Large-scale Low Earth Orbit (LEO) constellations often experience high node failure rates caused by dynamic environmental factors (random failures), such as satellite maneuvers, or by cyber or physical attacks on critical nodes (targeted attacks), which pose unique challenges for routing optimization. Traditional algorithms such as Dijkstra suffer from limited parallel scalability on GPUs due to irregular neighbor distributions, while deep learning methods lack generalization ability. To address these challenges, we propose TRACE (Topology-aware Routing via Adjacent-Constraint Encoding), a graph-constrained Transformer architecture equipped with a cascaded multi-head attention decoder for distributed dynamic routing in LEO domains. To improve algorithm throughput and resilience against random failures and targeted attacks, we further design NFD (Navigator–Follower Distillation), a self-distillation framework which enables each agent to learn routing policies from Monte Carlo episodes. Furthermore, a hierarchical distributed routing architecture is developed to extend the proposed method to multi-domain scenarios. Simulation results demonstrate that TRACE with NFD achieves near-optimal routing accuracy with controllable inter-domain errors, while significantly improving throughput compared with mainstream routing algorithms.

Xiuxuan Shen, Zhongyuan Jiang, Junsan Zhang, Junxiao Han, Yao Wan, Chengjie Guo, Bingcheng Liu, Jie Wu, Renxiang Li, Philip S. Yu. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Low Earth Orbit (LEO) mega-constellations enable the Internet of Things (IoT) industry to realize the vision of integrated space-air-ground-sea communication networks under B5G and 6G. However, due to the high complexity of O(|V|log|V|+|E|) of Dijkstra’s algorithm, existing LEO mega-constellations suffer from excessive routing reconvergence time under frequent topology changes caused by satellite-ground station link handovers and network failures. To this end, we propose MLDR, a Manhattan-like topology-and Low ISL Delay-based Routing scheme with an ultra-low routing complexity of O(|V|4) while maintaining near-optimal routing. Firstly, for any source satellite, MLDR divides the Manhattan-like topology of LEO mega-constellations into four non-interfering Minimum Hop (MH) areas. Secondly, MLDR concurrently and non-repeatedly computes MH paths for all destinations within each MH area and installs routing tables. Thirdly, MLDR incrementally computes and updates the MH paths by more optimal MH detour paths, which select other neighboring satellites as relaying nodes. Finally, by conducting extensive simulations on real-world LEO mega-constellations, our MLDR outperforms all state-of-the-art schemes by remarkably reducing reconvergence time and achieving the highest routing optimality.

Social networks especially the social communities facilitate the rapid and rich social activities of all individuals in the world. However, advanced community detection brings serious privacy disclosure (e.g., whether two important and sensitive individuals belong to the same community?) to us. For instance, online plainclothes policemen can be regarded as marginal community users who are often in the same community initially and in need of penetrating into as many as possible different communities to collect illegal evidence of network criminals, but adversarial community inference which can maliciously disclose the sensitive user relationships within a target community will expose their privacy and lead to task failure. Thus, privacy protection for the marginal community users becomes an urgent issue which is still open so far. In this work, we aim to study the community privacy protection for target marginal individuals of a community against multiple adversarial community detection (ACD) attacks. First, we define the marginal community user hiding problem and propose a marginal user pair selection strategy. Second, to enhance the privacy effectiveness of conventional methods, we propose a deep graph learning approach to maximally find the minimum link perturbation cost. Finally, we conduct various community detection attacks on many real social graphs, and the experimental results show that our method can more effectively hide the marginal-sensitive user pairs than baselines.

The emergence of generative models has revolutionized the field of remote sensing (RS) image generation. Despite generating high-quality images, existing methods are limited in relying mainly on text control conditions, and thus do not always generate images accurately and stably. In this article, we propose CRS-Diff, a new RS generative framework specifically tailored for RS image generation, leveraging the inherent advantages of diffusion models while integrating more advanced control mechanisms. Specifically, CRS-Diff can simultaneously support text-condition, metadata-condition, and image-condition control inputs, thus enabling more precise control to refine the generation process. To effectively integrate multiple condition control information, we introduce a new conditional control mechanism to achieve multiscale feature fusion (FF), thus enhancing the guiding effect of control conditions. To the best of our knowledge, CRS-Diff is the first multiple-condition controllable RS generative model. Experimental results in single-condition and multiple-condition cases have demonstrated the superior ability of our CRS-Diff to generate RS images both quantitatively and qualitatively compared with previous methods. Additionally, our CRS-Diff can serve as a data engine that generates high-quality training data for downstream tasks, e.g., road extraction. The code is available athttps://github.com/Sonettoo/CRS-Diff.

