Two research contributions in 64-bit computing: Testing and Applications

Following the release of Windows 64-bit and Redhat Linux 64-bit operating systems (OS) in late April 2005, this is the one of the first 64-bit OS research project completed in a British university. The objective is to investigate (1) the increase/decrease in performance compared to 32-bit computing; (2) the techniques used to develop 64-bit applications; and (3) how 64-bit computing should be used in IT and research organizations to improve their work. This paper summarizes research discoveries for this investigation, including two major research contributions in (1) testing and (2) application development. The first contribution includes performance, stress, application, multiplatform, JDK and compatibility testing for AMD and Intel models. Comprehensive testing results reveal that 64-bit computing has a better performance in application performance, system performance and stress testing, but a worse performance in compatibility testing than the traditional 32-bit computing. A 64-bit dual-core processor has been tested and the results show that it performs better than a 64-bit single-core processor, but only in application that requires very high demands of CPU and memory consumption. The second contribution is .NET 1.1 64-bit implementations. Without additional troubleshooting, .NET 1.1 does not work on 64-bit Windows operating systems in stable ways. After stabilizing .NET environment, the next step is the application development, which is a dynamic repository with functions such as registration, download, login-logout, product submissions, database storage and statistical reports. The technology is based on Visual Studio .NET 2003, .NET 1.1 Framework with Service Pack 1, SQL Server 2000 with Service Pack 4 and IIS Server 6.0 on the Windows Server 2003 Enterprise x64 platform with Service Pack 1

Spyware detection technique based on reinforcement learning

Analysis of the antivirus technologies, showed that they are not able to detect new spyware with high efficiency, which significantly reduces the reliability and efficiency of its identification. Techniques based on heuristic analysis have a high rate of false positives. The paper presents a new technique for the spyware detection method in computer systems that provides a principle of proactivity and is based on mechanisms machine learning with the reinforce-mentlearning. The suggested method of spyware detection is based on software behavior analysis in computer systems. The suggested method involves the computer systems monitoring concerning the software, operates with the behavior