High security and low latency are important in mission-critical data transmission, such as the end-to-end transmission in Industrial IoT (IIoT). However, existing schemes often struggle to simultaneously meet these demanding requirements due to hardware limitations and the lack of a packet lossless forwarding protocol in the network layer data plane. To address this challenge, we propose OCEAN (Optional Capability-based En route Acknowledgement in Network layer). OCEAN includes (1) an in-network caching hardware, which is a programmable Application Specific Integrated Circuit (ASIC) integrated with a Field Programmable Gate Array (FPGA), and (2) a packet lossless forwarding protocol in the network layer data plane. In OCEAN, each packet was generated by an authorized end device, while each en route node verifies the packet, and caches it until receiving the acknowledgment from the next en route node. It incurs negligible latency to packet forwarding when there is no packet loss while retransmitting the packet at the en route node after a short timeout, which reduces the packet forwarding latency. Besides that, the per-packet verification guarantees that the adversary could not subvert the forwarding protocol. Our simulation in the BMv2 environment confirms its functionality, and the hardware implementation demonstrates that it can process packets at line rate with a total processing latency ranging from 2519 ns to 6160 ns, which is negligible in end-to-end transmission.

Encrypted traffic analysis is crucial for cyberspace security. Self-supervised learning shows great promise to enhance traffic analysis with the pre-trained traffic encoder, which is constructed using large-scale, readily available unlabeled traffic data. However, existing approaches struggle to handle the increasingly prevalent encrypted traffic, as their generative reconstruction tasks cannot process encrypted content. To this end, we propose TACO, a robust and flexible encrypted traffic analysis system based on flow semantics learning. Specifically, we first design several feasible traffic data augmentation strategies to prepare flow semantics knowledge from the unlabeled traffic. Then, our traffic encoder with a traffic partition module learns the semantics knowledge based on the contrastive pre-training paradigm. It serves as a traffic foundation encoder that can comprehend flow semantics and extract effective semantic representations. Finally, we fine-tune the traffic encoder to leverage flow semantics for various downstream encrypted traffic analysis tasks. The experimental results illustrate that TACO outperforms the optimal baseline by 7.5% in average F1 score on four traffic classification datasets and achieves an improvement of at least 11.62% in average F1 score on the three transfer tasks, while indicating superior efficiency. We will release the source code as well as the experiment data upon publication to foster future research.

Website fingerprinting enables an eavesdropper to determine which websites a user is visiting over an encrypted connection. State-of-the-art website fingerprinting (WF) attacks have demonstrated effectiveness even against Tor-protected network traffic. However, existing WF attacks have critical limitations on accurately identifying websites in multi-tab browsing sessions, where the holistic pattern of individual websites is no longer preserved, and the number of tabs opened by a client is unknown a priori. In this paper, we propose ARES, a novel WF framework natively designed for multi-tab WF attacks. ARES formulates the multi-tab attack as a multi-label classification problem and solves it using the novel Transformer-based models. Specifically, ARES extracts local patterns based on multi-level traffic aggregation features and utilizes the improved self-attention mechanism to analyze the correlations between these local patterns, effectively identifying websites. We implement a prototype of ARES and extensively evaluate its effectiveness using our large-scale datasets collected over multiple months. The experimental results illustrate that ARES achieves optimal performance in several realistic scenarios. Further, ARES remains robust even against various WF defenses.

Machine learning (ML) is promising in accurately detecting malicious flows in encrypted network traffic; however, it is challenging to collect a training dataset that contains a sufficient amount of encrypted malicious data with correct labels. When ML models are trained with low-quality training data, they suffer degraded performance. In this paper, we aim to address a real-world low-quality training dataset problem, namely, detecting encrypted malicious traffic generated by continuously evolving malware. We develop RAPIER+ that fully utilizes different distributions of normal and malicious traffic data in the feature space, where normal data is tightly distributed in a certain area, and the malicious data is scattered over the entire feature space to augment training data for model training. RAPIER+ includes two pre-processing modules to convert traffic into feature vectors and correct label noises. We evaluate our system on two public datasets and one combined dataset. With 1000 samples and 45% noise from each dataset, our system achieves the F1 scores of 0.78, 0.84, and 0.87, respectively, achieving average improvements of 358.5%, 314.0%, and 221.1% over the existing methods, respectively. Furthermore, we evaluate RAPIER+ with a real-world dataset obtained from a security enterprise. RAPIER+ effectively achieves encrypted malicious traffic detection with the best F1 score of 0.81 and improves the F1 score of existing methods by an average of 288.7%.

Most existing DL-based encrypted traffic classification methods suffer performance degradation in real-world deployments due to dynamic network conditions, e.g., network environment changes and traffic obfuscation. Dynamic network conditions cause encrypted traffic to exhibit distinct feature patterns during training and testing phases. To address this issue, we propose MetaTraffic, a novel and general DL training framework built upon meta-learning that enhances the performance of supervised DL models designed for encrypted traffic classification against dynamic network conditions. Our key observation is that the traffic of the same network behaviors share the same semantic features even under different network conditions, which can be considered as stable feature representations. Therefore, MetaTraffic helps DL models learn stable feature representations by minimizing the discrepancies in how the models represent traffic features under different network conditions, thereby achieving robust classification under dynamic network conditions. We implement MetaTraffic based on meta-learning with three innovative facilitate modules to enhance its performance. We evaluate MetaTraffic using three public datasets and three new large-scale encrypted traffic datasets that cover multiple types of network conditions. Experimental results show that, under dynamic multiple types of network conditions, our framework improves the accuracy of DL models by 8.94% and the F1-Macro score by 12.55%, while existing robust training methods decrease the accuracy by 28.85% and the F1-Macro score by 33.52%.

Network traffic analysis is a critical research area, playing an essential role in enhancing network security and ensuring high-quality network services. Existing methods, which primarily rely on a single modality, face two significant limitations. First, while existing approaches may achieve strong performance in specific tasks, they often lack sufficient adaptability for diverse tasks. Second, existing pre-trained models are only trained with GB-scale traffic, with which increases the risk of over-fitting and limiting the models' overall performance. To address these challenges, we propose MM4flow, a pre-trained multi-modal model designed for versatile network traffic analysis. We divide network flows into two modalities: raw byte streams and transmission patterns, which encapsulate the content and behavior information, respectively. MM4flow is composed of two key stages: uni-modal pre-training and multi-modal fine-tuning. We develop an efficient data collection scheme enabling TB-scale traffic pre-training. Leveraging a real-world traffic that exceeds 70 TB, MM4flow conducts uni-modal pre-training on each modality with a modified BERT architecture tailored for network flows. For specific downstream tasks, we introduce a modal fusion module based on cross-attention mechanisms. The fusion module facilitates effective integration of multi-modal information, enabling MM4flow to fully utilize both content and behavior cues during fine-tuning with minimal labeled dataset. We evaluate MM4flow on six public datasets covering six various tasks. Extensive experiments demonstrate that MM4flow achieves superior accuracy than baselines. Especially, compared to existing pre-trained models, MM4flow achieves an 84% improvement in accuracy for website identification under encrypted tunnels. Moreover, the pre-trained MM4flow significantly reduces the reliance on high-quality labeled training data for downstream tasks.

