Wildcard Keyword Searchable Encryption (WKSE) has grown into a ubiquitous tool. It enables clients to search desired files with wildcard expressions. Although promising, previous schemes confront three barriers: (1) An adversary can launch a correlation attack to acquire the similarity between keywords. (2) The WKSE schemes exhibit false positives which can lead to wrong search results. (3) Existing feature extraction strategies limit the flexibility of search expressions. In this paper, we propose a Multi-Character Searchable Encryption scheme (MCSE) that overcomes the aforementioned barriers. To resist correlation attacks, we design the randomize pad model to encrypt the vector. To eradicate false positives, we apply the vector space model and complete feature extraction strategies so that a feature set uniquely identifies a keyword or expression. To enhance search flexibility, we introduce three distinct feature extraction strategies for keyword expressions, wildcard expressions, and logical expressions, enabling effective multi-character search. These strategies enable indexes to accom modate the search of diverse expressions. Finally, we prove that MCSE is indistinguishable against chosen-feature attacks and implement MCSE on two real datasets. Compared with state-of the-art schemes, the experiment results show that MCSE achieves good performance.

Estimating the frequency of items on the high-volume, fast data stream has been extensively studied in many areas, such as database and network measurement. Traditional sketches provide only coarse estimates under strict memory constraints. Although some learning-augmented methods have emerged recently, they typically rely on offline training with real frequencies or/and labels, which are often unavailable. Moreover, these methods suffer from slow update speeds, limiting their suitability for real-time processing despite offering only marginal accuracy improvements. To overcome these challenges, we propose UCL-sketch, a practical learning-based paradigm for per-key frequency estimation. Our design introduces two key innovations: (i) an online training mechanism based on equivalent learning that requires no ground truth (GT), and (ii) a highly scalable architecture leveraging logically structured estimation buckets to scale to real-world data stream. The UCL-sketch, which utilizes compressive sensing (CS), converges to an estimator that provably yields an error bound far lower than that of prior works, without sacrificing the speed of processing. Extensive experiments on both real-world and synthetic datasets demonstrate that our approach outperforms previously proposed approaches regarding per-key accuracy and distribution. Notably, under extremely tight memory budgets, its quality almost matches that of an (infeasible) omniscient oracle. Moreover, compared to the existing equation-based sketch, UCL-sketch achieves an average decoding speedup of nearly 500 times.

Given the high cost associated with directly measuring the Traffic Matrix (TM), researchers have devoted efforts to devising methods for estimating the complete TM from low-cost link loads by solving a set of heavily ill-posed linear equations. Today’s increasingly intricate networks present an even greater challenge: as adaptive and dynamically changing routing strategies are gradually replacing traditional fixed routing schemes, the routing matrix within these equations can no longer be deemed reliable. In our previous work, we pioneered a flow-based generative model, FlowTM, which estimated the TM by establishing an invertible correlation between the TM and link loads without relying on the routing matrix. We demonstrated that the missing information in the ill-posed equations can be decoupled from the TM and learned jointly with the invertible mapping. Considering that acquiring a complete training set for FlowTM is often impractical in many real-world networks, we further propose an enhanced model, FlowTM+, in this extended work. It incorporates anInspectormodule to mine deeper latent structures from the partially observed TM data and link load measurements. This new technique effectively compensates for unobservable information in the training data. Extensive experiments demonstrate that FlowTM improves the performance of the best baseline by 38%–58% when the actual routing matrix is absent. Remarkably, with only 2% of the training data, FlowTM+ achieves an estimation accuracy comparable to that of state-of-the-art baselines trained with full routing knowledge and complete training data.

Aiming at the problem of task offloading in multiaccess edge computing (MEC) scenarios, this paper proposes a meta-reinforcement learning (Meta-RL)-based computational task offloading method. The algorithm adopts a two-layer architecture: the inner layer models the task offloading process as a Markov Decision Process (MDP), designs a reward function based on task latency and energy consumption, and designs a task offloading algorithm based on Proximal Policy Optimization (PPO) to make offloading decisions for each task in a single scenario and optimize the offloading performance within the scenario. The outer layer introduces the Meta-RL mechanism to optimize the initial parameters of the inner-layer neural network and learns multiple MDPs based on gradient descent to generate neural network parameters that can be applied to the intelligence of each scenario, so that the proposed algorithm can adapt to the offloading scenarios quickly. The proposed algorithm can quickly adapt to each offloading scenario. Simulation results show that the proposed algorithm improves the average cost by 14.7 % and 20.51 % compared with PPO and DDPG.

