BGP, the Internet’s inter-domain routing protocol, is vulnerable to prefix hijacks due to tamperable prefix–origin bindings. RPKI addresses this by cryptographically binding prefixes to authorized ASes, enabling Route Origin Validation (ROV). Given RPKI’s critical role in Internet security, identifying the proportion of ASes that deploy ROV is a key research question. However, measuring ROV deployment is difficult due to limited visibility into private AS configurations. Existing methods suffer from low accuracy, restricted coverage, and the inability to detect hidden ASes whose behavior is masked by upstream ROV deployment. To address these limitations, we propose RIFT, a novel inference method based on temporal fingerprinting. RIFT leverages the insight that periodic ROA retrieval creates distinctive temporal patterns in the routing behavior of ROV-deployed ASes. Experiments show that RIFT achieves 94% accuracy and an F1 score of 0.88 in identifying ROV deployment.

RDMA is promising for enhancing the performance of cross-datacenter (DC) services. However, deploying RDMA over wide-area networks introduces severe congestion control unfairness, primarily due to asymmetric congestion feedback delays between inter-DC flows and intra-DC flows. As a result, intra-DC flows often bear the full burden of congestion response, leading to drastically increased flow completion times (FCT). In this work, we identify two key forms of unfairness — near-source and near-destination — depending on whether congestion occurs near the sender or receiver of inter-DC flows. Based on this, we propose THEMIS, a fairness maintenance patch for long-haul RDMA networks. To mitigate near-source unfairness, THEMIS devises a Proactive Notification Point to shorten the congestion feedback loop within a single DC. To alleviate near-destination unfairness, THEMIS introduces a Temporary Reaction Point to temporarily slow down the target inter-DC flow until the sender receives the corresponding congestion feedback. We implement an open-source prototype of THEMIS, and evaluate it on both real-world testbed and large-scale simulations. Compared to DCQCN, Annulus and BiCC, THEMIS reduces the intra-DC FCT by up to 79.2%, 63.6% and 55.6%, and decreases overall FCT by up to 61.2%, 31.9% and 59.5% respectively.

Superimposed pilot (SIP) schemes face significant challenges in effectively superimposing and separating pilot and data signals, especially in multiuser mobility scenarios with rapidly varying channels. To address these challenges, we propose a novel channel-aware learning framework for SIP schemes, termed CaSIP, that jointly optimizes pilot-data power (PDP) allocation and a receiver network for pilot-data interference (PDI) elimination, by leveraging channel path gain information, a form of large-scale channel state information (CSI). The proposed framework identifies user-specific, resource elementwise PDP factors and develops a deep neural network-based SIP receiver comprising explicit channel estimation and data detection components. To properly leverage path gain data, we devise an embedding generator that projects it into embeddings, which are then fused with intermediate feature maps of the channel estimation network. Simulation results demonstrate that CaSIP efficiently outperforms traditional pilot schemes and state-of-the-art SIP schemes in terms of sum throughput and channel estimation accuracy, particularly under high-mobility and low signal-to-noise ratio (SNR) conditions.

Hybrid precoding technology is an economically efficient solution for modern millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) communications due to its low hardware complexity and high performance. However, the design of hybrid precoding heavily relies on accurate channel state information (CSI), which requires a large number of pilot signals, thereby degrading system performance. Additionally, the computational complexity of hybrid precoding based on optimization or greedy algorithms increases exponentially with the number of antennas and users. This paper proposes a neural network (NN)-based multiuser hybrid precoding (MHP) framework that leverages the spatial consistency between sub-6G and mmWave channels to reduce pilot overhead while enabling efficient hybrid precoding design. The proposed frame-work consists of two key components: HNet and PNet, each designed to achieve specific objectives. First, HNet employs a convolutional NN (CNN)-based U-Net architecture to extract sub-6G CSI and reconstruct high-frequency mmWave CSI. Then, PNet, specifically designed based on the computational process of hybrid precoding, learns to compute the hybrid precoder from the reconstructed CSI. Simulation results demonstrate that the proposed framework effectively performs channel reconstruction and precoding design, achieving near-optimal hybrid precoding performance while reducing pilot overhead by 87.5%.

