Krishna Dagli writes to mention a Register article about a mechanical process for analyzing malware. Using an automated system, researchers are able to more accurately classify the often randomly-named bots and viruses that plague us. From the article: "The researchers modeled a piece of malicious software as the series of actions that the software takes at the operating system level. Referred to as 'events' in a paper written by Lee and anti-malware program team manager Jigar Mody, the actions can include data copying, changing registry keys and opening network connections. The researchers then trained a recognition engine using an adaptive clustering algorithm - similar to self-organising maps - and classified a previously unseen subset of malware using the trained system. Using more clusters typically resulted in better classification. When the software samples were classified based on 100 events, accuracy fell below 80 per cent, while classification based on 500 and 1,000 events typically has accuracy rates above 90 per cent."