Abstract-Decompilation of Java bytecode is the act of transforming Java bytecode to Java source code. Although easier than that of decompilation of machine code, problems still arise in Java bytecode decompilation. These include type inference of local variables and exception-handling.Since the last such evaluation (2003) several new commercial, free and open-source Java decompilers have appeared and some of the older ones have been updated.In this paper, we evaluate the currently available Java bytecode decompilers using an extension of the criteria that were used in the original study. Although there has been a slight improvement since this study, it was found that none passed all the tests, each of which were designed to target different problem areas. We give reasons for this lack of success and suggest methods by which future Java bytecode decompilers could be improved.
Software watermarks, which can be used to identify the intellectual property owner of a piece software, are broadly divided into two categories: static and dynamic. Static watermarks are embedded in the code and/or data of a computer program, whereas dynamic watermarking techniques store a watermark in a program's execution state. In this paper, we present a survey of the known static software watermarking techniques, including a brief explanation of each.
The purpose of this work is to contribute to the reverse engineering of software by providing a means for identifying the type of compiler used to compile a Java class or Linux ELF file. A software framework is presented for extracting potentially useful information from class files and analyzing that information to classify future files. A General Regression Neural Network is implemented and optimized using evolutionary computation. In experimental results, the system can classify compiler type on an file it has not seen before with over 98% accuracy.
As systems become more complex, the monitoring and interpretation of measurement data related to the health of the system becomes increasingly more difficult. Trend monitoring is an important task that involves a prediction of the future state of system health based upon past observations. In many systems, sensors or suites of sensors gather data about the state of health of the system and its processes. Analysis of the power spectrum of the time series resulting from this sort of data collection provides insight into the trends inherent. In this paper, we present a fractal-based approach to the interpretation of the power spectrum of the time series. Using fractal analysis enables the characterization of the power spectrum using a minimal set of parameters. A computational algorithm for the calculation of these parameters is presented and shows promise as a basis for trend monitoring.
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