INFOCOM 2021 Papers

Software-defined networking (SDN) based LEO satellite networks can make full use of satellite resources through flexible function configuration and efficient resource management of controllers. Consequently, controllers have to be carefully deployed based on dynamical topology and time-varying workload. However, existing work on controller placement and assignment is not applicable to LEO satellite networks with highly dynamic topology and randomly fluctuating load. In this paper, we first formulate the adaptive controller placement and assignment (ACPA) problem and prove its NP-hardness. Then, we propose the control relation graph (CRG) to quantitatively capture the control overhead in LEO satellite networks. Next, we propose the CRG-based controller placement and assignment (CCPA) algorithm with a bounded approximation ratio. Finally, using the predicted topology and estimated traffic load, a lookahead-based improvement algorithm is designed to further decrease the overall management costs. Extensive emulation results demonstrate that the CCPA algorithm outperforms related schemes in terms of response time and load balancing.

Deep neural networks (DNNs) have been applied in a wide range of applications, e.g., face recognition and image classification; however, they are vulnerable to adversarial examples. By adding a small amount of imperceptible perturbations, an attacker can easily manipulate the outputs of a DNN. Particularly, the localized adversarial examples only perturb a small and contiguous region of the target object, so that they are robust and effective in both digital and physical worlds. Although the localized adversarial examples have more severe real-world impacts than traditional pixel attacks, they have not been well addressed in the literature. In this paper, we propose a generic defense system called TaintRadar to accurately detect localized adversarial examples via analyzing critical regions that have been manipulated by attackers. The main idea is that when removing critical regions from input images, the ranking changes of adversarial labels will be larger than those of benign labels. Compared with existing defense solutions, TaintRadar can effectively capture sophisticated localized partial attacks, e.g., the eye-glasses attack, while not requiring additional training or fine-tuning of the original model's structure. Comprehensive experiments have been conducted in both digital and physical worlds to verify the effectiveness and robustness of our defense.

We design, develop, and evaluate P6, an automated approach to (a) detect, (b) localize, and (c) patch software bugs in P4 programs. Bugs are reported via a violation of pre-specified expected behavior that is captured by P6. P6 is based on machine learning-guided fuzzing that tests P4 switch non-intrusively, i.e., without modifying the P4 program for detecting runtime bugs. This enables an automated and real-time localization and patching of bugs. We used a P6 prototype to detect and patch existing bugs in various publicly available P4 application programs deployed on two different switch platforms: behavioral model (bmv2) and Tofino. Our evaluation shows that P6 significantly outperforms bug detection baselines while generating fewer packets and patches bugs in large P4 programs such as switch.p4 without triggering any regressions.

Ambient backscatter communication (AmBC), which enables energy harvesting and ultra-low-power communication by utilizing ambient radio frequency (RF) signals, has emerged as a cutting-edge technology to realize numerous Internet of Things (IoT) applications. However, the current literature lacks efficient secret key sharing solutions for resource-limited devices in AmBC systems to protect the backscatter communications, especially for private data transmission. Thus, we propose a novel physical layer key generation scheme between backscatter devices (BDs) by exploiting received superposed ambient signals. Based on the repeated patterns (i.e., cyclic prefix in OFDM symbols) in ambient RF signals, we present a joint transceiver design of BD backscatter waveform and BD receiver to extract the downlink signal and the backscatter signal from the superposed signals. By multiplying the downlink signal and the backscatter signal, we can actually obtain the triangle channel information as a shared random secret source for key generation. Besides, we study the trade-off between the rate of secret key generation and harvested energy by modeling it as a joint optimization problem. Finally, extensive numerical simulations are provided to evaluate the key generation performance, energy harvesting performance, and their trade-offs under various system settings.

The dynamics of the spread of contagions such as viruses, infectious diseases or even rumors/opinions over contact networks (graphs) have effectively been captured by the well known Susceptible-Infected-Susceptible (SIS) epidemic model in recent years. When it comes to competition between two such contagions spreading on overlaid graphs, their propagation is captured by so-called bi-virus epidemic models. Analysis of such dynamical systems involve the identification of equilibrium points and its convergence properties, which determine whether either of the viruses dies out, or both survive together. We demonstrate how the existing works are unsuccessful in characterizing a large subset of the model parameter space, including all parameters for which the competitiveness of the bi-virus system is significant enough to attain coexistence of the epidemics. In this paper, we fill in this void and obtain convergence results for the entirety of the model parameter space; giving precise conditions (necessary and sufficient) under which the system globally converges to a trichotomy of possible outcomes: a virus-free state, a single-virus state, and to a coexistence state - the first such result.

