INFOCOM 2018 论文列表

Account recovery (usually through a password reset) on many websites has mainly relied on accessibility to a registered email due to its favorable deployability and usability. However, it makes a user's online accounts vulnerable to a single point of failure when the registered email account is compromised. While previous research focuses on strengthening user passwords, the security risk imposed by email-based account recovery has not yet been well studied. In this paper, we investigate the possibility of mounting an email-based account recovery attack. Specifically, we examine the account authentication and recovery protocols in 239 traffic-heavy websites, confirming that most of them use emails for account recovery. We further scrutinize the security policy of major email service providers and show that a significant portion of them take no or marginal effort to protect user email accounts, leaving compromised email accounts readily available for mounting account recovery attacks. Then, we conduct case studies to assess potential losses caused by such attacks. Finally, we propose a lightweight email security enhancement called Secure Email Account Recovery (SEAR) to defend against account recovery attacks as an extra layer of protection to account recovery emails.

An increasing number of embedded devices are connecting to the Internet at a surprising rate. Those devices usually run firmware and are exposed to the public by device search engines. Firmware in embedded devices comes from different manufacturers and product versions. More importantly, many embedded devices are still using outdated versions of firmware due to compatibility and release-time issues, raising serious security concerns. In this paper, we propose generating fine-grained fingerprints based on the subtle differences between the filesystems of various firmware images. We leverage the natural language processing technique to process the file content and the document object model to obtain the firmware fingerprint. To validate the fingerprints, we have crawled 9,716 firmware images from official websites of device vendors and conducted real-world experiments for performance evaluation. The results show that the recall and precision of the firmware fingerprints exceed 90%. Furthermore, we have deployed the prototype system on Amazon EC2 and collected firmware in online embedded devices across the IPv4 space. Our findings indicate that thousands of devices are still using vulnerable firmware on the Internet.

The conventional high-speed Wi-Fi has recently become a contender for low-power Internet-of-Things (IoT) communications. OFDM continues its adoption in the new IoT Wi-Fi standard due to its spectrum efficiency that can support the demand of massive IoT connectivity. While the IoT Wi-Fi standard offers many new features to improve power and spectrum efficiency, the basic physical layer (PHY) structure of transceiver design still conforms to its conventional design rationale where access points (AP) and clients employ the same OFDM PHY. In this paper, we argue that current Wi-Fi PHY design does not take full advantage of the inherent asymmetry between AP and IoT. To fill the gap, we propose an asymmetric design where IoT devices transmit uplink packets using the lowest power while pushing all the decoding burdens to the AP side. Such a design utilizes the sufficient power and computational resources at AP to trade for the transmission (TX) power of IoT devices. The core technique enabling this asymmetric design is that the AP takes full power of its high clock rate to boost the decoding ability. We provide an implementation of our design and show that it can reduce the IoT's TX power by boosting the decoding capability at the receivers.

State-of-the-art IEEE 802.11ac supports wide bandwidth operation, which enables aggregating multiple 20 MHz channels up to 160 MHz bandwidth, as a key feature to achieve high throughput. In this paper, our experiment results reveal various situations where bandwidth adaptation without changing the receiver's baseband bandwidth, called operating channel width (OCW), leads to poor reception performance due surprisingly to time-domain interference not overlapping with the incoming desired signal in frequency domain. To cope with this problem, we develop RECONN, a standard-compliant and receiver-driven OCWadaptation scheme with ease of implementation. Our prototype implementation in commercial 802.11ac devices shows that RECONN achieves up to 1.85× higher throughput by completely eliminating time-domain interference. To our best knowledge, this is the first work to discover the time-domain interference problem, and to develop OCW adaptation scheme in 802.11ac system.

