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Wireless Mesh Networks (WMNs ) have many applications in homes, schools, enterprises, and public places because of their useful characteristics, such as high bandwidth, high speed, and wide coverage. However, the security of wireless mesh networks is a precondition for practical use. Intrusion detection is pivotal for increasing network security. Considering the energy limitations in wireless mesh networks, we adopt two types of nodes: Heavy Intrusion Detection Node (HIDN) and Light Intrusion Detection Node (LIDN). To conserve energy, the LIDN detects abnormal behavior according to probability, while the HIDN, which has sufficient energy, is always operational. In practice, it is very difficult to acquire accurate information regarding attackers. We propose an intrusion detection model based on the incomplete information game (ID-IIG). The ID-IIG utilizes the Harsanyi transformation and Bayesian Nash equilibrium to select the best strategies of defenders, although the exact attack probability is unknown. Thus, it can effectively direct the deployment of defenders. Through experiments, we analyze the performance of ID-IIG and verify the existence and attainability of the Bayesian Nash equilibrium. 相似文献
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Web应用防火墙(WAF)基于一组规则检测和过滤进出Web应用程序的HTTP流量,鉴于恶意流量的复杂性,需要对WAF规则进行不断更新以抵御最新的攻击。然而,现有的WAF规则更新方法都需要专业知识来人工设计关于某种攻击的恶意测试流量,并针对该恶意流量生成防护规则,这种方法十分耗时且不能扩展到其他类型的攻击。提出一种基于循环神经网络(RNN)的Web应用防火墙加固方案,在不依赖任何专业知识的情况下自动化加固WAF。使用RNN模型生成恶意攻击样本,从中找到能够绕过WAF的恶意攻击,发现WAF规则存在的安全风险。在此基础上,通过设计评分函数找到恶意攻击样本的重要字符串来生成加固签名,阻止后续类似的攻击,并设计简化的正则表达式作为加固签名的表达形式。在4款WAF上针对SQL注入、跨站脚本攻击和命令注入这3种攻击进行测试,结果显示,该方案成功生成了大量绕过WAF的恶意样本,WAF针对这些样本的平均拦截率仅为52%,与传统突变方案和SQLMap工具相比能够生成更多绕过恶意攻击,在应用加固签名后,WAF的恶意攻击拦截率提升至90%以上且误报率维持为0,表明加固签名成功阻止了这些绕过攻击,验证了所提方案的有效性。 相似文献
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