Diffusion models have exhibited state-of-the-art performance in generative tasks across various domains. A few recent works leveraged the powerful modeling ability of the diffusion model to time-series forecasting, leading to a significant breakthrough. However, all these works perform the forecasting through incorporating the historical time-series conditions into the backward denoising. This causes the diffusion model to lose the essential consistency between forward and backward processes, thereby limiting the precision of the inference. In this paper, we propose a novel Dual Trajectory Revised Diffusion Model (TimeDTR) for time-series forecasting, which leverages an unconventional conditioning strategy to incorporate the historical information into both forward and backward trajectories in the diffusion model. Experimental results on six real-world datasets demonstrate that TimeDTR takes a big step forward from the state-of-the-art in time-series forecasting, especially in the long-term forecasting tasks, in terms of forecasting accuracy. The codes of the experiments with datasets and our algorithms are available at https://github.com/hhzzlll/TimeDTR.

Large Language Models (LLMs) have demonstrated remarkable zero-shot capabilities across various domains. This paper pioneers the application of LLMs’ outstanding knowledge and reasoning abilities to the challenging task of Traffic Matrix (TM) imputation. However, the application poses significant challenges due to the skewed TM distribution and the deficient traffic feature under low sampling rate. To address these issues, we propose TM-LLM, the first LLM-based model specifically designed for TM imputation. Our approach includes two critical designs: Firstly, we develop an adversarial training strategy to pre-impute TM data, allowing the LLM to understand the distributional features even when faced with extensive missing data. Secondly, we devise a TM-specific embedding scheme along with a crafted prompt template, which enables our approach to harness LLMs’ exceptional inferential ability. Experimental results show that TMLLM significantly outperforms state-of-the-art imputation methods, achieves a notable 16.5% -44.8 % improvement in accuracy over the current best baseline, while reduces measurement costs by 80 % - 96 %. It can accurately capture the traffic pattern even when the sampling rate is extremely low. The code for reproducing our experiments is publicly available1. These findings strongly indicate the breakthrough potential of LLMs in network TM analysis tasks.1The experimental codes with our methods and the datasets are available at https://github.com/FILingK/TM-LLM

Blockchain technology autonomously executes smart contracts that require external data to facilitate specific applications, underscoring the necessity for Authenticated Data Feeds (ADF). Existing solutions fall short in providing genuine authentication of data, lack private and verifiable computations across multiple data sources, and overlook data traceability, rendering current systems inadequate for complex applications. We present WuKong (WK), a data governance system that offers authenticated, privately verifiable, and traceable data feeds. WK enables a server to collect faithful data through an oracle committee and to prove computation correctness in zero-knowledge proofs, and empowers legal entities to trace a leakage source conditionally. We formally define and prove the security of WK in the universal composability framework. We implement three applications that seamlessly integrate with WK. Experimental results indicate that WK effectively liberates sensitive data from distributed, untrusted, and anonymous providers, making it accessible to various services and establishing trust in a decentralized world.

Due to network operation and maintenance relying heavily on network traffic monitoring, traffic matrix analysis has been one of the most crucial issues for network management related tasks. However, it is challenging to reliably obtain the precise measurement in computer networks because of the high measurement cost, and the unavoidable transmission loss. Although some methods proposed in recent years allowed estimating network traffic from partial flow-level or link-level measurements, they often perform poorly for traffic matrix estimation nowadays. Despite strong assumptions like low-rank structure and the prior distribution, existing techniques are usually task-specific and tend to be significantly worse as modern network communication is extremely complicated and dynamic. To address the dilemma, this paper proposed a diffusion-based traffic matrix analysis framework named Diffusion-TM, which leverages problem-agnostic diffusion to notably elevate the estimation performance in both traffic distribution and accuracy. The novel framework not only takes advantage of the powerful generative ability of diffusion models to produce realistic network traffic, but also leverages the denoising process to unbiasedly estimate all end-to-end traffic in a plug-and-play manner under theoretical guarantee. Moreover, taking into account that compiling an intact traffic dataset is usually infeasible, we also propose a two-stage training scheme to make our framework be insensitive to missing values in the dataset. With extensive experiments with real-world datasets, we illustrate the effectiveness of Diffusion-TM on several tasks. Moreover, the results also demonstrate that our method can obtain promising results even with 5% known values left in the datasets.