Collaborative graph processing refers to the joint analysis of inter-connected graphs held by multiple graph owners. To honor data privacy and support various graph processing algorithms, existing approaches employ secure multi-party computation (MPC) protocols to express the vertex-centric abstraction. Yet, due to certain computation-intensive cryptography constructions, state-of-the-art (SOTA) approaches are asymptotically suboptimal, imposing significant overheads in terms of computation and communication. In this paper, we present RingSG, the first system to attain optimal communication/computation complexity within the MPC-based vertex-centric abstraction for collaborative graph processing. This optimal complexity is attributed to Ring-ScatterGather, a novel computation paradigm that can avoid exceedingly expensive cryptography operations (e.g., oblivious sort), and simultaneously ensure the overall workload can be optimally decomposed into parallelizable and mutually exclusive MPC tasks. Within Ring-ScatterGather, RingSG improves the concrete runtime efficiency by incorporating 3-party secure computation via share conversion, and optimizing the most cost-heavy part using a novel oblivious group aggregation protocol. Finally, unlike prior approaches, we instantiate RingSG into two end-to-end applications to effectively obtain application-specific results from the protocol outputs in a privacy-preserving manner. We developed a prototype of RingSG and extensively evaluated it across various graph collaboration settings, including different graph sizes, numbers of parties, and average vertex degrees. The results show RingSG reduces the system running time of SOTA approaches by up to 15.34× and per-party communication by up to 10.36×. Notably, RingSG excels in processing sparse global graphs collectively held by more parties, consistent with our theoretical cost analysis.

Privacy-sensitive users require deploying large language models (LLMs) within their own infrastructure (on-premises) to safeguard private data and enable customization.However, vulnerabilities in local environments can lead to unauthorized access and potential model theft.To address this, prior research on small models has explored securing only the output layer within hardware-secured devices to balance model confidentiality and customization.Yet this approach fails to protect LLMs effectively.In this paper, we discover that (1) query-based distillation attacks targeting the secured top layer can produce a functionally equivalent replica of the victim model; (2) securing the same number of layers, bottom layers before a transition layer provide stronger protection against distillation attacks than top layers, with comparable effects on customization performance; and (3) the number of secured layers creates a trade-off between protection and customization flexibility.Based on these insights, we propose SOLID, a novel deployment framework that secures a few bottom layers in a secure environment and introduces an efficient metric to optimize the trade-off by determining the ideal number of hidden layers.Extensive experiments on five models (1.3B to 70B parameters) demonstrate that SOLID outperforms baselines, achieving a better balance between protection and downstream customization.Our code can be found at: https://github.com/ OTTO-OTO/SOLID-OnPremiseDeployment.

To effectively manage and utilize massive distributed data at the network edge, Federated Learning (FL) has emerged as a promising edge computing paradigm across data silos. However, FL still faces two challenges: system heterogeneity (i.e., the diversity of hardware resources across edge devices) and statistical heterogeneity (i.e., non-IID data). Although sparsification can extract diverse submodels for diverse clients, most sparse FL works either simply assign submodels with artificially-given rigid rules or prune partial parameters using heuristic strategies, resulting in inflexible sparsification and poor performance. In this work, we propose Learnable Personalized Sparsification for heterogeneous Federated learning (FedLPS), which achieves the learnable customization of heterogeneous sparse models with importance-associated patterns and adaptive ratios to simultaneously tackle system and statistical heterogeneity. Specifically, FedLPS learns the importance of model units on local data representation and further derives an importance-based sparse pattern with minimal heuristics to accurately extract personalized data features in non-IID settings. Furthermore, Prompt Upper Confidence Bound Variance (P-UCBV) is designed to adaptively determine sparse ratios by learning the superimposed effect of diverse device capabilities and non-IID data, aiming at resource self-adaptation with promising accuracy. Extensive experiments show that FedLPS outperforms status quo approaches in accuracy and training costs, which improves accuracy by 1.28% – 59.34% while reducing running time by more than 68.80%.

Network traffic classification is essential for network management and security. In recent years, deep learning (DL) algorithms have emerged as essential tools for classifying complex traffic. However, they rely heavily on high-quality labeled training data. In practice, traffic data is often noisy due to human error or inaccurate automated labeling, which could render classification unreliable and lead to severe consequences. Although some studies have alleviated the label noise issue in specific scenarios, they are difficult to generalize to general traffic classification tasks due to the inherent semantic complexity of traffic data. In this paper, we propose FlowRefiner, a robust and general traffic classification framework against label noise. FlowRefiner consists of three core components: a traffic semantics-driven noise detector, a confidence-guided label correction mechanism, and a cross-granularity robust classifier. First, the noise detector utilizes traffic semantics extracted from a pre-trained encoder to identify mislabeled flows. Next, the confidence-guided label correction module fine-tunes a label predictor to correct noisy labels and construct refined flows. Finally, the cross-granularity robust classifier learns generalized patterns of both flow-level and packet-level, improving classification robustness against noisy labels. We evaluate our method on four traffic datasets with various classification scenarios across varying noise ratios. Experimental results demonstrate that FlowRefiner mitigates the impact of label noise and consistently outperforms state-of-the-art baselines by a large margin. The code is available at https://github.com/NSSL-SJTU/FlowRefiner.

The paradigm of Intelligent DataPlane (IDP) embeds deep learning (DL) models on the network dataplane to enable intelligent traffic analysis at line-speed. However, the current use of the match-action table (MAT) abstraction on the dataplane is misaligned with DL inference, leading to several key limitations, including accuracy degradation, limited scale, and lack of generality. This paper proposes Pegasus to address these limitations. Pegasus translates DL operations into three dataplane-oriented primitives to achieve generality: Partition, Map, and SumReduce. Specifically, Partition "divides" high-dimensional features into multiple low-dimensional vectors, making them more suitable for the dataplane; Map "conquers" computations on the low-dimensional vectors in parallel with the technique of Fuzzy Matching, while SumReduce "combines" the computation results. Additionally, Pegasus employs Primitive Fusion to merge computations, improving scalability. Finally, Pegasus adopts full-precision weights with fixed-point activations to improve accuracy. Our implementation on a P4 switch demonstrates that Pegasus can effectively support various types of DL models, including Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and AutoEncoder models on the dataplane. Meanwhile, Pegasus outperforms state-of-the-art approaches with an average accuracy improvement of up to 22.8%, along with up to 248× larger model size and 212× larger input scale.

Traffic data contains deep domain-specific knowledge, making labeling challenging, and the lack of labeled data adversely impacts the accuracy of learning-based traffic analysis. The pre-training technology is widely adopted in the fields of vision and natural language to address the problem of limited labeled data. However, the exploration in the domain of traffic analysis remains insufficient. This paper proposes an efficient pre-training model, TrafficFormer, for traffic data. In the pre-training stage, TrafficFormer introduces a fine-grained multi-classification task to enhance the representation capabilities of traffic data; in the fine-tuning stage, TrafficFormer proposes a traffic data augmentation method utilizing the random initialization feature of fields, which helps the traffic model focus on key information. We evaluate TrafficFormer using both traffic classification tasks and protocol understanding tasks. The experimental results show that TrafficFormer achieves superior performance on six traffic classification datasets, with improvements of up to 10% in the F1 score and demonstrates significantly superior protocol understanding capabilities compared to existing traffic pre-training models.