Fuzzing is a prevalent technology for identifying software vulnerabilities. Existing fuzzing techniques predominantly focus on maximizing code coverage to unearth potential security issues. However, the mere expansion of explored code does not necessarily correlate with an increased discovery of vulnerabilities. Additionally, existing fuzzers often neglect comprehensive execution path information in code exploration. Consequently, potential vulnerabilities may be delayed or overlooked in the fuzzing process. To address this, we propose VPGFUZZ, a vulnerable path-guided fuzzer that can not only explore new code but also exploit known vulnerability path knowledge for vulnerability discovery. It employs a vulnerable path recognition model to identify test cases with potentially vulnerable paths. This model is trained with various execution paths derived from real-world vulnerability PoCs (Proof of Concepts). Based on this model, VPGFUZZ applies an explore-exploit seed selection strategy to effectively choose test cases for testing. Unlike traditional seed selection methods that maintain a single queue for exploring new code, this strategy includes a separate queue for retaining test cases identified as potentially vulnerable, allowing for more thorough testing. Experimental results demonstrate that VPGFUZZ discovers 24 zero-day vulnerabilities, with 18 receiving vulnerability identifiers from third-party organizations such as CVE. Our evaluation also shows VPGFUZZ’s superior efficiency by uncovering the first vulnerability approximately 1.2 to 70 times faster than popular fuzzers in most programs.

Border Gateway Protocol (BGP) plays a pivotal role as the de facto inter-domain routing protocol on the Internet. However, BGP threats continually emerge and undermine the Internet reliability. Existing BGP threat detection methods based on machine learning require substantial labeled data and expert involvement, making them costly and labor-intensive. Moreover, they fail to learn rich information from massive unlabeled BGP data consistently generated on the Internet. In this paper, we propose FIRE that enables few-shot inter-domain routing threat detection with large-scale multi-modal pre-training. FIRE conducts domain-specific pre-training tasks to acquire rich BGP implicit knowledge from massive unlabeled BGP data for few-shot learning. Our experiments show that FIRE can be fine-tuned to precisely identify BGP threats with only a few labeled samples, e.g., a 93.2% precision in route leak detection with merely 8 events for fine-tuning.

RDMA is a kernel-bypass and transport-offload technology that provides high throughput and low delay for datacenter networks, and DCQCN is the default and most widely used congestion control algorithm in large-scale RDMA networks. DCQCN involves over 10 parameters at RNICs and switches, and their settings significantly affect network performance, currently relying heavily on exhaustive manual tuning. Although some automatic methods are proposed to tune a subset of DCQCN parameters, none of them comprehensively address all parameters at both RNICs and switches, resulting in compromised network performance. In this paper, we propose Paraleon, an automatic and adaptive system to tune DCQCN parameters comprehensively. We design a millisecond-level sketch-based monitoring mechanism for accurate network-wide measurement, which collects runtime metrics as feedback to guide the tuning process. We also analyze the complicated parameter impacts on network performance, and leverage an improved heuristic searching algorithm for timely performance optimization with better efficiency and convergence. We implement Paraleon and conduct extensive experiments in both NS3 simulations and a real-world testbed. The results show that Paraleon achieves$3.8 \% \sim 61.4 \%$higher performance than existing tuning schemes.

Network protocol fingerprinting is a critical technique for identifying various implementations of network protocols, which is essential for vulnerability assessment and security management. However, current fingerprinting methods such as Nmap still heavily rely on manual probe crafting, requiring experts with domain knowledge and leading to inefficiencies and potential oversights. This paper introduces pFuzz, an automatic network protocol fingerprint discovery system utilizing difference-guided fuzzing, to address the challenge of the vast search space inherent in fingerprinting. We propose a difference tree to model the nested recursive condition structure of network protocols and a packet oracle map to capture and utilize multifield relationships revealed by value co-occurrence. Our evaluation of pFuzz on the widely used TCP/IP protocol demonstrates its effectiveness and efficiency on discovering fingerprints.

