DISSECT: Distribution for SECurity tool

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Abstract

A security threat that affects the Java environment (as a typical environment where code is made available to the user machine) is the reverse-engineering and code-understanding of the architectureneutral bytecode format. A natural protection strategy is to hide parts of the execution in trusted locations (e.g., servers). However, the implementation and automatization of such an approach (beyond the abstract idea) is a challenging problem. In this paper, we present a novel software protection strategy and its automatization (implemented architecture) which materialize the above idea. It is employed in protecting the binary source of Java class files. Our software protection strategy partitions “programmer selected” classes of an application into server classes and client classes. Server classes contain the actual class code and run only on trusted systems (which we call servers but they can be other dedicated machines). Client classes, on the other hand, are assumed to perform most of the task (but the sensitive part) and execute on user systems; they must interact with their corresponding server class in order to execute the sensitive code and provide the behavior of the original class. We propose and implement DISSECT (DIStribution for SECurity Tool), an architecture based on the above partitioning (dissection) strategy, for Java 1.1. The tool relieves the developers from actually writing distributed applications by distributing the application automatically, according to designated sensitivities of application portions. We note thatthe remote execution of classes may increase the overhead. Thus, we have conducted initial experiments to understand the impact of partitioned classes on performance.We report initial performance results which show the overhead and demonstrate when it is low or non-existing, when it is high, and when we actually gain performance by partitioning.

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APA

Valdez, E., & Yung, M. (2001). DISSECT: Distribution for SECurity tool. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2200, pp. 125–143). Springer Verlag. https://doi.org/10.1007/3-540-45439-x_9

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