As modern network applications (e.g., large data analytics) become more distributed and can conduct application-layer traffic adaptation, they demand better network visibility to better orchestrate their data flows. As a result, the ability to predict the available bandwidth for a set of flows has become a fundamental requirement of today's networking systems. While there are previous studies addressing the case of non-reactive flows, the prediction for reactive flows, e.g., flows managed by TCP congestion control algorithms, still remains an open problem. In this paper, we identify three challenges in providing throughput prediction for reactive flows: throughput dynamics, heterogeneous reactive control mechanisms, and source-constrained flows. Based on a previous theoretical model, we introduce a novel learning-based prediction system with a key component named fast factor learning (FFL) model. We adopt novel techniques to overcome practical concerns such as scalability, convergence and unknown system parameters. A system, Prophet, is proposed leveraging the emerging technologies of Software Defined Networking (SDN) to realize the model. Evaluations demonstrate that our solution achieves significant accuracy in a wide range of settings.

In the past few years, various backscatter nodes have been invented for many emerging mobile applications, such as sports analytics, interactive gaming, and mobile healthcare. Backscatter networks are expected to provide a high-throughput and stable communication platform for those interconnected mobile nodes. Yet, through experiments, we find state-of-the-art rate adaptation methods for backscatter networks share a fundamental limitation of accommodating the hardware diversity of nodes because the common mapping paradigm that chooses the optimal rate based on the radio signal strength indicator (RSSI) or the like is hardly adaptable to hardware-dependent RSSIs. To address this issue, we propose MobiRate (Mobility-aware Rate adaptation) that fully exploits the mobility hints from PHY information and the characteristics of backscatter systems. The key insight is that mobility-hints, like velocity and position, can greatly benefit rate selection and channel probing. Specifically, we introduce a novel velocity-based loss rate estimation method that dynamically re-weighs packets based on time and mobility. In addition, we design a mobility-assisted probing trigger and a new selective-probing mechanism, significantly saving probing time. As MobiRate is fully compatible with the current standard, it is prototyped using a COTS RFID reader and a variety of commercial tags. Our extensive experiments demonstrate that MobiRate achieves up to 3.8x throughput gain over the state-of-the-art methods across a wide range of mobility, channel conditions, and tag types.

Since inception, DNS has used a TTL-based replication scheme that allows the source (i.e., an authoritative domain server) to control the frequency of record eviction from client caches. Existing studies of DNS predominantly focus on reducing query latency and source bandwidth, both of which are optimized by increasing the cache hit rate. However, this causes less-frequent contacts with the source and results in higher staleness of retrieved records. Given high data-churn rates at certain providers (e.g., dynamic DNS, CDNs) and importance of consistency to their clients, we propose that cache models include the probability of freshness as an integral performance measure. We derive this metric under general update/download processes and present a novel framework for measuring its value using remote observation (i.e., without access to the source or the cache). Besides freshness, our methods can estimate the inter-update distribution of DNS records, cache hit rate, distribution of TTL, and query arrival rate from other clients. Furthermore, these algorithms do not require any changes to the existing infrastructure/protocols.

Privacy-preserving data aggregation has been extensively studied in the past decades. However, most of these works target at specific aggregation functions such as additive or multiplicative aggregation functions. Meanwhile, they assume there exists a trusted authority which facilitates the keys and other information distribution. In this paper, we aim to devise a communication efficient and privacy-preserving protocol that can exactly compute arbitrary data aggregation functions without trusted authority. In our model, there exist one untrusted aggregator and n participants. We assume that all communication channels are insecure and are subject to eavesdropping attacks. Our protocol is designed under the semi-honest model, and it can also tolerate k (k ≤ n-2) collusive adversaries. Our protocol achieves (n - k) -source anonymity. That is, for the source of each collected data aparting from the colluded participants, what the aggregator learns is only from one of the (n - k) non-colluded ones. Compared with recent work [1] that computes arbitrary aggregation functions by collecting all the participants' data using the trusted authority, our protocol increases merely by at most a factor of O(([logn/loglogn])2) in terms of computation time and communication cost. The key of our protocol is that we have designed algorithms that can efficiently assign unique sequence numbers to each participant without the trusted authority.