The adoption of SDN/NFV opens avenues for efficient network slicing deployment and cost control. However, the dynamic cost reduction brought by deployment location optimization is not suitable for all scenarios. To further reduce the cost, we recommend a sharing strategy in NFV. In this paper, we introduce a two-layer VNF multiplexing architecture, named PSMA, which guarantees both efficient VNF sharing operations and secure slicing during multiplexing. Leveraging the SDN/NFV features, the proposed scheme splits data processing and key management and establish secure connections using SDN’s programmable routing. The provided framework integrates comprehensive life-cycle management and key delivery mechanisms. The article substantiates its availability through extensive simulations of VNF reuse on a randomly generated network with Virtual Network Requests (VNR). The empirical results indicate a significant cost reduction of 5% to 10%, particularly pronounced in scenarios involving a substantial number of short-lived and transitional VNFs.

The Proactive Recovery (PR) mechanism in Software-Defined Networking (SDN) provides good failure recovery resilience for the Beyond Fifth-Generation/Sixth-Generation (B5G/6G) delay-sensitive applications. However, PR’s fixed single backup path policy for any flow and fine-grained backup forwarding rule configuration poses severe challenges for post-recovery congestion management and limited Ternary Content Addressable Memory (TCAM) space in SDN switches. To this end, we propose K-backup, a load-and TCAM-aware multi-backup fast failure recovery scheme for SDNs. Firstly, K-backup formulates and solves the congestion-aware multi-backup path planning problem for various failure scenarios, exploiting the inherent load diversity of multi-backup paths to minimize the post-recovery maximum link utilization. Secondly, K-backup aggregates flows sharing the same backup-path-weight pair on a link into a cascading table of Fast-Failover and SELECT groups. Meanwhile, each outputted backup path is labeled, and a corresponding label-matching flow table is configured for each intermediate switch to aggregate all flows on that path. Thirdly, K-backup dynamically adjusts the backup path update period based on the network load stabilization to reduce unnecessary controller overhead. Compared with state-of-the-art, K-backup achieves the lowest controller overhead, the best load balancing performance, the near-fewest TCAM space usage, and the near-shortest recovery time.

In unmanned aerial vehicle (UAV) performing tasks, the UAV often faces electricity shortages. The traditional scheme to charge a UAV needs to return to the ground. Using the charging UAV (CUAV) can avoid the waste of electricity caused by the return. However, the existing works only consider a fixed charging location for electricity replenishment. Moreover, fewer works focus on the matching relationship between multi-CUAV and multi-UAV. It is challenging to complete the expected charging work due to the mismatch between the electricity demand and supply. To address this problem, we propose a two-stage electricity scheduling scheme. Specifically, in the charging location selection stage, we solve the Nash equilibrium (NE) of flight consumption between CUAVs and UAVs through the exact potential game, thereby determining the accessible charging position. Then, in the electricity transaction stage, we adopt the Stackelberg game model to determine the Stackelberg equilibrium (SE) between the acceptance rate of CUAVs and the rejection rate of UAVs, ensuring that both CUAVs and UAVs are satisfied with the unit electricity prices and electricity demands. Based on the above two game stages, we propose a supply and demand scheduling (SDS) algorithm to achieve dynamic scheduling between CUAVs and UAVs. Theoretical analysis indicates the exits of NE and SE. Furthermore, the extensive experiments show that our scheme has significant advantages over the baselines in charging cost, charging price, and flight consumption.

The surging interest in cryptocurrency has revitalized the research for digital signature schemes with strong security. In particular, signature schemes are investigated to resist the malleability attacks in cryptocurrency platforms. However, existing signature schemes only conquer partial malleability attacks due to various sources of attacks. Other solutions of new transaction realizations cannot simultaneously avoid the malleability attacks on both standard and contract transactions. Furthermore, the malleability attack becomes more stubborn in fast clearing applications. In this paper, we propose SigNT, an accessional signature scheme with unmalleable transaction implementations. The key of SigNT is an improved interactive signature scheme for securely instant confirmation of transactions. Unlike standard signatures, this signature is generated by the owner and block producers. Combining it with several other optimizations (i.e., hash execution of intermediate transactions and secret-based claiming conditions), SigNT achieves complete resistance against malleability attacks in both the standard and contract transactions. As an example, we show an implementation in Bitcoin with the “providing a deposit” protocol. The security analysis and comparative experiments demonstrate that SigNT has the best resistance against malleability attacks than previous malleability solutions. Besides, better performance is achieved than other schemes.

