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中文摘要：
面向入侵容忍的错误检测是系统安全最前沿的研究热点之一，它是保障容侵系统无边界退化、提供全部或降级服务的核心技术．分布式复杂网络环境中，错误的并发性和噪声信息的干扰使传统错误检测方法不再适用，在研究目前错误检测方法的基础上，结合容侵系统特性，提出了一种基于改进的贝叶斯并行学习的并行错误检测方法——PBL方法．该方法既能有效检测分布式环境下的并发错误，又能排除噪声数据的干扰．对PBL方法实现的关键问题进行了详细的讨论和分析．
英文摘要：
One of the most advanced research problems in intrusion tolerance systems （ITS） is error detection, which has become another essential technique in system security to prevent the intrusion from generating a system failure. A parallel error detection method named PBL for ITS, which is based on distributed Bayesian learning, is proposed in this paper. This method is particularly suitable for detecting errors with distributed sources. The PBL method not only is useful in detecting errors in the distributed network environment, but also can be used to enhance noise tolerant ability of ITS. Some key problems of the PBL method in detail are also discussed.
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