Channel state information (CSI) feedback is critical for achieving the promised advantages of enhancing spectral and energy efficiencies in massive multiple-input multiple-output (MIMO) wireless communication systems. Deep learning (DL)-based methods have been proven effective in reducing the required signaling overhead for CSI feedback. In practical dual-polarized MIMO scenarios, channels in the vertical and horizontal polarization directions tend to exhibit high polarization correlation. To fully exploit the inherent propagation similarity within dual-polarized channels, we propose a disentangled representation neural network (NN) for CSI feedback, referred to as DiReNet. The proposed DiReNet disentangles dual-polarized CSI into three components: polarization-shared information, vertical polarization-specific information, and horizontal polarization-specific information. This disentanglement of dual-polarized CSI enables the minimization of information redundancy caused by the polarization correlation and improves the performance of CSI compression and recovery. Additionally, flexible quantization and network extension schemes are designed. Consequently, our method provides a pragmatic solution for CSI feedback to harness the physical MIMO polarization as a priori information. Our experimental results show that the performance of our proposed DiReNet surpasses that of existing DL-based networks, while also effectively reducing the number of network parameters by nearly one third.

Deploying Machine Learning as a Service gives rise to model plagiarism, leading to copyright infringement. Ownership testing techniques are designed to identify model fingerprints for verifying plagiarism. However, previous works often rely on overfitting or robustness features as fingerprints, lacking theoretical guarantees and exhibiting under-performance on generalized models. In this paper, we propose a novel ownership testing method called VeriDIP, whichverifies aDNN model'sintellectualproperty. VeriDIP makes two major contributions. (1) It utilizes membership inference attacks to estimate the lower bound of privacy leakage, which reflects the fingerprint of a given model. The privacy leakage fingerprints highlight the unique patterns through which the models memorize sensitive training datasets. (2) We introduce a novel approach using less private samples to enhance the performance of ownership testing. Extensive experimental results confirm that VeriDIP is effective and efficient in validating the ownership of deep learning models trained on both image and tabular datasets. VeriDIP achieves comparable performance to state-of-the-art methods on image datasets while significantly reducing computation and communication costs. Enhanced VeriDIP demonstrates superior verification performance on generalized deep learning models, particularly on table-trained models. Additionally, VeriDIP exhibits similar effectiveness on utility-preserving differentially private models compared to non-differentially private baselines.

As the mainstream encrypted protocols adopt TCP protocol to ensure lossless data transmissions, the privacy of encrypted TCP traffic becomes a significant focus for adversaries. They can leverage Deep Learning (DL) models to infer the sensitive information from encrypted TCP traffic by analyzing its packet size, direction, and timing information. To defend against such DL-based traffic analysis attacks, recent advances reshape the encrypted traffic and achieve desired results. However, they typically require deploying cooperative modules on both communication endpoints and only support specific applications, such as browsers. In this paper, we propose Cactus, a client-side plug-in to obfuscate bidirectional encrypted TCP traffic for a wide range of applications transparently using the inherent TCP semantics and the emerging eBPF technique. In particular, Cactus provides four effective operations to enable bidirectional traffic obfuscation while preserving communication semantics of applications. Besides, Cactus empowers users to specify which applications to conduct traffic obfuscation and what obfuscation level for each application. We conduct comprehensive experiments to demonstrate that Cactus can effectively obfuscate encrypted TCP traffic with low overhead to hinder the traffic analysis efforts in website fingerprinting and application identification.

Border Gateway Protocol (BGP) is the only inter-domain routing protocol that plays an important role on the Internet. However, BGP suffers from route leaks, which can cause serious security threats. To mitigate the effects of route leaks, accurate and timely route leak locating is of great importance. Prior studies leverage AS business relationships to locate route leaks in real time. However, they fail to achieve high locating accuracy. Recent studies apply machine learning to accurately detect route leaks from statistical features of massive BGP messages. Nevertheless, they have high detection latency and cannot further locate route leaks. In this paper, we propose a real-time and accurate route leak locating system named RoLL+. It leverages distinctive AS triplet features to accurately locate AS triplets with route leaks from each BGP message in real time. Considering that RoLL+ may receive a substantial volume of BGP update messages per second, we integrate a cache-like design and a lazy update mechanism into the system to effectively identify route leaks at scale. Our experimental results on real-world BGP route leak data demonstrate that it can achieve 92% locating accuracy with less than 1 ms locating latency. Furthermore, the results show that RoLL+ can process over 7,000 AS triplets per second, meeting real-world throughput requirements.