We show that many popular mobile services suffer from excessive network bandwidth consumption. The root cause is that the existing mobile/cloud communication interfaces are designed and optimized for service providers rather than end user devices. Solving the problem is challenging, because of the conflicted interests of service providers and mobile devices. We propose Edge-hosted Personal Service (EPS), with which device-oriented solutions can be easily deployed without affecting service providers. EPS also enjoys other notable advantages, including enabling new mobile services, reducing loads on the cloud, and benefiting delay-sensitive applications. We demonstrate the usefulness of EPS by designing ETA (Edge-based web Traffic Adaptation), an effective solution for the excessive bandwidth consumption problem, and deploy ETA with a prototype EPS system. By exploring lightweight virtualization techniques, our EPS prototype system is highly scalable in terms of concurrent EPS instances, and secure in terms of resource isolation. The real-world evaluation shows that our ETA EPS can effectively reduce bandwidth for mobile devices with small overheads.

With the advent of Network Function Virtualization (NFV), Physical Network Functions (PNFs) are gradually being replaced by Virtual Network Functions (VNFs) that are hosted on general purpose servers. Depending on the call flows for specific services, the packets need to pass through an ordered set of network functions (physical or virtual) called Service Function Chains (SFC) before reaching the destination. Conceivably for the next few years during this transition, these networks would have a mix of PNFs and VNFs, which brings an interesting mix of network problems that are studied in this paper: (1) How to find an SFC-constrained shortest path between any pair of nodes? (2) What is the achievable SFC-constrained maximum flow? (3) How to place the VNFs such that the cost (the number of nodes to be virtualized) is minimized, while the maximum flow of the original network can still be achieved even under the SFC constraint? In this work, we will try to address such emerging questions. First, for the SFC-constrained shortest path problem, we propose a transformation of the network graph to minimize the computational complexity of subsequent applications of any shortest path algorithm. Second, we formulate the SFC-constrained maximum flow problem as a fractional multicommodity flow problem, and develop a combinatorial algorithm for a special case of practical interest. Third, we prove that the VNFs placement problem is NP-hard and present an alternative Integer Linear Programming (ILP) formulation. Finally, we conduct simulations to elucidate our theoretical results.

Recently several ground-breaking RF-based motion-recognition systems were proposed to detect and/or recognize macro/micro human movements. These systems often suffer from various interferences caused by multiple-users moving simultaneously, resulting in extremely low recognition accuracy. To tackle this challenge, we propose a novel system, called Motion-Fi, which marries battery-free wireless backscattering and device-free sensing. Motion-Fi is an accurate, interference tolerable motion-recognition system, which counts repetitive motions without using scenario-dependent templates or profiles and enables multi-users performing certain motions simultaneously because of the relatively short transmission range of backscattered signals. Although the repetitive motions are fairly well detectable through the backscattering signals in theory, in reality they get blended into various other system noises during the motion. Moreover, irregular motion patterns among users will lead to expensive computation cost for motion recognition. We build a backscattering wireless platform to validate our design in various scenarios for over 6 months when different persons, distances and orientations are incorporated. In our experiments, the periodicity in motions could be recognized without any learning or training process, and the accuracy of counting such motions can be achieved within 5% count error. With little efforts in learning the patterns, our method could achieve 93.1% motion-recognition accuracy for a variety of motions. Moreover, by leveraging the periodicity of motions, the recognition accuracy could be further improved to nearly 100% with only 3 repetitions. Our experiments also show that the motions of multiple persons separated by around 2 meters cause little accuracy reduction in the counting process.

Recently, gesture recognition has gained considerable attention in emerging applications (e.g., AR/VR systems) to provide a better user experience for human-computer interaction. Existing solutions usually recognize the gestures based on wearable sensors or specialized signals (e.g., WiFi, acoustic and visible light), but they are either incurring high energy consumption or susceptible to the ambient environment, which prevents them from efficiently sensing the fine-grained finger movements. In this paper, we present RF-finger, a device-free system based on Commercial-Off-The-Shelf (COTS) RFID, which leverages a tag array on a letter-size paper to sense the fine-grained finger movements performed in front of the paper. Particularly, we focus on two kinds of sensing modes: finger tracking recovers the moving trace of finger writings; multi-touch gesture recognition identifies the multi-touch gestures involving multiple fingers. Specifically, we build a theoretical model to extract the fine-grained reflection feature from the raw RF -signal, which describes the finger influence on the tag array in cm- level resolution. For the finger tracking, we leverage K-Nearest Neighbors (KNN) to pinpoint the finger position relying on the fine-grained reflection features, and obtain a smoothed trace via Kalman filter. Additionally, we construct the reflection image of each multi-touch gesture from the reflection features by regarding the multiple fingers as a whole. Finally, we use a Convolutional Neural Network (CNN) to identify the multi-touch gestures based on the images. Extensive experiments validate that RF -finger can achieve as high as 88% and 92% accuracy for finger tracking and multi-touch gesture recognition, respectively.

