Temporal knowledge graph (TKG) reasoning involves inferring future unknown facts based on historical data. Current approaches to temporal reasoning can be broadly categorized into two main paradigms: embedding-based methods and symbolic methods. While embedding-based methods excel at capturing time by representing temporal facts as vector, symbolic methods exploit temporal dependencies using techniques such as random walks for inference purposes. However, existing methods often fail to fully exploit both the inherent time and intricate temporal relationship patterns simultaneously. To address this limitation, we propose Temporal neural probabilistic logic learning (TNPLL), an innovative framework that seamlessly integrates symbolic logic with neural embeddings for robust temporal reasoning. Our approach incorporates two key components, a set of temporal logic rules equipped with explicit temporal relationships and a scoring module implemented through a novel temporal memory network architecture. The proposed method effectively combines time and temporal relationship patterns to predict future facts. We conducted experiments on several benchmark datasets, demonstrating that TNPLL achieves improved performance while fully leveraging time information. Specifically, our framework excels in scenarios where prior knowledge is available, but data samples are sparse. The experimental outcomes show that TNPLL outperforms state-of-the-art models in such cases.

Unmanned aerial vehicle (UAV) communications are susceptible to malicious compound interference signals due to the complexity and variability of the electromagnetic environment and the openness of the air-to-ground wireless channels, leading to degradation of communication quality. Therefore, effective detection and accurate recognition of compound interference are the key to ensuring secure UAV communication in complex environments. However, existing deep learning-based interference recognition algorithms suffer from fewer recognizable compound interference types, a large number of model parameters, and lower interference recognition accuracy under low interference-to-noise power ratio (INR) conditions. This paper proposes a malicious compound interference recognition method for UAV communication based on multi-modal multi-label learning and designs a lightweight multi-modal interference recognition network. By introducing a multi-label learning mechanism and making full use of the complementary information between different modalities of the signal, the method can achieve more flexible, accurate and stable recognition of compound interference signals under low INR. We construct both simulation and real measured datasets containing 31 classes of compound interference signals, and conduct simulation experiments with sufficient samples, insufficient samples, and different training strategies. The results demonstrate that the proposed method enhances the recognition accuracy of UAV communication compound interference under low INR and across different training datasets, all while maintaining a small number of model parameters.

Time series data analysis, employing dynamic time warping (DTW) algorithms, has a wide range of applications in fields such as medicine and economics. Given the widespread distribution of data across different domains, integrating and analyzing these datasets through outsourced cloud computing can enhance analytics, though privacy concerns arise. Privacy preserving data analysis, underpinned by secure multi-party computing, emerges as a crucial approach to address this challenge. However, existing efforts face high communication costs and increased interactions, resulting in significant efficiency constraints in practical applications. In this paper, we propose a function secret sharing (FSS)-based framework for secure collaborative analysis of time series data using the DTW algorithm. Utilizing the distributed comparison function, we develop efficient building blocks with minimal online interaction and communication, enhancing the practicability of security protocols. To address the challenges of FSS key generation due to uncertain computational topology when cascading multiple distances, we adopt a modular design and decompose the analysis process into several critical modules. Furthermore, our framework efficiently supports various constraint methods for DTW. We implement and evaluate our framework using publicly available datasets. The results demonstrate a significant reduction in communication costs and the number of interactions during the online phase.

Entanglement distribution between distant quantum nodes plays an important role in quantum networks. However, due to the unique properties of quantum mechanics and hardware limitations, entanglement resources in quantum networks are scarce. Quantum links that fail to meet request demands become bottleneck links, significantly hindering remote entanglement distribution in multi-request scenarios. In this paper, we propose an entanglement routing scheme called SwappingBoost that can effectively reduce resource consumption along entanglement distribution paths, alleviating the negative impact of bottleneck links. SwappingBoost first employs a decreasing resource reservation method to compensate for resource losses caused by failed entanglement swapping, freeing up pre-reserved resources on downstream links to accommodate other paths and requests. Besides, SwappingBoost introduces a path-priority-based rounding algorithm that achieves integer-level resource allocation while ensuring balanced resource allocation. Extensive simulation results demonstrate that SwappingBoost can effectively reduce the load of bottleneck links, enhancing network throughput while maintaining fairness among multiple requests.

Automatic modulation recognition plays a crucial role in the domain of electromagnetic situational awareness. Early recognition methods predominantly relied on expert experience and prior knowledge, demanding a high level of professional background and experience from practitioners, and usually underperformed in complex signal environments. In recent years, the continual development of deep learning (DL) technologies has introduced solution ideas to address the challenge of modulation recognition in complex electromagnetic environments. However, DL methods heavily depend on large volumes of high-quality labeled data and face challenges in real electromagnetic environments with limited samples. To fully leverage the respective strengths of expert knowledge in the radio domain and data-driven approaches, this article proposes a hybrid-driven neural network (HDNet) framework for radio signal recognition. HDNet integrates deep features extracted through data-driven methods with manual features extracted based on expert knowledge, aiming to enhance recognition performance in few-shot scenarios. Experimental results on both simulated and real measured datasets demonstrate that HDNet achieves high-recognition accuracy and robustness.