Color invariance is critical for computer vision systems since it significantly increases the robustness and effectiveness of the system. MSDA approaches (e.g., FMix and CutMix) have attracted considerable attention in recent years since they are simple, effective, and do not require extra computation con-sumption. By mixing samples, these approaches extend the distribution of training samples. However, the color information of these mixed samples is not changed, which makes it still difficult for trained models to achieve color invariance. To address this issue, we propose a universal module called Mixed Color Channels (MCC) that implements color changes by mixing the sample and its color variants, which enables trained models to achieve color invariance. In the experimen-tal section, we insert MCC into four state-of-the-art MSDA approaches, evaluate its effectiveness, and embed MCC into a non-MSDA method to demonstrate its extensibility.

The modern system is becoming more and more complex in scale and structure. Mastering the operation status is crucial to ensure the stable and reliable operation of the system. Log anomaly detection is the critical means of system state monitoring and anomaly response. However, the characteristics of complex log data structure, large amount of data and hidden abnormal behavior patterns bring new challenges to efficient and automated log anomaly detection. This paper summarized the basic framework of log anomaly detection, including log collection and filtering, log parsing, feature extraction and anomaly detection. We have reviewed the relevant technologies and methods involved in each link. In particular, various deep learning detection models in recent years are analyzed, such as the use of recurrent neural network and convolutional neural network to capture the context information of log sequences, the use of generative adversarial network to make up for the deficiency of abnormal data, and the training of federated learning between different systems. We hope that our work can help beginners understand log anomaly detection and relevant experts keep abreast of the latest research trends.

It is an obvious fact that drivers’ drowsiness is more likely to cause traffic accidents. Recently, driver drowsiness detection has drawn considerable attention. In this paper, a novel drowsiness detection scheme is proposed, which can recognize drivers’ drowsiness actions through their facial expressions. First, a drowsiness action recognition model based on 3D-CNN is proposed, which can effectively distinguish drivers’ drowsiness actions and nondrowsiness actions. Second, a fusion algorithm of the two input streams is proposed, which can fuse gray image sequence and optical image sequence containing target motion information. Finally, the proposed model is evaluated on National Tsinghua University Driver Drowsiness Detection (NTHU-DDD) dataset. The experimental results show that the algorithm performs better than other algorithms, and its accuracy reaches 86.64%.

Rumors (i.e., untrue emergence saying of COVID-19 in an area) that rapidly disseminate on the ubiquitous social media easily cause public panics and irrational behaviors (e.g., taking unnecessary medicine) of many very sensitive individuals referred to astarget recipients. Thus, rumor controlling or blocking for these target recipients is very critical, which differs from the traditional way of protecting all individuals and remains an open and challenging problem so far. In this work, on the basis that rumors are disseminated from the given sources to target recipients via multiple paths which may be significantly interrupted by deleting a few key links referred to asprotectors, we first mathematically define a general target information disseminating (TID) model and do theoretical proofs. Second, based on the TID model, we introduce a random walk algorithm to sample the paths of rumor dissemination for recipients. Third, aiming at deleting a budget-limited set of protectors efficiently in a large number of selected paths to reduce or weaken the negative rumor influences on the target recipients, we propose a heuristical strategy-based rumor influence decay mechanism referred to asRumorDecay(i.e., RumorDecay$k$hop nearest neighbor method and RumorDecay$k$hop random walk method in this work) which can locate the optimal protectors quickly and efficiently. Finally, we conduct extensive experiments on many real social networks and the results show that the RumorDecay strategy can significantly weaken the rumor dissemination ability with less time cost.