Emerging 802.11p vehicle-to-vehicle (V2V) networks rely on periodic Basic Safety Messages (BSMs) to disseminate time-sensitive safety-critical information, such as vehicle position, speed, and heading - that enables several safety applications and has the potential to improve on-road safety. Due to mobility, lack of global-knowledge and limited communication resources, designing an optimal BSM broadcast rate-control protocol is challenging. Recently, minimizing Age of Information (AoI) has gained momentum in designing BSM broadcast rate-control protocols. In this paper, we show that minimizing AoI solely does not always improve the safety of V2V networks. Specifically, we propose a novel metric, termed Trackability-aware Age of Information TAoI, that in addition to AoI, takes into account the self risk assessment of vehicles, quantified in terms of self tracking error (self-TE) - which provides an indication of collision risk posed by the vehicle. Self-TE is defined as the difference between the actual location of a certain vehicle and its self-estimated location. Our extensive experiments, based on realistic SUMO traffic traces on top of ns-3 simulator, demonstrate that TAoI based rate-protocol significantly outperforms baseline AoI based rate protocol and default 10 Hz broadcast rate in terms of safety performance, i.e., collision risk, in all considered V2V settings.

In a multi-tenant cloud, cloud vendors provide services (e.g., elastic load-balancing, virtual private networks) on service nodes for tenants. Thus, the mapping of tenants' traffic and service nodes is an important issue in multi-tenant clouds. In practice, unreliability of service nodes and uncertainty/dynamics of tenants' traffic are two critical challenges that affect the tenants' QoS. However, previous works often ignore the impact of these two challenges, leading to poor system robustness when encountering system accidents. To bridge the gap, this paper studies the problem of robust service mapping in multi-tenant clouds (RSMP). Due to traffic dynamics, we take a two-step approach: service node assignment and tenant traffic scheduling. For service node assignment, we prove its NP-Hardness and analyze its problem difficulty. Then, we propose an efficient algorithm with bounded approximation factors based on randomized rounding and knapsack. For tenant traffic scheduling, we design an approximation algorithm based on fully polynomial time approximation scheme (FPTAS). The proposed algorithm achieves the approximation factor of 2+ ε , where ε is an arbitrarily small value. Both small-scale experimental results and large-scale simulation results show the superior performance of our proposed algorithms compared with other alternatives.

This paper studies social network de-anonymization problem, which aims to identify users of an anonymized network by matching its user set with that of another auxiliary sanitized network. Prior arts primarily assume that both networks share exactly the same set of users, as opposed to many real situations of partially shared users in between. Different from the full matching case that only needs to take care of increasing the number of correctly matched pairs, the case of partial overlapping imposes additional demand on avoiding the wrong matches of those who do not have accounts across networks.To this end, we establish a new cost function, which we call the structural F-score to incorporate both the structural commonness and difference across networks. Intrinsically, the structural F-score computes the ratio of link agreements and disagreements, thus serving as the harmonic mean of precision and recall for any given matching function. Theoretically, we show that for networks parameterized by node overlap t2and link overlap s2, as long as the mean degree of networks grows as Ω(t-2s-3log n), maximizing the structural F-score provably ensures the perfect matching, where the nodal precision and recall are both maximized to 1. Algorithmically, for small-scale networks, we propose a two-step heuristic of F-score based de-anonymization, which firstly finds the optimal full matching between networks and then removes those pairs hindering structural F-score maximization. Due to the universal adaptability of the structural F-score, we further extend the algorithm to large-scale networks via a progressive matching process. Empirical results also validate the effectiveness of our methods in terms of improving the nodal F-score.

Federated learning enables distributed learning in a privacy-protected manner, but two challenging reasons can affect learning performance significantly. First, mobile users are not willing to participate in learning due to computation and energy consumption. Second, with various factors (e.g., training data size/quality), the model update quality of mobile devices can vary dramatically, inclusively aggregating low-quality model updates can deteriorate the global model quality. In this paper, we propose a novel system named FAIR, i.e., Federated leArning with qualIty awaReness. FAIR integrates three major components: 1) learning quality estimation: we leverage historical learning records to estimate the user learning quality, where the record freshness is considered and the exponential forgetting function is utilized for weight assignment; 2) quality-aware incentive mechanism: within the recruiting budget, we model a reverse auction problem to encourage the participation of high-quality learning users, and the method is proved to be truthful, individually rational, and computationally efficient; and 3) model aggregation: we devise an aggregation algorithm that integrates the model quality into aggregation and filters out non-ideal model updates, to further optimize the global learning model. Based on real-world datasets and practical learning tasks, extensive experiments are carried out to demonstrate the efficacy of FAIR.