In recent years, multi-hop Quantum Key Distribution (QKD) network has been proven as a promising solution through rigorous practices to provide end-to-end key exchange service for arbitrary communication parties. However, existing decentralized solutions still face critical challenges including consistency and fairness that stem from storable nature of quantum key material. Thus, in this paper, we first devise a Key Management and Service (KM&S) framework for decentralized multi-hop QKD networks, which provides functional decoupling and pipeline processing to guarantee flexibility and compatibility for practical implementation. After that, to address consistency and fairness challenges during end-to-end key exchange service, we focus on two aspects including local key management and end-to-end congestion control, and respectively propose an elastic key supply rate control scheme named AUTO and a Capacity Probing-driven Backpressure Flow Control (CP-BFC) scheme. Furthermore, we construct an experiment platform equipped with realistic QKD devices based on China metropolitan QKD network topology to implement the proposed framework and schemes, and conduct extensive experiments compared to representative schemes in existing studies. The experimental results show that AUTO&CP-BFC significantly outperforms representative schemes in terms of consistency and fairness.

Quantum communication networks enable advanced quantum applications through remote entanglement distribution among source-destination pairs. Despite efforts to optimize entanglement distribution, fairness in multi-request scenarios has been neglected, potentially causing issues like “request starvation”. To address such issue, this paper concentrates on the unique properties of entangled systems and introduces a process-oriented fairness metric, i.e., expected throughput, departing from conventional approaches used in classical networks. Furthermore, we propose an entanglement distribution scheme named Fair-EAS, which prioritizes entanglement allocation and selection for batching multiple requests to maximize overall throughput while maintaining max-min fairness. To facilitate a convenient solution, we transform the nonlinearity of the problem into an equivalent linear programming formulation and decouple the solution into offline and online phases. In the offline phase, we design a multi-round water-filling-like optimization algorithm to determine the optimal path set for predicting entanglement allocation. In the online phase, we introduce an adaptive compensation algorithm and an entanglement “fragment” exhaustion algorithm to dynamically adjust the path set based on successfully generated entangled pairs. Comprehensive simulations show that Fair-EAS outperforms the existing schemes in terms of fairness by significantly enhancing the minimum throughput and throughput deviation among multiple requests while maintaining an overall throughput close to the optimal level.

Automatic modulation classification (AMC) plays an increasingly crucial role in intelligent spectrum management and dynamic spectrum access, which can effectively support the reallocation of low-utilization spectrum resources in wireless communication systems. While deep learning approaches have been widely employed in AMC, most deep learning-based AMC methods focus on signal classification as a singular task. Therefore, this paper proposes a multi-task learning-based method for radio signal recognition aimed at enhancing AMC performance. This method utilizes the designed multi-task collaborative learning network (MCLNet) model to achieve complementary gains across different tasks. By sharing parameters, it enhances the learning capability of crucial signal features, thereby acquiring more discriminative signal features and improving classification accuracy. Experimental results demonstrate that the proposed method outperforms other benchmark models on two benchmark datasets and exhibits greater performance gains in few-shot scenarios.

Electronic voting plays a crucial role in facilitating democratic and convenient decision-making in people’s lives. However, implementing an electronic voting system poses challenges, such as meeting the stringent security requirements for anonymity, fairness, and verifiability. Another concern is the performance degradation when dealing with a large number of voters. In this paper, we propose$S^{3}$Voting, a blockchain sharding-based e-voting scheme that addresses these challenges. By combining robust security and scalability,$S^{3}$Voting provides reliable technical support for conducting large-scale elections. Utilizing advanced technologies such asHomomorphic Time-Lock Puzzle (HTLP)andone-time ring signature, the system safeguards voters’ privacy and ballot confidentiality. The approach involves dividing voters and miners into smaller shards, and implementing shard managing mechanisms to ensure security and enhance system efficiency. Through thorough security analysis, we demonstrate that$S^{3}$Voting not only meets the fundamental security requirements of e-voting but also offers verifiability and strong robustness-essential elements for successful large-scale elections. Moreover, experimental results indicate that$S^{3}$Voting significantly reduces the computational burden on individual miners and minimizes system processing time compared to existing blockchain-based e-voting solutions.

The surge in blockchain-based cryptocurrencies has created a pressing need for Cross-Chain Transaction (CCTx) solutions. Existing solutions either lack sufficient security, like centralized exchanges, or suffer from poor efficiency and scalability, such as atomic swaps. Inspired by the success of the Lightning Network in accelerating Bitcoin transactions, we propose CrossChannel that establishes cross-and-off-chain micropayment channels to achieve efficient and scalable CCTx. Specifically, we analyze the challenges of extending one-chain channels to cross-chain scenarios caused by the separation of blockchains. To overcome these challenges, we employ the chain relay mechanism to synchronize channel-related information across blockchains and construct the channel management protocol on this basis, ensuring the same security level as one-chain channels in cross-chain settings. We prototype CrossChannel between two Ethereum testnets, comparing its transaction efficiency and costs with a typical HTLC-swap scheme. Results demonstrate the significant advancements in efficiency and scalability offered by CrossChannel. Even with channels closing after just 20 transactions, CrossChannel exhibits a fivefold capacity increase for handling CCTxs compared to HTLC swaps.