Given the high cost associated with directly measuring the traffic matrix (TM), researchers have dedicated decades to devising methods for estimating the complete TM from low-cost link loads by solving a set of heavily ill-posed linear equations. Today’s increasingly intricate networks present an even greater challenge: the routing matrix within these equations can no longer be deemed reliable. To address this challenge, we, for the first time, employ a flow-based generative model for TM estimation by establishing an invertible correlation between TM and link loads, oblivious of the routing matrix. We demonstrate that the lost information within the ill-posed equations can be independently segregated from the TM. Our model collaboratively learns the invertible correlations between TM and link loads as well as the distribution of the lost information. As a result, our model can unbiasedly reverse-transform the link loads to the true TM. Our model has undergone extensive experiments on two real-world datasets. Surprisingly, even without knowledge of the routing matrix, it significantly outperforms six representative baselines in deterministic and noisy routing scenarios regarding estimation accuracy and distribution similarity. Particularly, if the actual routing matrix is absent, our model can improve the performance of the best baseline by 41% ∼ 58%.

The Lightning Network (LN) is a second layer system for solving the scalability problem of Bitcoin transactions. In the current implementation of LN, channel capacity (i.e., the sum of individual balances held in the channel) is public information, while individual balances are kept secret for privacy concerns. Attackers may discover a particular balance of a channel by sending multiple fake payments through the channel. Such an attack, however, can hardly threaten the security of the LN system due to its high cost and noticeable intrusions. In this work, we present a novel non-intrusive balance tomography attack, which infers channel balances silently by performing legal transactions between two pre-created LN nodes. To minimize the cost of the attack, we propose an algorithm to compute the optimal payment amount for each transaction and design a path construction method using reinforcement learning to explore the most informative path to conduct the transactions. Finally, we propose two approaches (NIBT-RL and NIBT-RL-β) to accurately and efficiently infer all individual balances using the results of these transactions. Experiments using simulated account balances over actual LN topology show that our method can accurately infer 90% ∼ 94% of all balances in LN with around 12 USD.

As part of airport behavior decisions, the accurate prediction of airport delay is highly significant in optimizing flight takeoff and landing sequences. However, the combination of various influencing factors affects airport delay prediction strongly, which would bring severe challenges in prediction. This paper introduces the sequence-to-sequence network and proposes a multi-step regression prediction method for the airport delay (DA-BILSTM) to accurately predict the airport delay. Rather than only considering a single kind of airport delay influencing factors, we design an attention fusion network for learning the sequence and condition correlation features adaptively. Moreover, the Bayesian optimization algorithm is introduced to optimize DA-BILSTM’s hyperparameters. The method is applied individually to two datasets for predicting the airport’s delays. The experiment results show that the prediction performance of DA-BILSTM is better than many state-of-the-art methods including the autoregressive integrated moving average model (ARIMA), long short-term memory (LSTM), gated recurrent unit(GRU), CNN-BILSTM, and TS-LSTM. When using DA-BILSTM in the two datasets, the average MAE of airport delay prediction in the next 5 hours is about 10 minutes, and the average RMSE is 20 minutes.

Estimating the Traffic Matrix (TM) is a critical yet resource-intensive process in network management. With the advent of deep learning models, we now have the potential to learn the inverse mapping from link loads to origin-destination (OD) flows more efficiently and accurately. However, a significant hurdle is that all current learning-based techniques necessitate a training dataset covering a comprehensive TM for a specific duration. This requirement is often unfeasible in practical scenarios. This paper addresses this complex learning challenge, specifically when dealing with incomplete and biased TM data. Our initial approach involves parameterizing the unidentified flows, thereby transforming this problem of target-deficient learning into an empirical optimization problem that integrates tomography constraints. Following this, we introduce AutoTomo, a learning-based architecture designed to optimize both the inverse mapping and the unexplored flows during the model’s training phase. We also propose an innovative observation selection algorithm, which aids network operators in gathering the most insightful measurements with limited device resources. We evaluate AutoTomo with three public traffic datasets Abilene, GÉANT and Cernet. The results reveal that AutoTomo outperforms five state-of-the-art learning-based TM estimation techniques. With complete training data, AutoTomo enhances the accuracy of the most efficient method by 15%, while it shows an improvement between 30% to 56% with incomplete training data. Furthermore, AutoTomo exhibits rapid testing speed, making it a viable tool for real-time TM estimation.

