Automatic Content Analysis

Malicious software (i.e. malware) has become a severe threat to interconnected computer systems for decades and has caused billions of dollars damages each year. A large volume of new malware samples are discovered daily. Even worse, malware is rapidly evolving becoming more sophisticated and evasive to strike against current malware analysis and defense systems. Automatic Malware Analysis presents a virtualized malware analysis framework that addresses common challenges in malware analysis. In regards to this new analysis framework, a series of analysis techniques for automatic malware analysis is developed. These techniques capture intrinsic characteristics of malware, and are well suited for dealing with new malware samples and attack mechanisms.

This thesis reports on the development of a fully integrated and automated microsystem consisting of low-cost, disposable plastic chips for DNA extraction and PCR amplification, combined with a reusable glass capillary array electrophoresis chip, which can be employed in a modular-based format for genetic analysis. In the thesis, DNA extraction is performed by adopting a filter paper-based method, followed by an “in-situ” PCR carried out directly in the same reaction chamber of the chip without elution.

This book describes effective methods for automatically analyzing a sentence, based on the syntactic and semantic characteristics of the elements that form it. To tackle ambiguities, the authors use selectional preferences (SP), which measure how well two words fit together semantically in a sentence.

Automatic modulation recognition is a rapidly evolving area of signal analysis. In recent years, interest from the academic and military research institutes has focused around the research and development of modulation recognition algorithms.