ECN marking has been integrated into datacenter switches to enable high-throughput and low-latency transport. We observe that current marking schemes are coarse-grained: they blindly mark all flows when congestion occurs, causing large flows to occupy undeserved bandwidth and preventing newly arriving small flows from finishing quickly. In this paper, we propose DiffECN, a differential marking strategy that marks only the flows that are the culprits of congestion and protects the remaining flows from being limited. We have implemented it in the Barefoot Tofino switch and performed extensive evaluations via both physical testbed and large-scale simulations. The results show that DiffECN can restrain flows responsible for congestion successfully while providing desirable network performance. For instance, compared to the legacy way of ECN marking, DiffECN achieves up to 32.5% (40.1%) lower average (99th percentile) flow completion time (FCT) for small flows while delivering similar FCT for large flows under production workloads.

Random Early Detection (RED) has been integrated into datacenter switches as a fundamental Active Queue Management (AQM) for decades. The accurate configuration of RED parameters is crucial to achieving high throughput and low latency. However, due to the highly dynamic nature of workloads in datacenter networks, maintaining consistently high performance with statically configured RED thresholds poses a challenge. Prior work applies reinforcement learning to predict proper thresholds, but their real-world deployment has been hindered by poor tail performance caused by instability. In this paper, we propose$\textsf {PRED}$, a novel system that enables automatic and stable RED parameter adjustment in response to traffic dynamics. Specifically, the system employs a Multiplicative-Increase Multiplicative-Decrease (MIMD) strategy to dynamically adapt to flow concurrency while utilizing an Additive-Increase Additive-Decrease (AIAD) mechanism to adapt to flow distribution. We perform extensive evaluations on our physical testbed and large-scale simulations. The results demonstrate that$\textsf {PRED}$can keep up with the real-time network dynamics generated by realistic workloads. For instance, compared with the static-threshold-based methods,$\textsf {PRED}$keeps 66% shorter switch queue length and obtains up to 80% lower Flow Completion Time (FCT). Compared with the state-of-the-art learning-based method,$\textsf {PRED}$reduces the tail FCT by 34%.

Website Fingerprinting (WF) attacks can effectively identify the websites visited by Tor clients via analyzing encrypted traffic patterns. Existing attacks focus on identifying different websites, but their accuracy dramatically decreases when applied to identify fine-grained webpages, especially when distinguishing among different subpages of the same website. WebPage Fingerprinting (WPF) attacks face the challenges of highly similar traffic patterns and a much larger scale of webpages. Furthermore, clients often visit multiple webpages concurrently, increasing the difficulty of extracting the traffic patterns of each webpage from the obfuscated traffic. In this paper, we propose Oscar, a WPF attack based on multi-label metric learning that identifies different webpages from obfuscated traffic by transforming the feature space. Oscar can extract the subtle differences among various webpages, even those with similar traffic patterns. In particular, Oscar combines proxy-based and sample-based metric learning losses to extract webpage features from obfuscated traffic and identify multiple webpages. We prototype Oscar and evaluate its performance using traffic collected from 1,000 monitored webpages and over 9,000 unmonitored webpages in the real world. Oscar demonstrates an 88.6% improvement in the multi-label metric Recall@5 compared to the state-of-the-art attacks.

Collaborative graph learning represents a learning paradigm where multiple parties jointly train a graph neural network (GNN) using their own proprietary graph data. To honor the data privacy of all parties, existing solutions for collaborative graph learning are either based on federated learning (FL) or secure machine learning (SML). Although promising in terms of efficiency and scalability due to their distributed training scheme, FL-based approaches fall short in providing provable security guarantees and achieving good model performance. Conversely, SML-based solutions, while offering provable privacy guarantees, are hindered by their high computational and communication overhead, as well as poor scalability as more parties participate.

The increased adoption of smart contracts in many industries has made them an attractive target for cybercriminals, leading to millions of dollars in losses. Thus, continuously fixing newly found vulnerabilities of smart contracts becomes a routine software maintenance task for running smart contracts. However, fixing the vulnerabilities that are specific to the smart contract domain requires security knowledge that many developers lack. Without effective tool support, this task can be very costly in terms of manual labor. To fill this critical need, in this paper, we propose VFIX, which automatically generates security patches for vulnerable smart contracts. In particular, VFIX provides a novel program analysis framework that can incorporate different fix patterns for fixing various types of vulnerabilities. To address the unique challenges in accurately fixing smart contract vulnerabilities, VFIX innovatively combines template-based repair with a set of static program analysis techniques specially designed for smart contracts. Specifically, given an input smart contract, VFIX conducts ensemble identification based on multiple static verification tools to identify vulnerabilities for an automatic fix. Then, VFIX generates patches using template-based fix patterns, and conducts static program analysis (e.g., program dependency computation, pointer analysis) for smart contracts to accurately infer and populate the parameter values for the fix templates. Finally, VFIX performs static verification to ensure that the patched contract is free of vulnerabilities. Our evaluations on 144 real smart contracts containing different types of vulnerabilities show that VFIX can successfully fix 94% of the vulnerabilities and preserve the expected normal behaviors of the smart contracts.

Distributed Denial of Service (DDoS) attack is a critical and persistent threat to the Internet. Recent DDoS detection schemes based on emerging programmable switches can achieve higher processing throughput and improve detection accuracy. However, with limited data plane memory, such schemes are not suitable for handling a large number of concurrent flows. Prior arts that attempt to increase memory efficiency have failed to do so without the expense of cost and accuracy. In this paper, we propose NetSentry, the first programmable switch based dynamic pooled testing DDoS detector. NetSentry detects DDoS in a pooled testing manner, where multiple flows are grouped to share the same storage unit on the data plane. NetSentry designs an elastic flow aggregation mechanism to dynamically adjust the detection granularity. Further, to achieve accurate DDoS detection for aggregated flows, NetSentry implements frequency domain DDoS detection on programmable switches. Evaluations of NetSentry’s hardware prototype show that NetSentry can achieve better accuracy while saving up to 91% of the data plane memory required to store flow features compared to the state-of-the-art programmable switch-based flow classification scheme.

Defending against Distributed Denial of Service (DDoS) attacks is a fundamental problem in the Internet. Over the past few decades, the research and industry communities have proposed a variety of solutions, from adding incremental capabilities to the existing Internet routing stack, to clean-slate future Internet architectures, and to widely deployed commercial DDoS prevention services. Yet a recent interview with over 100 security practitioners in multiple sectors reveals that existing solutions arestill insufficient against, due to either unenforceable protocol deployment or non-comprehensive traffic filters. This seemingly endless arms race with attackers probably means that we need a fundamental paradigm shift. In this paper, we propose a new DDoS prevention paradigm namedpreference-driven and in-network enforced traffic shaping, aiming to explore the novel DDoS prevention norms that focus on delivering victim-preferred traffic rather than consistently chasing after the DDoS attacks. Towards this end, we propose DFNet, a novel DDoS prevention system that provides reliable delivery of victim-preferred trafficwithoutfull knowledge of DDoS attacks. At a very high level, the core innovative design of DFNet embraces the advances in Machine Learning (ML) and new network dataplane primitives, byencodingthe victim's traffic preference (in the form of complex ML models) into dataplane packet scheduling algorithms such that the victim-preferred traffic is forwarded with priority at line-speed, regardless of the attacker strategy. We implement a prototype of DFNet in 11,560 lines of code, and extensively evaluate it on our testbed. The results show thata single instanceof DFNet can forward 99.93% of victim-desired traffic when facing previously unseen attacks, while imposing less than 0.1% forwarding overhead on a dataplane with 80 Gbps upstream links and a 40 Gbps bottleneck.