State-of-the-art inter-datacenter WANs rely on centralized traffic engineering (TE) to improve the network performance, where TE computation is a periodical procedure and timely performs routing configurations (i.e., enforces TE polices via add, remove and modify forwarding rules) in response to the changing network conditions. The TE computation determines the routing configurations corresponding to the current network conditions and the network update operations change the routing configurations from last TE to current TE solution. Existing works take centralized TE computation and network update as two individual optimization procedures, which inevitably leads to suboptimal solution in the long run. In this paper we initiate the study of online joint optimization on TE computation and network update with the objective of minimizing the sum of TE cost and network update cost. We formulate this problem as an optimization program and propose a set of provable online algorithms with rigorous competitive and regret analysis. Trace-driven simulations on two empirical topologies demonstrate that our algorithms can significantly decrease the total cost.

The In-band Network Telemetry (INT) enables hop-by-hop device-internal state exposure for reliably maintaining and troubleshooting data center networks. For achieving network-wide telemetry, orchestration on top of the INT primitive is further required. One straightforward solution is to flood the INT probe packets into the network topology for maximum measurement coverage, which, however, leads to huge bandwidth overhead. A refined solution is to leverage the SDN controller to collect the topology and carry out centralized probing path planning, which, however, cannot seamlessly adapt to occasional topology changes. To tackle the above problems, in this work, we propose INT-label, a lightweight In-band Network-Wide Telemetry architecture via interval-based distributed labelling. INT-label periodically labels device-internal states onto sampled packets, which is cost-effective with minor bandwidth overhead and able to seamlessly adapt to topology changes. Furthermore, to avoid telemetry resolution degradation due to loss of labelled packets, we also design a feedback mechanism to adaptively change the instant label frequency. Evaluation on software P4 switches suggests that INT-label can achieve 99.72% measurement coverage under a label frequency of 20 times per second. With adaptive labelling enabled, the coverage can still reach 92% even if 60% of the packets are lost in the data plane.

The increasing challenge in designing online algorithms lies in the distribution uncertainty. To cope with the distribution variations in online optimization, an intuitive idea is to reselect an algorithm from the candidate set that will be more suitable to future distributions. In this paper, we propose Ostasos, an automatic algorithm selection framework that can choose the most suitable algorithm on the fly with provable guarantees. Rigorous theoretical analysis demonstrates that the performance of Ostasos is no worse than that of any candidate algorithms in terms of competitive ratio. Finally, we apply Ostasos to the online car-hailing problem and trace-driven experiments verify the effectiveness of Ostasos.

Social media, such as Reddit, has become a norm in our daily lives, where users routinely express their attitude using upvotes (likes) or downvotes. These social interactions may encourage users to interact frequently and form strong ties of trust between one another. It is therefore important to predict social trust from these interactions, as they facilitate routine features in social media, such as online recommendation and advertising.Conventional methods for predicting social trust often accept static graphs as input, oblivious of the fact that social interactions are time-dependent. In this work, we propose Medley, to explicitly model users' time-varying latent factors and to predict social trust that varies over time. We propose to use functional time encoding to capture continuous-time features and employ attention mechanisms to assign higher importance weights to social interactions that are more recent. By incorporating topological structures that evolve over time, our framework can infer pairwise social trust based on past interactions. Our experiments on benchmarking datasets show that Medley is able to utilize time-varying interactions effectively for predicting social trust, and achieves an accuracy that is up to 26% higher over its alternatives.

Video analytics constitute a core component of many wireless services that require processing of voluminous data streams emanating from handheld devices. Multi-Access Edge Computing (MEC) is a promising solution for supporting such resource-hungry services, but there is a plethora of configuration parameters affecting their performance in an unknown and possibly time-varying fashion. To overcome this obstacle, we propose an Automated Machine Learning (AutoML) framework for jointly configuring the service and wireless network parameters, towards maximizing the analytics' accuracy subject to minimum frame rate constraints. Our experiments with a bespoke prototype reveal the volatile and system/data-dependent performance of the service, and motivate the development of a Bayesian online learning algorithm which optimizes on-the-fly the service performance. We prove that our solution is guaranteed to find a near-optimal configuration using safe exploration, i.e., without ever violating the set frame rate thresholds. We use our testbed to further evaluate this AutoML framework in a variety of scenarios, using real datasets.

