Network traffic data facilitates understanding the Internet of Things (IoT) behaviors and improving IoT service quality in the real world. However, large-scale IoT traffic data is rarely accessible, and privacy issues also impede realistic data sharing even with anonymous personal identifiable information. Researchers propose to generate synthetic IoT traffic but fail to cover the multiple services provided by widespread real-world IoT devices. In this work, we take the first step to generate large-scale IoT traffic via a knowledge-enhanced generative adversarial network (GAN) framework, which introduces both the semantic knowledge (e.g., location and environment information) and the network structure knowledge for various IoT devices via a knowledge graph. We use a condition mechanism to incorporate the knowledge and device category for IoT traffic generation. Then, we adopt LSTM and a self-attention mechanism to capture the temporal correlation in the traffic series. Extensive experiment results show that the synthetic IoT traffic datasets generated by our proposed model outperform state-of-art baselines in terms of data fidelity and applications. Moreover, our proposed model is able to generate realistic data by only training on small real datasets with knowledge enhanced.

As the largest microblogging service in China, Sina Weibo, is associated with a huge amount of traffic to and from other major websites. Thanks to a massive set of anonymized data from a major operator covering a large-scale metropolitan area, we extract the click-based transitions between Weibo and other websites. Such transitions have not been looked into in most studies that rely on the visible hyper-links among websites. Based on this unique dataset, we show that 38% of the outgoing transitions (leaving Weibo) go to video clip services. Comparatively, links related to news and long videos are clicked less but posted more. This indicates that photograph and text-sharing in microblog have been gradually replaced by video clip sharing. Also, ignoring video clip sharing, 53% of the transitions go to five of the largest Chinese websites, showing how closely connected Weibo has become in the Chinese Web ecosystem. We further look at users' click behavior in transitions, and find that through co-clustering there do exist a number of prevalent and distinct behavior patterns, suggesting that only seven co-clusters can capture the click behavior of 192 000 studied users. Overall, our work is the first to unveil the significant role of microblogging platforms in the Web ecosystem. Considering the massive user base of Weibo, our findings can aid the design of more personalized link recommendation in Weibo, thus generating more referral traffic, i.e., user visits, from Weibo to other major websites.

Due to the extensive code reuse and the widespread use of third-party SDKs, homologous binaries are widely found in IoT firmwares. Once a vulnerability is found in one firmware, other firmwares sharing the similar piece of codes are at high risk. Thus, homologous binary search is of great significance to IoT firmware security analysis. However, there are still no scalable and efficient homologous binary search methods for IoT firmwares. The time complexity of the state-of-the-art method is O(N), and it is not scalable for large-scale IoT firmwares. In this paper, we design, implement, and evaluate a scalable and efficient homologous binary search scheme (termed as IHB) for IoT firmwares with time complexity O(1). The main idea of our methodology is to leverage readable strings in binaries to calculate the similarities between different IoT firmwares. Furthermore, we employ a string filter and the string-based MinHash to achieve both accuracy and efficiency. We test both our scheme and the state-of-the-art methods on a real dataset containing 1024 binary files. The results show that our method is three orders of magnitude more efficient than the existing methods. Meanwhile, our method has a higher true positive rate (92.88%) and a lower false positive rate (2.83%). In the interest of open science, we also make our tools and datasets publicly available to seed future improvements.

Data plane programmability is regarded as an essential feature of future Internet. However, the flexibility of programmability has not been carefully considered. We present GrainFlow, a hardware-based packet processing platform supporting customization on every header bit's processing and adapting to packet header length variation. We evaluate GrainFlow on a Virtex-5 FPGA board. Experiment results show that users can flexibly redefine the data plane with only 10-20 rules and data plane forwarding rate can achieve wire-speed with acceptable latency increase.

A significant concern for cloud operators is to provide global network performance isolation for concurrent tenants. To address this, we propose M2cloud, a software defined framework providing scalable network control for multi-site data centers (DCs). M2cloud employs two-level controllers with decoupled functions, providing each tenant with flexible virtualization support in both intra- and inter-DC networks.

Some new medical applications, such as wireless endoscopy system for the diagnoses of whole human digestive tract and real-time endoscopic image monitoring, demand high speed transmission in wireless body sensor network (WBSN). This paper proposes a heterogeneous high speed wireless body sensor network based on ZIGBEE and single carrier ultra wideband (SC-UWB). Our system can choose high speed mode or low speed mode automatically according to the type of data source. Several measures are taken to miniaturize the nodes, lower the system complexity and reduce power consumption, offering system users great mobility and flexibility. We develop and validate a prototype WBSN, supporting four real-time high speed video streams and six low speed sensor data. The performance of the developed system is experimentally evaluated using prototype WBSN, and the ASIC has been designed and fabricated in the standard 0.18im CMOS technology, occupying a die area of 3.0mm * 3.0mm and consuming a dynamic power of 43.4280mw.