The vulnerability of deep neural networks against adversarial attacks,i.e., imperceptible adversarial perturbations can easily give rise to wrong predictions, poses a huge threat to the security of their real-world deployments. In this paper, a novel Adversarial Detection method via Disentangling Natural images and Perturbations (ADDNP) is proposed. Compared to natural images that can typically be modeled by lower-dimensional subspaces or manifolds, the distributions of adversarial perturbations are much more complex,e.g., one normal example’s adversarial counterparts generated by different attack strategies can be significantly distinct. The proposed ADDNP exploits such distinct properties for the detection of adversarial attacks amongst normal examples. Specifically, we use a dual-branch disentangling framework to encode natural images and perturbations of inputs separately, followed by joint reconstruction. During inference, the reconstruction discrepancy (RD) measured in the learned latent feature space is used as an indicator of adversarial perturbations. The proposed ADDNP algorithm is evaluated on three popular datasets,i.e., CIFAR-10, CIFAR-100, andminiImageNet with increasing data complexity, across multiple popular attack strategies. Compared to the existing and state-of-the-art detection methods, ADDNP has demonstrated promising performance on adversarial detection, with significant improvements on more challenging datasets.

With the increasing demand for blockchain technology in various industry sectors, there has been a growing interest in the Byzantine Fault Tolerance (BFT) consensus that is the backbone of most of these blockchains. However, many state-of-the-art algorithms that require reliable connections can only offer limited throughput in wide-area networks (WANs), where participants are connected over long distances and may experience unpredictable network failures. The partially-connected BFTs are designed for unreliable and highly dynamic networks yet impose exponential communication complexity. This paper proposes Stable Byzantine Fault Tolerance (SBFT), a BFT communication abstraction that can sustain high throughput and low latency in WAN. SBFT separates the leader from consensus in pipelined BFT consensus and uses an adaptive consensus mechanism to resist dynamic faulty links, maintaining consensus efficiency when network connectivity is high while adapting to dynamic networks with low connectivity. We implemented a prototype of SBFT and tested it on the WAN. The results demonstrate that SBFT has a throughput similar to HotStuff in a fault-free environment but can reduce about 80% of consensus latency. Besides, SBFT retains 40% of the original throughput when the link failure probability is 0.4, while the baseline HotStuff retains less than 40% when the link failure probability is only 0.1.

The development of machine learning models requires a large amount of training data. Data marketplace is a critical platform to trade high-quality and private-domain data that is not publicly available on the Internet. However, as data privacy becomes increasingly important, directly exchanging raw data becomes inappropriate. Federated Learning (FL) is a distributed machine learning paradigm that exchanges data utilities (in form of local models or gradients) among multiple parties without directly sharing the original data. However, we recognize several key challenges in applying existing FL architectures to construct a data marketplace. (i) In existing FL architectures, the Data Acquirer (DA) cannot privately assess the quality of local models submitted by different Data Providers (DPs) prior to trading; (ii)The model aggregation protocols in existing FL designs cannot effectively exclude malicious DPs without "overfitting'' to the DA's (possibly biased) root dataset; (iii) Prior FL designs lack a proper billing mechanism to enforce the DA to fairly allocate the reward according to contributions made by different DPs. To address above challenges, we propose martFL, the first federated learning architecture that is specifically designed to enable a secure utility-driven data marketplace. At a high level, martFL is empowered by two innovative designs: (i) a quality-aware model aggregation protocol that allows the DA to properly exclude local-quality or even poisonous local models from the aggregation, even if the DA's root dataset is biased; (ii) a verifiable data transaction protocol that enables the DA to prove, both succinctly and in zero-knowledge, that it has faithfully aggregated these local models according to the weights that the DA has committed to. This enables the DPs to unambiguously claim the rewards proportional to their weights/contributions. We implement a prototype of martFL and evaluate it extensively over various tasks. The results show that martFL can improve the model accuracy by up to 25% while saving up to 64% data acquisition cost.

Recently unsupervised machine learning based systems have been developed to detect zero-day Web attacks, which can effectively enhance existing Web Application Firewalls (WAFs). However, prior arts only consider detecting attacks on specific domains by training particular detection models for the domains. These systems require a large amount of training data, which causes a long period of time for model training and deployment. In this paper, we propose RETSINA, a novel meta-learning based framework that enables zero-day Web attack detection across different domains in an organization with limited training data. Specifically, it utilizes meta-learning to share knowledge across these domains, e.g., the relationship between HTTP requests in heterogeneous domains, to efficiently train detection models. Moreover, we develop an adaptive preprocessing module to facilitate semantic analysis of Web requests across different domains and design a multi-domain representation method to capture semantic correlations between different domains for cross-domain model training. We conduct experiments using four real-world datasets on different domains with a total of 293M Web requests. The experimental results demonstrate that RETSINA outperforms the existing unsupervised Web attack detection methods with limited training data, e.g., RETSINA needs only 5-minute training data to achieve comparable detection performance to the existing methods that train separate models for different domains using 1-day training data. We also conduct real-world deployment in an Internet company. RETSINA captures on average 126 and 218 zero-day attack requests per day in two domains, respectively, in one month.

Embracing software-driven smart contracts to fulfill legal agreements is a promising direction for digital transformation in the legal sector. Existing solutions mostly consider smart contracts as simple add-ons, without leveraging the programmability of smart contracts to realize complex semantics of legal agreements. In this paper, we propose iSyn, the first end-to-end system that synthesizes smart contracts to fulfill the semantics of financial legal agreements, with minimal human interventions. The design of iSyn centers around a novel intermediate representation (SmartIR) that closes the gap between the natural language sentences and smart contract statements. Specifically, iSyn includes a synergistic pipeline that unifies multiple NLP-techniques to accurately construct SmartIR instances given legal agreements, and performs template-based synthesis based on the SmartIR instances to synthesize smart contracts. We also design a validation framework to verify the correctness and detect known vulnerabilities of the synthesized smart contracts.We evaluate iSyn using legal agreements centering around financial transactions. The results show that iSyn-synthesized smart contracts are syntactically similar and semantically correct (or within a few edits), compared with the “ground truth” smart contracts manually developed by inspecting the legal agreements.

Website fingerprinting enables an eavesdropper to determine which websites a user is visiting over an encrypted connection. State-of-the-art website fingerprinting (WF) attacks have demonstrated effectiveness even against Tor-protected network traffic. However, existing WF attacks have critical limitations on accurately identifying websites in multi-tab browsing sessions, where the holistic pattern of individual websites is no longer preserved, and the number of tabs opened by a client is unknown a priori. In this paper, we propose ARES, a novel WF framework natively designed for multi-tab WF attacks. ARES formulates the multi-tab attack as a multi-label classification problem and solves it using a multi-classifier framework. Each classifier, designed based on a novel transformer model, identifies a specific website using its local patterns extracted from multiple traffic segments. We implement a prototype of ARES and extensively evaluate its effectiveness using our large-scale dataset collected over multiple months (by far the largest multi-tab WF dataset studied in academic papers.) The experimental results illustrate that ARES effectively achieves the multi-tab WF attack with the best F1-score of 0.907. Further, ARES remains robust even against various WF defenses.