It is crucial to generate random graphs with specific structural properties from real graphs, which could anonymize graphs or generate targeted graph data sets. The state-of-the-art method called spectral graph forge (SGF) was proposed at INFOCOM 2018. This method uses a low-rank approximation of the matrix by throwing away some spectrums, which provides privacy protection after distributing graphs while ensuring data availability to a certain extent. As shown in SGF, it needs to discard at least 20% spectrum to defend against deanonymous attacks. However, the data availability will be significantly decreased after more spectrum discarding. Thus, is there a way to generate a graph that guarantees maximum spectrum and anonymity at the same time? To solve this problem, this paper proposes graph nonlinear scaling (GNS). We firmly prove that GNS can preserve all eigenvectors meanwhile providing high anonymity for the forged graph. Precisely, the GNS scales the eigenvalues of the original spectrum and constructs the forged graph with scaled eigenvalues and original eigenvectors. This approach maximizes the preservation of spectrum information to guarantee data availability. Meanwhile, it provides high robustness towards deanonymous attacks. The experimental results show that when SGF discards only 10% of the spectrum, the forged graph has high data availability. At this time, if the distance vector deanonymity algorithm is used to attack the forged graph, almost 100% of the nodes can be identified, while when achieving the same availability, only about 20% of the nodes in the forged graph obtained from GNS can be identified. Moreover, our method is better than SGF in capturing the real graph’s structure in terms of modularity, the number of partitions, and average clustering.

Complex social network is a kind of relationship system composed of many nodes according to social relations. Community detection helps scholars to understand this network topology and find out meaningful communities. Many scholars are therefore actively exploring new community detection algorithms. However, it brings privacy issues such as the disclosure of personal or group information of community members and goes against an individual or group desire to be hidden. Hence, how to hide a target community in a network to resist the community detection algorithms becomes critical. Given this, this article studies the community hiding problem, which has not been extensively focused so far and aims to properly hide a target community into other communities by perturbing a budget limited number of social links. First, we formalize the community hiding problem and design a gain function. Second, we prove the feasibility of link perturbation operations and propose an efficient algorithm to solve the community hiding problem. Finally, we conduct extensive experiments on varieties of real small- and large-scale social networks and compare our method with other community deception algorithms. The experimental results demonstrate that the proposed algorithm is more efficient than previously proposed algorithms.

Single-path routing in Software Defined Network (SDN) is vulnerable to Sybil attacks, which can damage the routing and even the entire network by forging the identification of multiple normal nodes. Meanwhile, the multi-path routing has been proved to be able to improve the routing security in SDN significantly. In this paper, we aim to enhance the path reliability against Sybil attacks. We first present one random and three targeted Sybil attacker locating models which incorporate degree, betweenness, and PageRank information respectively. Secondly, we propose an improved Multi-Path-Trees (iMPT) algorithm which performs a review of Sybil nodes by calculating node reliability and uses a partial re-routing method to avoid incorrect deletion of routing paths on the multi-path tree. Finally, the experimental simulations on two kinds of classical generated networks and a real network confirm the effectiveness of our proposed algorithm for resisting Sybil attacks in SDN.

In this paper, we incorporate the realistic scenario of key protection into link privacy preserving and propose the target-link privacy preserving (TPP) model: target links referred to as targets are the most important and sensitive objectives that would be intentionally attacked by adversaries, in order that need privacy protections, while other links of less privacy concerns are properly released to maintain the graph utility. The goal of TPP is to limit the target disclosure by deleting a budget limited set of alternative non-target links referred to as protectors to defend the adversarial link predictions for all targets. Traditional link privacy preserving treated all links as targets and concentrated on structural level protections in which serious link disclosure and high graph utility loss is still the bottleneck of graph releasing today, while TPP focuses on the target level protections in which key protection is implemented on a tiny fraction of critical targets to achieve better privacy protection and lower graph utility loss. Currently there is a lack of clear TPP problem definition, provable optimal or near optimal protector selection algorithms and scalable implementations on large-scale social graphs. Firstly, we introduce the TPP model and propose a dissimilarity function used for measuring the defense ability against privacy analyzing for the targets. We consider two different problems by budget assignment settings: 1) we protect all targets and to optimize the dissimilarity of all targets with a single budget; 2) besides the protections of all targets, we also care about the protection of each target by assigning a local budget to every target. Moreover, we propose two local protector selections, namely cross-target and with-target pickings. Each problem with each protector picking selection is corresponding to a greedy algorithm. We also implement scalable implementations for all greedy algorithms by limiting the selection scale of protectors, and we prove that all greedy-based algorithms achieve approximation by holding the monotonicity and submodularity. Through experiments on large real social graphs, we demonstrate the effectiveness and efficiency of the proposed target link protection methods.