Cross-Technology Communication (CTC) supports direct message exchange among heterogeneous wireless technologies (e.g., Wi-Fi, ZigBee, and BlueTooth) under the same ISM band, enabling explicit cross-technology control and coordination. For instance, a Wi-Fi AP can directly control ZigBee-enabled smart light bulbs without an expensive dual-radio gateway. Such CTC capability can be further amplified if we can extend the communication range of CTC to support long-range wide-area IoT applications such as environmental monitoring, smart metering, and precision agriculture. Our work, named LongBee, is the first to extend the range of Cross-Technology Communication. At the transmitter side, LongBee concentrates the effective TX power through down-clocked operations, and at the receiver side, LongBee improves the RX sensitivity with an innovative transition coding to ensure reliable preamble detection and payload reception. All these are achieved without modifying hardware and without introducing extra Wi-Fi RF energy cost. We implemented the LongBee on the USRP platform and commodity ZigBee devices. Our comprehensive evaluation reveals that LongBee with concentrated TX power and higher RX sensitivity achieves reliably over 10x range extension over native ZigBee communication and 2x range extension than the longest distance achieved by existing CTC schemes so far.

Software-Defined Networking (SDN) greatly simplifies middlebox policy enforcement. Middleboxes need tag packet headers to avoid forwarding ambiguity on SDN switches. In this paper, we present a new attack, called middlebox-bypass attack, to breach SDN-based middlebox policy enforcement. Such an attack manipulates a compromised switch to locally tag attacking packets without handing them over to the attached middlebox for inspection. Existing SDN security solutions, however, cannot detect the middlebox-bypass attack under practical constraints of efficiency, robustness, and applicability. We design and implement FlowCloak, the first protocol for per-packet real-time detection and prevention of middlebox-bypass attacks. FlowCloak enables middleboxes to generate tags that are probabilistically unknown to an attacker and confines it to only random guessing. We propose a multi-tag verification technique to address the tradeoff between FlowCloak robustness and TCAM usage by tag verification rules on the egress switch. Experiment results show that dozens of verification rules can confine the attacking probability under 0.1 %. FlowCloak imposes only a 0.3 ms packet processing delay on middleboxes and no obvious delay on the egress switch.

Cross-technology communication (CTC) is a technique that enables direct communication among different wireless technologies. Recent works in this area have made positive progress, but high-throughput CTC from ZigBee to WiFi remains an open problem. In this paper, we propose ZigFi, a novel CTC framework that enables direct communication from ZigBee to WiFi. Without impacting the ongoing WiFi transmissions, ZigFi carefully overlaps ZigBee packets with WiFi packets. Through experiments we show that Channel State Information (CSI) of the overlapped packets can be used to convey data from ZigBee to WiFi. Based on this finding, we propose a receiver-initiated protocol and translate the decoding problem into a problem of CSI classification with Support Vector Machine. We further build a generic model through experiments, which describes the relationship between the Signal to Interference and Noise Ratio (SINR) and the symbol error rate (SER). We implement ZigFi on commercial-off-the-shelf WiFi and ZigBee devices. We evaluate the performance of ZigFi under different experimental settings. The results demonstrate that ZigFi achieves a throughput of 215.9bps, which is 18X faster than the state-of-the-art.