In the era of deep learning, it is critical to protect the intellectual property of high-performance deep neural network (DNN) models. Existing proposals, however, are subject to adversarial ownership forgery (e.g., methods based on watermarks or fingerprints) or require full access to the original training dataset for ownership verification (e.g., methods requiring the replay of the learning process). In this paper, we propose a novel Provenance of Training (PoT) scheme, the first empirical study towards verifying DNN model ownership without accessing any original dataset while being robust against existing attacks. At its core, PoT relies on a coherent model chain built from the intermediate checkpoints saved during model training to serve as the ownership certificate. Through an in-depth analysis of model training, we propose six key properties that a legitimate model chain shall naturally hold. In contrast, it is difficult for the adversary to forge a model chain that satisfies these properties simultaneously without performing actual training. We systematically analyze PoT’s robustness against various possible attacks, including the adaptive attacks that are designed given the full knowledge of PoT’s design, and further perform extensive empirical experiments to demonstrate our security analysis.

Despite the bandwidth scaling limit of electrical switching and the high cost of building Clos data center networks (DCNs), the adoption of optical DCNs is still limited. There are two reasons. First, existing optical DCN designs usually face high deployment complexity. Second, these designs are not full-optical and the performance benefit over the non-blocking Clos DCN is not clear. After exploring the design tradeoffs of the existing optical DCN designs, we propose TROD (ThresholdRouting basedOpticalDatacenter), a low-complexity optical DCN with superior performance than other optical DCNs. There are two novel designs in TROD that contribute to its success. First, TROD performs robust topology optimization based on the recurring traffic patterns and thus does not need to react to every traffic change, which lowers deployment and management complexity. Second, TROD introduces tVLB (threshold-based Valiant Load Balance), which can avoid network congestion as much as possible even under unexpected traffic bursts. We conduct simulation based on both Facebook’s real DCN traces and our synthesized highly bursty DCN traces. TROD reduces flow completion time (FCT) by about 1.15-2.16$\times$compared to Google’s Jupiter DCN, at least 2$\times$compared to other optical DCN designs, and about 2.4-3.2$\times$compared to expander graph DCN. Compared with the non-blocking Clos, TROD reduces the hop count of the majority packets by one, and could even outperform the non-blocking Clos with proper bandwidth over-provision at the optical layer. Note that TROD can be built with commercially available hardware and does not require host modifications.

Internet content providers often deliver content through bandwidth bottlenecks that are out of their control. Thus, despite often having massively over-provisioned upstream servers, the content providers still cannot control the end-to-end user experience. This paper explores remote traffic shaping, allowing the content provider to allocate its share of a remote bottleneck link across its users using a metric other than TCP fairness, while remaining TCP-friendly to cross traffic on the bottleneck link. To evaluate this approach, we designed FlowTele, the first system that shapes outbound traffic on an Internet-scale network to optimize provider-selected metrics, using source control with neither in-network support nor special client support. Our extensive evaluations over the Internet show that by strategically reallocating bandwidth among provider-owned co-bottlenecked flows, FlowTele improves the provider's total revenue by roughly 20%--30% in various network settings, compared with both (i) status quo TCP fairshare and (ii) recent practice by content providers that proactively throttles video quality during the COVID-19 pandemic, while being TCP-friendly to cross-traffic. Besides revenue, we also study other metrics, such as QoE fairness, that a content provider may wish to optimize using FlowTele.

The IP protocol has made a great contribution to the development of the Internet and has become the narrow waist of the Internet. However, the fixed packet processing of IP hinders the functional expansion and evolution of the Internet. In order to solve the rigidity of the Internet, our community has proposed various new L3 protocols to better support various network functions at the network layer. In this paper, we propose DIP (Dynamic Internet Protocol), a novel primitive to unify these protocols. DIP builds a common network function core shared by these L3 protocols based on a new L3 function core primitive, named Field Operation (FN). With FNs, each standalone L3 protocol can be decomposed into a combination of multiple FNs, and meanwhile it is feasible to compose various FNs to realize new (derived) L3 protocols. We demonstrate the feasibility of DIP by realizing five radically different network layer protocols1: the canonical IP forwarding, NDN [41], XIA [12], OPT [16], and NDN+OPT (a derived L3 protocol combining the merits of both NDN and OPT). We implement a prototype of DIP and evaluate its forwarding performance.

Abnormal data access operations in database systems always hap-pen, which are typically incurred by misoperations or attacks, though these systems are enforced with strict access control policies. However, prior arts only focus on detecting abnormal data accesses by utilizing known attack patterns or identifying behaviors significantly deviated from normal behaviors. They cannot capture stealthy abnormal data access operations that are similar to normal ones. In this paper, we propose a novel unsupervised anomaly detection system UCAD, which aims to detect abnormal data access operations, by comparing operation's semantics with their contextual intent. However, it is non-trivial to obtain accurate semantics of operations for intent analysis because (i) the same operation may exhibit diverse semantics under different operation contexts and (ii) different operation sequences could have identical semantics due to heterogeneous user access patterns. To address this issue, we develop a new transformer model called Trans-DAS for UCAD. Trans-DAS learns the semantics of individual operations by utilizing the attention mechanism that analyzes the relevance between any pair of operations in sequence, and captures the contextual intent of operations inferred from the contexts. Specifically, Trans-DAS utilizes a particular embedding layer to embed the semantics of individual operations without the operation order information and a masking mechanism that allows Trans-DAS to learn the semantics according to the bidirectional contexts. Also, we define a new training objective for Trans-DAS to enlarge the difference among the embedded semantics. Furthermore, in order to effectively utilize Trans-DAS for detection, we develop two modules in UCAD, i.e., a data preprocessing module that allows Trans-DAS to accurately learn the normal semantic information by removing noisy data, and an anomaly detection module that learns the semantic information for intent comparison. We evaluate the performance of UCAD on real-world data traces under different settings (e.g., varied parameters and hybrid datasets). The results demonstrate that UCAD achieves the average F1-score of 0.94 in two scenarios, which significantly outperform baselines, and shows robustness to hybrid data and good transferability to different tasks.

${\mathsf Web3.0}$, often cited to drastically shape our lives, is ubiquitous. However, few literatures have discussed the crucial differentiators that separate${\mathsf Web3.0}$from the era we are currently living in. Via a thorough analysis of the recent blockchain infrastructure evolution, we capture a key invariant featuring the evolution, based on which we provide the first academic definition for${\mathsf Web3.0}$. Our definition is not the only way of understanding${\mathsf Web3.0}$, yet, it captures the fundamental and defining trait of${\mathsf Web3.0}$, and meanwhile it is has two desirable properties. Under this definition, we articulate three key categories of infrastructural enablers for${\mathsf Web3.0}$: individual smart-contract capable blockchains, federated or centralized platforms capable of publishing verifiable states, and an interoperability platform to hyperconnect those state publishers to provide a unified and connected computing platform for${\mathsf Web3.0}$applications. While innovations in all categories are necessary to fully enable${\mathsf Web3.0}$, in this article, we present a design for the third enabler, i.e., the first interoperability platform, namely${\mathsf HyperService}$, that advances the state-of-the-art by simultaneously deliversinteroperabilityandprogrammabilityacrossheterogeneousblockchains and state publishers.${\mathsf HyperService}$is powered by two innovative designs:${\mathsf (i)}$a developer-facing programming framework that allows developers to build cross-chain applications in a unified programming model; and${\mathsf (ii)}$a secure blockchain-facing cryptography protocol that provably realizes those applications on blockchains. We implement a prototype of${\mathsf HyperService}$in approximately 62,000 lines of code to demonstrate its practicality, usability and scalability.

