Website fingerprinting attacks can reveal the receiver in anonymous networks and cause a potential threat to users' privacy. Previous studies focus more on identifying individual webpages. They also neglect the hyperlink transition information, because it induces extra “noise” to classify the original webpage. However, it is a common scenario that the users surf a website by clicking hyperlinks on the webpage. In this paper, we propose a website modeling method based on profile hidden Markov model (PHMM) which is widely used in bioinformatics for DNA sequencing analysis. Our technique explicitly accounts for possible hyperlink transitions made by users when fingerprinting a target website, and therefore can work in a more realistic environment than existing methods. Using SSH and Shadowsocks, we collect various data sets and conduct extensive evaluations. We also show that our approach could work both in webpage and website identification in a closed world setting. The experimental results demonstrate that our website fingerprinting is more accurate and robust than existing methods.

In SDN, the logically centralized control plane ("network OS") is often realized via multiple SDN controllers for scalability and reliability. ONOS is such an example, where it employs Raft -- a new consensus protocol developed recently -- for state replication and consistency among the distributed SDN controllers. The reliance of network OS on consensus protocols to maintain consistent network state introduces an intricate inter-dependency between the network OS and the network under its control, thereby creating new kinds of fault scenarios or instabilities. In this paper, we use Raft to illustrate the problems that this inter-dependency may introduce in the design of distributed SDN controllers and discuss possible solutions to circumvent these issues.

Multipath TCP (MPTCP) is a promising technique for boosting application throughput while using well-known and versatile network socket interfaces. Recently, many interesting applications of MPTCP in various environments such as wireless networks and data centers have been proposed, but little work has been done to investigate the impact of this protocol on conventional network devices. For example, MPTCP throughput advantage can be better achieved if all MPTCP subflows are routed on disjoint paths, but this is currently not feasible since routers are not designed to recognize the membership of MPTCP subflows. In this paper, we take a first step to address this issue by proposing SAMPO, an online algorithm to detect and associate MPTCP subflows in network. The main challenge is that sampling techniques and network dynamics may cause a network device to only obtain partial flow records. SAMPO takes advantage of both protocol information and statistical characteristics of MPTCP data sequence number to overcome the challenge in network. Through analysis and experimentation, we show that SAMPO is able to detect and associate MPTCP subflows with high accuracy even when a small portion of the entire flow records are available.

In this study, behavioral and brain measures were taken to assess the effects of training a Japanese adult subject to perceptually distinguish English/l/and. Behavioral data showed significant improvement in identifying both trained and untrained speech stimuli. Correspondingly, neuromagnetic results showed enhanced mismatch field responses in the left hemisphere and reduced activities in the right hemisphere. This pattern of neural plasticity was not observed for truncated nonspeech stimuli.