The cybersecurity breaches render surveillance systems vulnerable to video forgery attacks, under which authentic live video streams are tampered to conceal illegal human activities under surveillance cameras. Traditional video forensics approaches can detect and localize forgery traces in each video frame using computationally-expensive spatial-temporal analysis, while falling short in real-time verification of live video feeds. The recent work correlates time-series camera and wireless signals to recognize replayed surveillance videos using event-level timing information but it cannot realize fine-grained forgery detection and localization on each frame. To fill this gap, this paper proposes Secure-Pose, a novel cross-modal forgery detection and localization system for live surveillance videos using WiFi signals near the camera spot. We observe that coexisting camera and WiFi signals convey common human semantic information and the presence of forgery attacks on video frames will decouple such information correspondence. Secure-Pose extracts effective human pose features from synchronized multi-modal signals and detects and localizes forgery traces under both inter-frame and intra-frame attacks in each frame. We implement Secure-Pose using a commercial camera and two Intel 5300 NICs and evaluate it in real-world environments. Secure-Pose achieves a high detection accuracy of 95.1% and can effectively localize tampered objects under different forgery attacks.

Network support for Unmanned Aerial Vehicles (UAVs) is raising an interest among researchers due to the strong potential applications. However, current knowledge on UAV data traffic is mainly based on conceptual studies and does not provide an in-depth insight on the data traffic properties. To close this gap, we present a measurement-based study analyzing in detail the Control and Non-payload Communication (CNPC) traffic produced by three different UAVs when communicating with their remote controller over 802.11 protocol. We analyze the traffic in terms of data rate, inter-packet interval and packet length distributions, and identify their main influencing factors. The data traffic appears neither deterministic nor periodic but bursty, with a tendency towards Poisson traffic. We further create an understanding on how the traffic of the investigated UAVs are internally generated and propose a model to analytically capture their traffic processes, which provides an explanation for the observed behavior. We implemented a publicly available UAV traffic generator "AVIATOR" based on the proposed traffic model and verified the model by comparing the simulated traces with the experimental results.

We describe the data transmitted to backend servers by the contact tracing apps now deployed in Europe with a view to evaluating user privacy. These apps consist of two separate components: a "client" app managed by the national public health authority and the Google/Apple Exposure Notification (GAEN) service, that on Android devices is managed by Google and is part of Google Play Services. We find that the health authority client apps are generally well behaved from a privacy point of view, although the privacy of the Irish, Polish, Danish and Latvian apps could be improved. In marked contrast, we find that the Google Play Services component of these apps is problematic from a privacy viewpoint. Even when minimally configured, Google Play Services still contacts Google servers roughly every 20 minutes, potentially allowing location tracking via IP address. In addition, the phone IMEI, hardware serial number, SIM serial number and IMSI, handset phone number etc are shared with Google, together with detailed data on phone activity. This data collection is enabled simply by enabling Google Play Services, even when all other Google services and settings are disabled, and so is unavoidable for users of GAEN-based contact tracing apps on Android.

Federated learning (FL) has emerged in edge computing to address limited bandwidth and privacy concerns of traditional cloud-based centralized training. However, the existing FL mechanisms may lead to long training time and consume a tremendous amount of communication resources. In this paper, we propose an efficient FL mechanism, which divides the edge nodes into K clusters by balanced clustering. The edge nodes in one cluster forward their local updates to cluster header for aggregation by synchronous method, called cluster aggregation, while all cluster headers perform the asynchronous method for global aggregation. This processing procedure is called hierarchical aggregation. Our analysis shows that the convergence bound depends on the number of clusters and the training epochs. We formally define the resource-efficient federated learning with hierarchical aggregation (RFL-HA) problem. We propose an efficient algorithm to determine the optimal cluster structure (i.e., the optimal value of K) with resource constraints and extend it to deal with the dynamic network conditions. Extensive simulation results obtained from our study for different models and datasets show that the proposed algorithms can reduce completion time by 34.8%-70% and the communication resource by 33.8%-56.5% while achieving a similar accuracy, compared with the well-known FL mechanisms.

Major manufacturers and retailers are increasingly using RFID systems in supply-chain scenarios, where theft of goods during transport typically causes significant economic losses for the consumer. Recent sample-based authentication methods attempt to use a small set of random sample tags to authenticate the integrity of the entire tag population, which significantly reduces the authentication time at the expense of slightly reduced reliability. The problem is that it still incurs extensive initialization overhead when writing the authentication information to all of the tags. This paper presents KTAuth, a lightweight integrity authentication approach to efficiently and reliably detect missing tags and counterfeit tags caused by stolen attacks. The competitive advantage of KTAuth is that it only requires writing the authentication information to a small set of deterministic key tags, offering a significant reduction in initialization costs. In addition, KTAuth strictly follows the C1G2 specifications and thus can be deployed on Commercial-Off-The-Shelf RFID systems. Furthermore, KTAuth proposes a novel authentication chain mechanism to verify the integrity of tags exclusively based on data stored on them. To evaluate the feasibility and deployability of KTAuth, we implemented a small-scale prototype system using mainstream RFID devices. Using the parameters achieved from the real experiments, we also conducted extensive simulations to evaluate the performance of KTAuth in large-scale RFID systems.