In Website Fingerprinting (WF) attack, a local passive eavesdropper utilizes network flow information to identify which web pages a user is browsing. Previous researchers have demonstrated the feasibility and effectiveness of WF attacks under a strong Single Page Assumption: the network flow extracted by the adversary belongs to a single web page. In reality, the assumption may not hold because users tend to open multiple tabs simultaneously (or within a short period of time) so that their network traffic is mixed. In this article, we propose an automated multi-tab Website Fingerprinting attack that is able to accurately classify websites regardless of the number of simultaneously opened pages. Our design is powered by two innovative designs. First, we develop a split point classification method to dynamically identify the split point between the first page and its subsequent pages. As a result, the network traffic before the split point is solely generated for the first page. Then, we propose a new chunk-based WF classifier to infer the websites based on the initial chunk of clean traffic. For both classifiers, we apply automated feature selection to select a concise yet representative feature set. We implement a prototype of our design and perform extensive evaluations using SSH and Tor-based datasets to demonstrate the effectiveness of both our system components individually and the integrated system as a whole.

Network Function Virtualization (NFV) is a new networking paradigm to enable dynamic network function deployment in networks. Existing studies focused on optimized function deployment and management in NFV. Unfortunately, these studies did not well address the problem of efficient security function enforcement in networks, which is the goal of deploying network functions (NFs), i.e., for real-time security function enforcement on the traffic, since optimal function deployment does not mean efficient security function enforcement on network traffic. In particular, they incurred significant NF enforcement cost. In order to address this issue, in this paper, we propose${\textsf {FuncE}}$that aims to solve the efficient real-time security function enforcement problem by developing unified dynamic flow and function scheduling. We formulate the problem as an integer linear programming problem and prove that it is NP-hard. We tackle the problem by decomposing it and developing heuristics to achieve near-optimal solutions. We conduct comprehensive experiments by using real topologies to demonstrate the effectiveness of the${\textsf {FuncE}}$design. The experimental results demonstrate that${\textsf {FuncE}}$achieves near-optimal network function enforcement, which incurs over 100 times less latency than the existing the optimal solver. In particular, compared to the state-of-art defenses,${\textsf {FuncE}}$processes the same number of candidate flows using over 50% less VNFs, while ensuring the same level of function enforcement.

Reinforcement Learning (RL) has been applied to solve decision-making problems in computer network designs, especially in TCP congestion control. As RL-based congestion control methods enable powerful learning abilities, it achieves competitive performance and adaptiveness advantages over the traditional methods. However, RL-based systems suffer from adversarial attacks that generate perturbations to significantly degrade the performance. In this paper, we conduct a comprehensive study of adversarial attacks against RL-based congestion control systems. Unlike the state-of-the-art adversarial attacks on images where an attacker can easily obtain the input states to introduce perturbations, the attacker cannot directly obtain the input states in congestion control settings that are only available to the agents. It is challenging to add effective perturbations without knowing the input states for RL-based congestion control models. To solve the challenge, we develop an adversarial attack to estimate states of the target agent, craft adversarial perturbations, and apply the generated perturbations in an automated fashion. We evaluate how our adversarial attack affects the target agent’s decision-making process. Our experiments illustrate that our attack can effectively reduce about 50% average throughput while increasing more than 36x latency and 45% packet loss rate.

Privacy-centric mobile social network (PC-MSN), which allows users to build intimate and private social circles, is an increasingly popular type of online social networks (OSNs). Because of strict usage policy enforced by PC-MSNs (such as restricted account and content access), malicious accounts (or users) have to act like normal accounts to accumulate credentials before committing malicious activities. Therefore, analysis merely relying on static account profile information or social graphs is ineffective to detect such growing-up accounts. Besides, existing behavior-based malicious account detection methods fail to effectively detect growing-up accounts who pretend to be benign and have similar behaviors to benign users during the growing-up stage.

RPCs are fundamental to our large-scale distributed system. From a security perspective, the blast radius of RPCs is worryingly big since each RPC often interacts with tens of internal system components. Thus, discovering RPC vulnerabilities is often a top priority in the software quality assurance process for production systems. In this paper, we present the design, implementation, and deployment experiences of PAIR, a fully automated system for privilege-escalation vulnerability discovery in Ant Group's large-scale RPC system. The design of PAIR centers around the live replay design principle where the vulnerability discovery is driven by the live RPC requests collected from production, rather than relying on any engineered testing requests. This ensures that PAIR is able to provide complete coverage to our production RPC requests in a privacy-preserving manner, despite the manifest of scale (billions of daily requests), complexity (hundreds of system-services involved) and heterogeneity (RPC protocols are highly customized). However, the live replay design principle is not a panacea. We made a couple of critical design decisions (and addressed their corresponding challenges) along the way to realize the principle in production. First, to avoid inspecting the responses of user-facing RPCs (due to privacy concerns), PAIR designs a universal and privacy-preserving mechanism, via profiling the end-to-end system invocation, to represent the RPC handling logic. Second, to ensure that PAIR provides proactive defense (rather than reactive defense that is often limited by known vulnerabilities), PAIR designs an empirical vulnerability labeling mechanism to effectively identify a group of potentially insecure RPCs while safely excluding other RPCs. During the course of three-year production development, PAIR in total helped locate 133 truly insecure RPCs, from billions of requests, while maintaining a zero false negative rate per our production observations.

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in various graph structure related tasks such as node classification and graph classification. However, GNNs are vulnerable to adversarial attacks. Existing works mainly focus on attacking GNNs for node classification; nevertheless, the attacks against GNNs for graph classification have not been well explored.

Searchable symmetric encryption (SSE) enables users to search over encrypted documents in untrusted clouds without leaking the search keywords to the clouds. Existing SSE schemes achieve high search efficiency at the expense of leaking access patterns and search patterns, where clouds can recover a large percentage of queried keywords using the leaked access patterns and search patterns. To prevent clouds from recovering users' keywords, researchers have proposed a number of solutions to protect either search patterns or access patterns. However, none of them can protect both access patterns and search patterns. Moreover, existing SSE schemes cannot work in the generic database setting that allows multiple users to write or read over encrypted documents. In this paper, we propose an efficient searchable symmetric encryption scheme, called SAP-SSE, which protects both access patterns and search patterns in the generic database setting. The main idea of protecting search patterns is to leverage re-encryption cryptosystems to shuffle index entries over multiple clouds. To protect access patterns, we distribute secure indexes to multiple clouds and then propose an index redistribution protocol that allows users to renew index entries in clouds. Furthermore, SAP-SSE provides a configurable security policy to balance security and efficiency. Formal security analysis and experimental evaluation show that SAP-SSE can prevent pattern leakage with low overhead.

