Machine Learning in Security Applications

TitleMachine Learning in Security Applications
Publication TypeBook Chapter
Year of Publication2013
AuthorsAriu, D, Corona, I, Tronci, R, Giacinto, G
EditorPerner, P
Book TitleMachine Learning, Software Engineering and Standardization
ISBN Number978-3-942952-20-0

One of the most important assets to be protected is information, as every aspect of the life of a society deeply depends on the available information. Nowadays information is stored, processed, and communicated by computers. It turns out that computers represent the most critical tool in modern society. A number of pro- tection mechanisms are available so far, such as antivirus software tools, and biome- tric access control systems. For their effectiveness, frequent updates are needed, due to the rapid evolution of attack patterns. In fact, attacks are often devised and spread by running computer programs, which can produce new effective attacks in a short time frame. It turns out that machine learning techniques with their generalization capability are one of the favorite approaches to deploy protection and attack detection mechanisms. In this paper, we discuss the approaches that should be followed when devising machine learning techniques for security applications. In particular, we will focus on testing methodologies, performance measures, and techniques aimed at reducing the intrinsic variability of performance that often machine learning application exhibit in real-world scenarios.

Citation Key 1346