In crowd sensing, truth discovery (TD) refers to finding reliable information from noisy/biased data collected from different providers. To protect providers' data while enabling truth distillation, privacy-preserving truth discovery (PPTD) has received wide attention recently. However, all existing approaches require iterative interaction between server(s) and individual providers, which inevitably demand all providers to be always online. Otherwise, the protocol would fail or expose extra provider information. In this paper, we design and implement the first non-interactive PPTD system that completely removes the online requirement with strong privacy guarantees. Our framework follows the same two-server model from the best-known prior solution, and leverages Yao's Garbled Circuit (GC). Yet, we devise non-trivial speedup techniques for TD-optimized implementation. Firstly, we identify reusable computations in TD to accelerate the circuit generation. Secondly, we securely evaluate the burdensome non-linear functions in TD via customized approximation with accuracy and improved efficiency. Thirdly, we reduce the online execution time by bridging together latest advancements of component-based GC and various computations needed in TD. Unlike prior arts, our framework does not reveal any intermediate results, and further supports “late-join” providers without protocol suspension/restart. The practical performance of our proof-of-concept implementation is verified through extensive evaluations.

Searching for a particular group of tags in an RFID system is a key service in such important Internet-of-Things applications as inventory management. When the system scale is large with a massive number of tags, deterministic search can be prohibitively expensive, and probabilistic search has been advocated, seeking a balance between reliability and time efficiency. Given a failure probability [1/(O(K))], where K is the number of tags, state-of-the-art solutions have achieved a time cost of O(K log K) through multi-round hashing and verification. Further improvement however faces a critical bottleneck of repetitively verifying each individual target tag in each round. In this paper, we present a novel Tree-based Tag Search (TTS) that approaches O (K) through batched verification. TTS smartly hashes multiple tags into each internal tree node and adaptively controls the node degrees. It conducts bottom-up search to verify tags group by group with the number of groups decreasing rapidly. We derive the optimal hash code length and node degrees to accommodate hash collisions, and demonstrate the superiority of TTS through both theoretical analysis and extensive simulations. In particular, we show that, with increasing reliability demand and system size, TTS achieves an even higher performance gain, making it a highly scalable solution.

In this paper, we investigate the Antenna Orientation (AO) and Antenna Orientation and Power Assignment (AOPA) problems concerning symmetric connectivity in Directional Wireless Sensor Networks (DWSNs) where each sensor node is equipped with 2 ≤ k ≤ 5 directional antennas having beamwidth θ ≥ 0. The AO problem for DWSNs is closely related with the well-known Euclidean Degree-Bounded Minimum Bottleneck Spanning Tree (EBMBST) problem where different cases for the degree bound have been studied. While current works on DWSNs focus on solving each case of k(=2, 3, 4) separately, we propose a uniform approach for the AO problem that yields constant-factor approximation algorithms for the AO as well as the EBMBST problem where the degree bound is between 2 and 4. Our method achieves the same constant factors. For the AOPA problem, to the best of our knowledge, our paper provides the first results concerning this problem. We show that the problem is NP-hard when 2 ≤ k ≤ 4. We also establish the first constant-factor approximation algorithms for the problem. Finally, we perform some simulations to understand the practical performance of our algorithms.

Nowadays, manufacturers want to collect the data of their sold-products to the cloud, so that they can conduct analysis and improve the operation, maintenance and services of their products. Manufacturers are looking for a self-contained solution for data transmission since their products are typically deployed in a large number of different buildings, and it is neither feasible to negotiate with each building to use the building's network (e.g., WiFi) nor practical to establish its own network infrastructure. ISPs are aware of this market. Since the readily available 3G/4G is over costly for most IoT devices, ISPs are developing new choices. Nevertheless, it can be expected that the choices from ISPs will not be fine-grained enough to match hundreds or thousands of requirements on different costs and data volumes from the IoT applications. To address this problem, we for the first time propose IoT communication sharing (ICS). We first clarify the ICS scenarios. We then formulate the IoT communication sharing (ICS) problem, and develop a set of algorithms with provable performance. We further present our implementation of a fully functioning system. Our evaluations show that ICS and our algorithms can lead to a cost reduction of five times and eight times respectively for the two real-world cases.