Data centers, the critical infrastructure underpinning Cloud computing, often employ Software-Defined Networks (SDN) to manage cluster, wide-area and enterprise networks. As the network forwarding in SDN is dynamically programmed by controllers, it is crucial to ensure that the controller intent is correctly translated into underlying forwarding rules. Therefore, detecting and locating forwarding anomalies in SDN is a fundamental problem in production networks. Existing research proposals, roughly categorized into probing-based, packet piggybacking-based, and flow statistics analysis-based, either impose significant overhead or do not provide sufficient coverage for certain forwarding anomalies. In this article, we propose${\sf FADE}$, a controllable and passive measuring scheme to simultaneously deliver detection efficiency and accuracy.${\sf FADE}$first analyzes the entire network topology and flow rules, and then computes a minimal set of flows that can cover all forwarding rules. For each selected network flow,${\sf FADE}$decides the optimal number of monitoring positions on its path (much less than total number of hops), and installs dedicated rules to collect flow statistics.${\sf FADE}$controls the installation and expiration of these rules, along with unique flow labels, to guarantee the accuracy of collected statistics, based on which${\sf FADE}$algorithmically decides whether a forwarding anomaly is detected, and if so it further locates the anomaly. On top of${\sf FADE}$, we propose${\sf iFADE}$(a more scalable version of${\sf FADE}$) to further optimize the usage and deployment of dedicated measurement rules.${\sf iFADE}$achieves over 40 percent rule reduction compared with${\sf FADE}$. We implement a prototype of both${\sf FADE}$and${\sf iFADE}$in about 12000 lines of code and evaluate the prototype extensively. The experiment results demonstrate${\sf (i)}$${\sf FADE}$and${\sf iFADE}$are accurate, e.g., they achieve over 95 percent true positive rate and 99 percent true negative rate in anomaly detection;${\sf (ii)}$${\sf FADE}$and${\sf iFADE}$are lightweight, e.g., they reduce the overhead of control messages compared with state-of-the-art by about 50 and 90 percent, respectively.

Blockchain interoperability, which allows state transitions across different blockchain networks, is critical functionality to facilitate major blockchain adoption. Existing interoperability protocols mostly focus on atomic token exchanges between blockchains. However, as blockchains have been upgraded from passive distributed ledgers into programmable state machines (thanks to smart contracts), the scope of blockchain interoperability goes beyond just token exchanges. In this paper, we present HyperService, the first platform that delivers interoperability and programmability across heterogeneous blockchains. HyperService is powered by two innovative designs: (i) a developer-facing programming framework that allows developers to build cross-chain applications in a unified programming model; and (ii) a secure blockchain-facing cryptography protocol that provably realizes those applications on blockchains. We implement a prototype of HyperService in approximately 35,000 lines of code to demonstrate its practicality. Our experiments show that (i) HyperService imposes reasonable latency, in order of seconds, on the end-to-end execution of cross-chain applications; (ii) the HyperService platform is scalable to continuously incorporate new large-scale production blockchains.

Mobile apps have been an indispensable part in our daily life. However, there exist many potentially harmful apps that may exploit users' privacy data, e.g., collecting the user's information or sending messages in the background. Keeping these undesired apps away from the market is an ongoing challenge. While existing work provides techniques to determine what apps do, e.g., leaking information, little work has been done to answer, are the apps' behaviors compatible with the intentions reflected by the app's UI? In this work, we explore the synergistic cooperation of deep learning and program analysis as the first step to address this challenge. Specifically, we focus on the UI widgets that respond to user interactions and examine whether the intentions reflected by their UIs justify their permission uses. We present DeepIntent, a framework that uses novel deep icon-behavior learning to learn an icon-behavior model from a large number of popular apps and detect intention-behavior discrepancies. In particular, DeepIntent provides program analysis techniques to associate the intentions (i.e., icons and contextual texts) with UI widgets' program behaviors, and infer the labels (i.e., permission uses) for the UI widgets based on the program behaviors, enabling the construction of a large-scale high-quality training dataset. Based on the results of the static analysis, DeepIntent uses deep learning techniques that jointly model icons and their contextual texts to learn an icon-behavior model, and detects intention-behavior discrepancies by computing the outlier scores based on the learned model. We evaluate DeepIntent on a large-scale dataset (9,891 benign apps and 16,262 malicious apps). With 80% of the benign apps for training and the remaining for evaluation, DeepIntent detects discrepancies with AUC scores 0.8656 and 0.8839 on benign apps and malicious apps, achieving 39.9% and 26.1% relative improvements over the state-of-the-art approaches.

Internet of Things (IoT) devices achieve the rapid development and have been widely deployed recently. Meanwhile, inherent vulnerabilities of IoT systems (including firmware and software) have been continually uncovered and thus the systems are always exposed to various attacks. The root cause of the issue is that IoT systems always have design flaws and implementation bugs. In particular, the released systems (e.g., by third-party marketplaces and IoT vendors) may be maliciously repackaged with malware. Unfortunately, IoT consumers are not able to effectively capture such vulnerabilities because of the limited detection capabilities. In this paper, we propose SmartCrowd, a blockchain-based platform that aims to outsource security detection of IoT systems to distributed detectors with strong detection incentives. SmartCrowd enables built-in accountability for IoT providers and authoritative references of detection results for IoT consumers. By building smart contracts, we can incentivize the efficient and high-coverage security detection of IoT systems, while providing decentralized and automated incentives for both IoT providers releasing secure IoT systems and detectors uncovering vulnerabilities. We present the security and theoretical analysis that demonstrates the security of SmartCrowd and the incentives for participators. We prototype SmartCrowd by using Ethereum and the experimental results show that SmartCrowd has both technical feasibility and financial benefits, which can be applied to build a secure IoT ecosystem.

Defending against distributed denial of service (DDoS) attacks on the Internet is a fundamental problem. However, recent industrial interviews with over 100 security experts from more than ten industry segments indicate that DDoS problems have not been fully addressed. The reasons are twofold. On one hand, many academic proposals that are provably secure witness little real-world deployment. On the other hand, the operation model for existing DDoS-prevention service providers (e.g., Cloudflare, Akamai) is privacy invasive for large organizations (e.g., government). In this paper, we present Umbrella, a new DDoS defense mechanism enabling Internet service providers to offer readily deployable and privacy-preserving DDoS prevention services to their customers. At its core, Umbrella develops a multi-layered defense architecture to defend against a wide spectrum of DDoS attacks. In particular, the flood throttling layer stops amplification-based DDoS attacks; the congestion resolving layer, aiming to prevent sophisticated attacks that cannot be easily filtered, enforces congestion accountability to ensure that legitimate flows are guaranteed to receive their fair shares regardless of attackers' strategies; and finally the user-specific layer allows DDoS victims to enforce self-desired traffic control policies that best satisfy their business requirements. Based on Linux implementation, we demonstrate that Umbrella is capable to deal with large-scale attacks involving millions of attack flows, meanwhile imposing negligible packet processing overhead. Further, our physical test bed experiments and large-scale simulations prove that Umbrella is effective to mitigate various DDoS attacks.

With the advent of software-defined networking, network configuration through programmable interfaces becomes practical, leading to various on-demand opportunities for network routing update in multi-tenant datacenters, where tenants have diverse requirements on network routings such as short latency, low path inflation, large bandwidth, high reliability, etc. Conventional solutions that rely on topology search coupled with an objective function to find desired routings have at least two shortcomings: ${\sf (i)}$(i) they run into scalability issues when handling consistent and frequent routing updates and ${\sf (ii)}$(ii) they restrict the flexibility and capability to satisfy various routing requirements. To address these issues, this paper proposes a novel search and optimization decoupled design, which not only saves considerable topology search costs via search result reuse, but also avoids possible sub-optimality in greedy routing search algorithms by making decisions based on the global view of all possible routings. We implement a prototype of our proposed system, OpReduce, and perform extensive evaluations to validate its design goals.

