple thermal process. The structure and morphology of the as-synthesized nanowires were characterized using x-ray diffraction, scanning and transmission electron microscopy and electron energy loss spectroscopy. A correlation between the surface smoothness of the nanowire sidewalls and their lateral sizes has been observed and it is a consequence of the anisotropic formation of the coating layers. A growth mechanism is also proposed for these growth phenomena.

An automated classification system based on the strings of trojan and virus families

Classifying malware correctly is an important research issue for anti-malware software producers. This paper presents an effective and efficient malware classification technique based on string information using several wellknown classification algorithms. In our testing we extracted the printable strings from 1367 samples, including unpacked trojans and viruses and clean files. Information describing the printable strings contained in each sample was input to various classification algorithms, including treebased classifiers, a nearest neighbour algorithm, statistical algorithms and AdaBoost. Using k-fold cross validation on the unpacked malware and clean files, we achieved a classification accuracy of 97%. Our results reveal that strings from library code (rather than malicious code itself) can be utilised to distinguish different malware families.