The rapid development of e-commerce requires efficient and reliable logistics services. Nowadays, couriers are still the main solution to address the "last mile" problem in logistics. They are usually required to record the accurate delivery time of each parcel manually, which provides vital information for applications like delivery insurances, delivery performance evaluations, and customer available time discovery. Couriers' trajectories generated by their PDAs provide a chance to infer the delivery time automatically to ease the burdens on the couriers. However, directly using the nearest stay point to infer the delivery time is under satisfactory due to two challenges: 1) inaccurate delivery locations, and 2) various stay scenarios. To this end, we propose Delivery Time Inference (DTInf), to automatically infer the delivery time of waybills based on couriers' trajectories. Our solution is composed of three steps: 1) Data Pre-processing, which detects stay points from trajectories, and separates stay points and waybills by delivery trips, 2) Delivery Location Correction, which infers true delivery locations of waybills by mining historical deliveries, and 3) Delivery Event-based Matching, which selects the best-matched stay point for waybills in the same delivery location to infer the delivery time. Extensive experiments and case studies based on large scale real-world waybill and trajectory data from JD Logistics confirm the effectiveness of our approach. Finally, we introduce a system based on DTInf, which is deployed and used internally in JD Logistics.

Vehicular ad-hoc networks (VANETs) have recently attracted considerable attention from both industry and academia for improving road safety and traffic efficiency. Trust modeling plays a significant role in VANETs, however, the existing trust models cannot primely conform to the characteristics of VANETs. This article proposes a novel trust cascading-based emergency message dissemination (TCEMD) model which incorporates the entity-oriented trust values into data-oriented trust evaluation in an efficient manner. In the proposed model, when an emergency event (e.g., an obstacle in front of the road) occurs, the emergency messages can be disseminated among the nearby vehicles in a trust cascading manner, where the entity-oriented trust values (which are evaluated and updated by leveraging the trust certificates and are contained in the messages) are adopted as important weights. Subsequently, the theoretical analysis for the robustness against several kinds of attacks and malicious behaviors, failure tolerance features, compatibility for several kinds of special situations, and incentive mechanisms in the TCEMD model are detailed. Afterwards, a series of simulations and analyses are conducted in a typical highway environment, and the results reveal that the proposed model significantly outperforms the existing models in several cases.

The disclosure of an important yet sensitive link may cause serious privacy crisis between two users of a social graph. Only deleting the sensitive link referred to as a target link which is often the attacked target of adversaries is not enough, because the adversarial link prediction can deeply forecast the existence of the missing target link. Thus, to defend some specific adversarial link prediction, a budget limited number of other non-target links should be optimally removed. We first propose a path-based dissimilarity function as the optimizing objective and prove that the greedy link deletion to preserve target link privacy referred to as the GLD2Privacy which has monotonicity and submodularity properties can achieve a near optimal solution. However, emulating all length limited paths between any pair of nodes for GLD2Privacy mechanism is impossible in large scale social graphs. Secondly, we propose a Walk2Privacy mechanism that uses self-avoiding random walk which can efficiently run in large scale graphs to sample the paths of given lengths between the two ends of any missing target link, and based on the sampled paths we select the alternative non-target links being deleted for privacy purpose. Finally, we compose experiments to demonstrate that the Walk2Privacy algorithm can remarkably reduce the time consumption and achieve a very near solution that is achieved by the GLD2Privacy.

There is an underlying cascading behavior over road networks. Traffic cascading patterns are of great importance to easing traffic and improving urban planning. However, what we can observe is individual traffic conditions on different road segments at discrete time intervals, rather than explicit interactions or propagation (e.g., A→B) between road segments. Additionally, the traffic from multiple sources and the geospatial correlations between road segments make it more challenging to infer the patterns. In this paper, we first model the three-fold influences existing in traffic propagation and then propose a data-driven approach, which finds the cascading patterns through maximizing the likelihood of observed traffic data. As this is equivalent to a submodular function maximization problem, we solve it by using an approximate algorithm with provable near-optimal performance guarantees based on its submodularity. Extensive experiments on real-world datasets demonstrate the advantages of our approach in both effectiveness and efficiency.