Today's cloud networks are shared among many tenants. Bandwidth guarantees and work conservation are two key properties to ensure predictable performance for tenant applications and high network utilization for providers. Despite significant efforts, very little prior work can really achieve both properties simultaneously even some of them claimed so. In this paper, we present QShare, a comprehensive in-network solution to achieve bandwidth guarantees and work conservation simultaneously. QShare leverages weighted fair queuing on commodity switches to slice network bandwidth for tenants, and solves the challenge of queue scarcity through balanced tenant placement and dynamic tenant-queue binding. We have implemented a QShare prototype and evaluated it extensively via both testbed experiments and simulations. Our results show that QShare ensures bandwidth guarantees while driving network utilization to over 91% even under unpredictable traffic demands.

The Internet lacks verification of source authenticity and path compliance between the planned packet delivery paths and the real delivery paths, which allows attackers to construct attacks like source spoofing and traffic hijacking attacks. Thus, it is essential to enable source and path verification in networks to detect forwarding anomalies and ensure correct packet delivery. However, most of the existing security mechanisms can only capture anomalies but are unable to locate the detected anomalies. Besides, they incur significant computation and communication overhead, which exacerbates the packet delivery performance. In this paper, we propose a high-efficient packet forwarding verification mechanism called PPV for networks, which verifies packet source and their forwarding paths in real time. PPV enables probabilistic packet marking in routers instead of verifying all packets. Thus, it can efficiently identify forwarding anomalies by verifying markings. Moreover, it localizes packet forwarding anomalies, e.g., malicious routers, by reconstructing packet forwarding paths based on the packet markings. We implement PPV prototype in Click routers and commodity servers, and conducts real experiments in a real testbed built upon the prototype. The experimental results demonstrate the efficiency and performance of PPV. In particular, PPV significantly improves the throughput and the goodput of forwarding verification, and achieves around 2 times and 3 times improvement compared with the-state-of-art OPT scheme, respectively.

Internet of Things (IoT) has already been in the period of rapid development and widespread deployment, while it is still vulnerable to various malicious attacks. Security detection before system installation is not enough to ensure that IoT devices are always secure, because newly emerging vulnerabilities can still be exploited to launch attacks. To address this issue, retrospective detection is often required to trace the security status of IoT systems. Unfortunately, existing centralized detection mechanisms cannot easily provide a comprehensive security analysis. In particular, consumers cannot automatically receive security notification whenever a new vulnerability is uncovered. In this paper, we propose a novel blockchain-powered incentive platform, called SmartRetro, that can incentivize and attract more distributed detectors to participate in retrospective vulnerability detection and contribute their detection results. Leveraging smart contracts, consumers in SmartRetro receive automatic security feedback about their installed IoT systems. We perform the security and theoretical analysis to demonstrate that SmartRetro achieves our desirable security goals.We further implement SmartRetro prototype on Ethereum to evaluate its performance. Our experimental results show SmartRetro is technically feasible and economically beneficial.

Tor is the most widely used anonymity network, currently serving millions of users each day. However, there is no access control in place for all these users, leaving the network vulnerable to botnet abuse and attacks. For example, criminals frequently use exit relays as stepping stones for attacks, causing service providers to serve CAPTCHAs to exit relay IP addresses or blacklisting them altogether, which leads to severe usability issues for legitimate Tor users. To address this problem, we propose TorPolice, the first privacy-preserving access control framework for Tor. TorPolice enables abuse-plagued service providers such as Yelp to enforce access rules to police and throttle malicious requests coming from Tor while still providing service to legitimate Tor users. Further, TorPolice equips Tor with global access control for relays, enhancing Tor's resilience to botnet abuse. We show that TorPolice preserves the privacy of Tor users, implement a prototype of TorPolice, and perform extensive evaluations to validate our design goals.

Volumetric attacks, which overwhelm the bandwidth of a destination, are amongst the most common DDoS attacks today. One practical approach to addressing these attacks is to redirect all destination traffic (e.g., via DNS or BGP) to a third-party, DDoS-protection-as-a-service provider (e.g., CloudFlare) that is well provisioned and equipped with filtering mechanisms to remove attack traffic before passing the remaining benign traffic to the destination. An alternative approach is based on the concept of network capabilities, whereby source sending rates are determined by receiver consent, in the form of capabilities enforced by the network. While both third-party scrubbing services and network capabilities can be effective at reducing unwanted traffic at an overwhelmed destination, DDoS-protection-as-a-service solutions outsource all of the scheduling decisions (e.g., fairness, priority and attack identification) to the provider, while capability-based solutions require extensive modifications to existing infrastructure to operate. In this paper we introduce MiddlePolice, which seeks to marry the deployability of DDoS-protection-as-a-service solutions with the destination-based control of network capability systems. We show that by allowing feedback from the destination to the provider, MiddlePolice can effectively enforce destination-chosen policies, while requiring no deployment from unrelated parties.

In this paper, we focus on the capacity and delay tradeoff for multicast traffic pattern in cognitive radio mobile ad hoc networks (MANETs). In our system model, the primary network consisting of n primary nodes overlaps with the secondary network consisting of m secondary nodes in a unit square. Assume that all nodes move according to an independent and identically distributed mobility model, and each primary node serves as a source that multicasts its packets to kp primary destination nodes, whereas each secondary source node multicasts its packets to ks secondary destination nodes. Under the cell partitioned network model, we study the capacity and delay for the primary networks under two communication schemes, i.e., noncooperative scheme and cooperative scheme. The communication pattern considered for the secondary network is cooperative scheme. Given that m = nβ(β > 1), we show that per-node capacities O(1/kp) and O(1/ks) are achievable for the primary network and the secondary network, with average delays Θ(n log kp) and Θ(m log ks), respectively. Moreover, to reduce the average delay in the secondary network, we employ a redundancy scheme and prove that a per-node capacity O(1/ks√m log ks) is achievable with average delay Θ(√m log ks). We find that the fundamental delay-capacity tradeoff in the secondary network is delay/capacity ≥ O(mkslog ks) under both cooperative and redundancy schemes.

The fc-connectivity of cognitive radio networks is discussed in this paper. By combining asymptotic k-connectivity and percolation, we define that the cognitive radio networks are k-connected if there exists an infinite k-connected component in the secondary network. Utilizing continuum percolation theory, we prove that the secondary network is k-disconnected if secondary users' density λ2, where l → ∞ is the side length of a square-shaped network, r is the transmission range of the secondary network, and θ = Ω(1). And the network is k-connected when λs≥ log l + (k − 1 − + ξ) log, log l/πr2, where ξ = Ω(1). Finally, we provide the necessary condition and sufficient condition of k-connectivity on primary user density λp.

There has been recent interest within the networking research area to understand the transmission delay in Cognitive Radio (CR) Networks with overlapping primary network and secondary network. In this paper, we investigate the scaling behavior of transmission delay in large scale ad hoc CR networks. We take different scenarios of CR networks into consideration and thus obtain a wind range of results. We first neglect propagation delay and study the ratio of transmission delay to distance, denoted by γ (λsAp and figure out its exact value in supercritical secondary network. In case of subcritical secondary network, we introduce a multi-cluster hop transmission process to get the lower bound of γ (λsAp. Then we take propagation delay into consideration to obtain further results. Finally, we use simulation results to verify our theoretical analysis. The results present the scaling behavior of transmission delay in CR networks and provide the design guidelines for large scale wireless networks.