现有存储型网络隐蔽信道的研究主要根据不同协议中不同字段来隐藏信息。在众多协议中,例如TCP、UDP协议,对其研究较多,而OSFP使用广泛却在国内研究较少。针对OSPF协议下的Hello报文进行分析可以构建网络隐蔽信道的字段。从所有可能字段...现有存储型网络隐蔽信道的研究主要根据不同协议中不同字段来隐藏信息。在众多协议中,例如TCP、UDP协议,对其研究较多,而OSFP使用广泛却在国内研究较少。针对OSPF协议下的Hello报文进行分析可以构建网络隐蔽信道的字段。从所有可能字段中选择Authentication、Router Dead Interval和Neighbor三个字段分别使用随机值模式、值调制模型和序列模式进行构建三种隐蔽信道,利用微协议技术优化信道,并将三种隐蔽信道组合成一个传输速率更高的隐蔽信道模型。经过验证,该模型具有一定的可行性和隐蔽性,可为存储型网络隐蔽信道构建技术提供一定的理论支持和技术支撑。展开更多
Despite extensive research, timing channels (TCs) are still known as a principal category of threats that aim to leak and transmit information by perturbing the timing or ordering of events. Existing TC detection appr...Despite extensive research, timing channels (TCs) are still known as a principal category of threats that aim to leak and transmit information by perturbing the timing or ordering of events. Existing TC detection approaches use either signature-based approaches to detect known TCs or anomaly-based approach by modeling the legitimate network traffic in order to detect unknown TCs. Un-fortunately, in a software-defined networking (SDN) environment, most existing TC detection approaches would fail due to factors such as volatile network traffic, imprecise timekeeping mechanisms, and dynamic network topology. Furthermore, stealthy TCs can be designed to mimic the legitimate traffic pattern and thus evade anomalous TC detection. In this paper, we overcome the above challenges by presenting a novel framework that harnesses the advantages of elastic re-sources in the cloud. In particular, our framework dynamically configures SDN to enable/disable differential analysis against outbound network flows of different virtual machines (VMs). Our framework is tightly coupled with a new metric that first decomposes the timing data of network flows into a number of using the discrete wavelet-based multi-resolution transform (DWMT). It then applies the Kullback-Leibler divergence (KLD) to measure the variance among flow pairs. The appealing feature of our approach is that, compared with the existing anomaly detection approaches, it can detect most existing and some new stealthy TCs without legitimate traffic for modeling, even with the presence of noise and imprecise timekeeping mechanism in an SDN virtual environment. We implement our framework as a prototype system, OBSERVER, which can be dynamically deployed in an SDN environment. Empirical evaluation shows that our approach can efficiently detect TCs with a higher detection rate, lower latency, and negligible performance overhead compared to existing approaches.展开更多
文摘现有存储型网络隐蔽信道的研究主要根据不同协议中不同字段来隐藏信息。在众多协议中,例如TCP、UDP协议,对其研究较多,而OSFP使用广泛却在国内研究较少。针对OSPF协议下的Hello报文进行分析可以构建网络隐蔽信道的字段。从所有可能字段中选择Authentication、Router Dead Interval和Neighbor三个字段分别使用随机值模式、值调制模型和序列模式进行构建三种隐蔽信道,利用微协议技术优化信道,并将三种隐蔽信道组合成一个传输速率更高的隐蔽信道模型。经过验证,该模型具有一定的可行性和隐蔽性,可为存储型网络隐蔽信道构建技术提供一定的理论支持和技术支撑。
文摘Despite extensive research, timing channels (TCs) are still known as a principal category of threats that aim to leak and transmit information by perturbing the timing or ordering of events. Existing TC detection approaches use either signature-based approaches to detect known TCs or anomaly-based approach by modeling the legitimate network traffic in order to detect unknown TCs. Un-fortunately, in a software-defined networking (SDN) environment, most existing TC detection approaches would fail due to factors such as volatile network traffic, imprecise timekeeping mechanisms, and dynamic network topology. Furthermore, stealthy TCs can be designed to mimic the legitimate traffic pattern and thus evade anomalous TC detection. In this paper, we overcome the above challenges by presenting a novel framework that harnesses the advantages of elastic re-sources in the cloud. In particular, our framework dynamically configures SDN to enable/disable differential analysis against outbound network flows of different virtual machines (VMs). Our framework is tightly coupled with a new metric that first decomposes the timing data of network flows into a number of using the discrete wavelet-based multi-resolution transform (DWMT). It then applies the Kullback-Leibler divergence (KLD) to measure the variance among flow pairs. The appealing feature of our approach is that, compared with the existing anomaly detection approaches, it can detect most existing and some new stealthy TCs without legitimate traffic for modeling, even with the presence of noise and imprecise timekeeping mechanism in an SDN virtual environment. We implement our framework as a prototype system, OBSERVER, which can be dynamically deployed in an SDN environment. Empirical evaluation shows that our approach can efficiently detect TCs with a higher detection rate, lower latency, and negligible performance overhead compared to existing approaches.