BGP is the only inter-domain routing protocol that plays an important role on the Internet. However, BGP suffers from route leak, which can cause serious security threats. To mitigate the effects of route leak, accurate and timely route leak location is of great importance. Prior studies leverage AS business relationships to locate route leak in real time. However, they fail to achieve high location accuracy. Recent studies apply machine learning to accurately detect route leak from statistical features of massive BGP messages. Nevertheless, they have high detection latency and cannot further locate route leak. In this paper, we propose a real-time and accurate route leak location system named RoLL. It leverages distinctive AS triplet features to accurately locate AS triplets with route leak from each BGP update message in real time. Our experimental results on real-world BGP route leak data demonstrate that RoLL can achieve 91% location accuracy with less than 10 ms location latency.

Deep learning (DL) applied to a device’s radio-frequency fingerprint (RFF) has attracted significant attention in physical-layer authentication due to its extraordinary classification performance. Conventional DL-RFF techniques are trained by adopting maximum likelihood estimation (MLE). Although their discriminability has recently been extended to unknown devices in open-set scenarios, they still tend to overfit the channel statistics embedded in the training dataset. This restricts their practical applications as it is challenging to collect sufficient training data capturing the characteristics of all possible wireless channel environments. To address this challenge, we propose a DL framework of disentangled representation (DR) learning that first learns to factor the signals into a device-relevant component and a device-irrelevant component via adversarial learning. Then, it shuffles these two parts within a dataset for implicit data augmentation, which imposes a strong regularization on RFF extractor learning to avoid the possible overfitting of device-irrelevant channel statistics, without collecting additional data from unknown channels. Experiments validate that the proposed approach, referred to as DR-based RFF, outperforms conventional methods in terms of generalizability to unknown devices under unknown complicated propagation environments, e.g., dispersive multipath fading channels, even though all the training data are collected in a simple environment with dominated direct line-of-sight (LoS) propagation paths.

Software-Defined Networking (SDN). SDN enables network innovations with a centralized controller controlling the whole network through the control channel. Because the control channel delivers all network control traffic, its security and reliability are of great importance. For the first time in the literature, we propose the CrossPath attack that disrupts the SDN control channel by exploiting the shared links in paths of control traffic and data traffic. In this attack, crafted data traffic can implicitly disrupt the forwarding of control traffic in the shared links. As the data traffic does not enter the control channel, the attack is stealthy and cannot be easily perceived by the controller. In order to identify the target paths containing the shared links to attack, we develop a novel technique called adversarial path reconnaissance. Our experimental results show its feasibility and efficiency of identifying the target path. We systematically study the impacts of the attack on various network applications in a real SDN testbed. Experiments show the attack significantly degrades the performance of existing network applications and causes serious network anomalies, e.g., routing blackhole, flow table resetting, and even network-wide DoS. To defeat the CrossPath attack, we design a lightweight defense system named CrossGuard. Experiments demonstrate that it can effectively protect the control channel and quickly locate the attack flow with 98% accuracy while introducing a small overhead.

In order to fully exploit the advantages of massive multiple-input multiple-output (mMIMO), it is critical for the transmitter to accurately acquire the channel state information (CSI). Deep learning (DL)-based methods have been proposed for CSI compression and feedback to the transmitter. Although most existing DL-based methods consider the CSI matrix as an image, structural features of the CSI image are rarely exploited in neural network design. As such, we propose a model of self-information that dynamically measures the amount of information contained in each patch of a CSI image from the perspective of structural features. Then, by applying the self-information model, we propose a model-and-data-driven network for CSI compression and feedback, namely IdasNet. The IdasNet includes the design of a module of self-information deletion and selection (IDAS), an encoder of informative feature compression (IFC), and a decoder of informative feature recovery (IFR). In particular, the model-driven module of IDAS pre-compresses the CSI image by removing informative redundancy in terms of the self-information. The encoder of IFC then conducts feature compression to the pre-compressed CSI image and generates a feature codeword which contains two components, i.e., codeword values and position indices of the codeword values. Subsequently, the IFR decoder decouples the codeword values as well as position indices to recover the CSI image. Experimental results verify that the proposed IdasNet noticeably outperforms existing DL-based networks under various compression ratios while it has the number of network parameters reduced by orders-of-magnitude compared with various existing methods.