The traffic matrix estimation (TME) problem has been widely researched for decades of years. Recent progresses in deep generative models offer new opportunities to tackle TME problems in a more advanced way. In this paper, we leverage the powerful ability of denoising diffusion probabilistic models (DDPMs) on distribution learning, and for the first time adopt DDPM to address the TME problem. To ensure a good performance of DDPM on learning the distributions of TMs, we design a preprocessing module to reduce the dimensions of TMs while keeping the data variety of each OD flow. To improve the estimation accuracy, we parameterize the noise factors in DDPM and transform the TME problem into a gradient-descent optimization problem. Finally, we compared our method with the state-of-the-art TME methods using two real-world TM datasets, the experimental results strongly demonstrate the superiority of our method on both TM synthesis and TM estimation.

This paper addresses the problem of anomaly detection for high-dimensional sensing data. The one-class support vector machine (OCSVM) is one of the most popular unsupervised methods for anomaly detection. When data are high dimensional and large scale, however, the efficiency of OCSVM-based methods in anomaly detection suffers. Although dimensionality-reduction tools, such as deep belief networks, can be applied to compress the high-dimensional data to alleviate the problem, the accuracy and timely detection are still hard to improve due to the inherent features of OCSVM. In this paper, we propose a new form of OCSVM model based on the structure of the compressed data and the characteristics of OCSVM. Based on the new model, we design both optimal and approximate methods for model training and testing. We evaluate the performance of our methods with extensive experiments on four real-world datasets. The experimental results demonstrate that our new methods, both optimal and approximate ones, not only significantly outperform the state-of-the-art in accuracy and efficiency, but also achieve the good performance without the need of manual parameter tuning. In addition, our approximate training and testing mechanism can reduce the computing time by three orders of magnitude with a negligible loss in accuracy.

The Lightning Network (LN) is a second layer technology for solving the scalability problem of blockchain-based cryptocurrencies such as Bitcoin. The LN nodes (i.e., LN users), linked by payment channels, can make payments to each other directly or through multiple hops of payment channels, subject to the available balances of the serving channels. In current LN implementation, the channel capacity (i.e., the sum of the bidirectional balances in the channel) is open to the public, but the bidirectional balances are kept secret for privacy concerns. Nevertheless, the balances can be directly measured by conducting multiple fake payments to probe the precise value of the balance. Such a method, while effective, creates many fake invoices and incurs high cost when used for discovering balances for multiple users.

This paper addresses the problem of inferring link loss rates based on network performance tomography in noisy network systems. Since network tomography emerged, all existing tomography-based methods are limited to the fulfillment of a basic condition: both network topologies and end-to-end routes must be absolutely accurate, which in most cases is impractical, especially for large-scale heterogeneous networks. To overcome the impracticability of tomography-based methods, we propose a robust tomography-based loss inference method capable of accurately inferring all link loss rates even when the given knowledge about the system is unreliable. Rather than computing the link loss rates directly from end-to-end loss rates, it calculates an upper bound for each link loss rate. It then infers all the link loss rates that most closely conform to the measurement results within their upper bounds. For a scenario where noisy measurements are caused by link (or router port) failures, we propose a hidden fault diagnosis approach that utilizes the inferred link loss rates to pinpoint the insidious faults that are hardly detected. It first determines the possible fake routes based on inferred link loss rates. Subsequently, it finds the maximum probable faults that can best explain the fake routes. Through intensive experiments, the results strongly confirm the promising performance of our proposed approaches.

In this paper, we propose a method to address the issue of link loss inference in uncertain networks. Although numerous loss inference methods have been proposed in recent years, most of them ignore the unstable states of networks. That is, the performances of real network environments, such as link loss rates and end-to-end routes, are constantly changing. Ignoring these uncertain factors of the underlying network significantly hinders development of a solution. To address this problem, we propose a method to infer the link loss rates, even when the network is uncertain. After obtaining the routing matrix corresponding to the given topology, optimal probing paths are selected from all available paths to measure the end-to-end loss rates. According to the measurement results, each link is divided into different loss levels. Finally, we compute the loss range of each congested link by sample fitting. Compared with a state-of-the art method applied to realistic Internet service provider topologies, our algorithm not only required fewer injected probes, but it also increased the accuracy by 25 to 35%. The promising results demonstrate that our new method can be well applied to the practical uncertain networks.