Generative Adversarial Networks (GAN)-synthesized table publishing lets people privately learn insights without access to the private table. However, existing studies on Membership Inference (MI) Attacks show promising results on disclosing membership of training datasets of GAN-synthesized tables. Different from those works focusing on discovering membership of a given data point, in this paper, we propose a novel Membership Collision Attack against GANs (TableGAN-MCA), which allows an adversary given only synthetic entries randomly sampled from a black-box generator to recover partial GAN training data. Namely, a GAN-synthesized table immune to state-of-the-art MI attacks is vulnerable to the TableGAN-MCA. The success of TableGAN-MCA is boosted by an observation that GAN-synthesized tables potentially collide with the training data of the generator.

Radio-frequency fingerprints (RFFs) are promising solutions for realizing low-cost physical layer authentication. Machine learning-based methods have been proposed for RFF extraction and discrimination. However, most existing methods are designed for the closed-set scenario where the set of devices is remains unchanged. These methods can not be generalized to the RFF discrimination of unknown devices. To enable the discrimination of RFF from both known and unknown devices, we propose a new end-to-end deep learning framework for extracting RFFs from raw received signals. The proposed framework comprises a novel preprocessing module, called neural synchronization (NS), which incorporates the data-driven learning with signal processing priors as an inductive bias from communication-model based processing. Compared to traditional carrier synchronization techniques, which are static, this module estimates offsets by two learnable deep neural networks jointly trained by the RFF extractor. Additionally, a hypersphere representation is proposed to further improve the discrimination of RFF. Theoretical analysis shows that such a data-and-model framework can better optimize the mutual information between device identity and the RFF, which naturally leads to better performance. Experimental results verify that the proposed RFF significantly outperforms purely data-driven DNN-design and existing handcrafted RFF methods in terms of both discrimination and network generalizability.

It is critical for urban planners and real estate developers to understand how the built environment and house characteristics are valued in housing market. However, this problem is challenging because of the existence of the submarket effect resulted from the heterogeneity nature of city. In this paper, we propose a probabilistic approach to residential property hedonic valuation problem modeling the full scope of submarket effect based on built environment and house characteristics. Specifically, we introduce a latent variable representing housing submarket and model both of the submarket criteria and hedonic price model(HPM) into a Bayesian network. Utilizing the probabilistic dependencies in the Bayesian network, our model is able to capture the full scope of the submarket effect. Furthermore, to analyze the relationship among the discovered submarkets, we propose a probabilistic hierarchical clustering method to infer the hierarchical structure of housing market. In particular, we perform Bayesian hypothesis testings to find the most similar submarkets and agglomerate submarkets step-by-step, thus revealing the hierarchical structure of housing market. Finally, we conduct comprehensive experiments in the housing market of Nanjing which is a metropolis in eastern China. The experimental results demonstrate the effectiveness of our proposed modeling method.

Deep neural networks have been proven to be vulnerable to the so-called adversarial attacks. Recently there have been efforts to defend such attacks with deep generative models. These defenses often predict by inverting the deep generative models rather than simple feedforward propagation. Such defenses are difficult to attack due to the obfuscated gradients caused by inversion. In this work, we propose a new white-box attack to break these defenses. The idea is to view the inversion phase as a dynamical system, through which we extract the gradient w.r.t the image by backtracking its trajectory. An amortized strategy is also developed to accelerate the attack. Experiments show that our attack better breaks state-of-the-art defenses (e.g DefenseGAN, ABS) than other attacks (e.g BPDA). Additionally, our empirical results provide insights for understanding the weaknesses of deep generative model defenses.

The low-rate TCP attack is essentially a great threat to the Internet. It causes significant throughput degradation of TCP flows by generating periodical pulsing flows. Due to its low rate, the attack is difficult to be detected and throttled. Recently, Software-Defined Networking (SDN) has emerged as a promising network paradigm. Several SDN-based defense systems have been proposed to deal with various Denial of Service (DoS) attacks. However, they fail to consider the low-rate TCP attack. In this paper, we propose SoftGuard, which is an SDN-based defense that effectively detects and mitigates the low-rate TCP attack. SoftGuard detects the attack by installing crafted flow rules to monitor the degradation of aggregated TCP throughput in ports of switches. It confirms the attack by judging whether there is periodicity for aggregated TCP throughput with adaptive Fast Fourier Transform, and accurately identifies attack flows with Mean Euclidean Distance. Identified attack flows will be effectively throttled by installing mitigation rules in ingress switches. We implement SoftGuard in the Floodlight controller. Experiments in a real SDN testbed demonstrate its effectiveness on defending against the low-rate TCP attack.