The lack of privacy-preserving capabilities hinders the further development of blockchains and smart contracts. While numerous privacy solutions have been proposed, limitations persist. First, most existing solutions focus on specific privacy protections such as anonymous payments, private data, or multi-party computation tasks. However, these solutions lack a general privacy ability, allowing users to deploy applications with diverse privacy requirements. Second, existing solutions have limited customizability, which means users cannot easily customize and adapt the privacy policies according to their specific demands or preferences. In this article, we present EtherCloak, which adopts trusted execution environments (TEEs) to achieve a general and customizable privacy policy on account model blockchains, enabling users to conceal any on-chain information. To address the security issues caused by the unreliability of the host the TEE runs on, we design the enclave state check and crash recovery mechanisms and employ them in the block generation process. In addition, we propose an access control mechanism for privacy policy management and data query. We prove that EtherCloak offers general and customizable privacy protection with a minimal increase in transaction size (less than triple) and communication overhead (approximately 10%) compared to Ethereum.

The widespread adoption of cloud storage has raised considerable data privacy concerns for outsourced databases. In recent years, Structured Encryption (STE) has emerged as a promising solution to build encrypted databases that efficiently handle queries while preserving privacy through underlying structures called Encrypted Multi-Maps (EMMs). However, current STE-based schemes primarily focus on static settings, and their direct extensions to dynamic settings introduce significant challenges in client storage overhead and update efficiency with join condition. In this paper, we present an efficient dynamic encrypted database scheme supporting large-scale data. To address the challenges in dynamic settings, we first propose a novel dynamic EMM design with constant client storage that utilizes a global counter to reduce client storage overhead. We then introduce an algorithm for dynamically handling join queries based on tags generated from values of the join attribute, significantly reducing update overhead. We implement our scheme and conduct comparative analyses with existing dynamic STE schemes. The experimental results demonstrate that our scheme offers significant advantages in terms of client storage overhead and update performance.

Searchable symmetric encryption (SSE) is a technique that enables secure outsourcing of data to an untrusted cloud server without sacrificing search functionality. Recently, multi-user SSE schemes for data sharing, which support access control from various users, have gained attention. However, the access control mechanisms in existing schemes are not adequate for realistic data-sharing scenarios as they do not consider time constraints or only partially address them, making these mechanisms unsuitable for SSE schemes. To address this issue, we first highlight the importance of time constraints in multi-user SSE and propose a completely time-constrained SSE scheme under a two-server model. By taking advantage of the Lagrange interpolation and pre-computation, our proposed scheme enables searching over time-related encrypted data with owner-enforced time constraints. Additionally, we employ the blinding technique with the assistance of a semi-honest time server to ensure the completeness of time constraints, which is not guaranteed in existing works. Based on the leakage function, we prove the security of our proposed scheme in the simulation-based security model. Furthermore, extensive experiments demonstrate the practicality of our scheme in supporting time-constrained functions.

Privacy-preserving truth discovery (PPTD) enables the crowdsensing platform to extract reliable inferred truths from unreliable user sensory data. While mobile crowdsensing systems have driven the emergence of many applications, continuously extracting inferred truths of evolving objects over streaming data (continuous PPTD) remains a challenge. Most existing works focus on static scenarios and cannot handle the new challenges in continuous PPTD, such as accuracy decrease, user dynamics, real-time requirements, and outliers. To address these challenges, we present PTET, a PPTD framework for continuous PPTD. By mining evolving patterns, PTET extracts accurate inferred truths of evolving objects even when some epochs lack sufficient user sensory data. PTET ensures the privacy of both users and data requesters while achieving high accuracy. Furthermore, we present PTET-P for practical applications. It employs a virtual user combined with evolving patterns to effectively eliminate the impact of user dynamics in continuous PPTD. Meanwhile, PTET-P achieves “immediate on-arrival processing” to improve real-time performance significantly. In addition, we address the outliers problem with the help of evolving patterns. We provide security analysis to prove that our frameworks protect the privacy of both users and data requesters. Extensive experiments demonstrate that our frameworks dramatically outperform the existing schemes in extracting inferred truths of evolving objects in continuous PPTD.

Crowdsourcing has emerged as an effective paradigm for large-scale data collection and statistical analysis. However, the paramount concern about worker privacy has driven the development of privacy-preserving statistical analysis methods. We propose PSAC, a novel framework that leverages histograms to facilitate privacy-preserving statistical analysis in crowdsourcing. PSAC integrates secure statistical analysis protocols based on homomorphic encryption and secure two-party computation, addressing the limitations of a single cryptographic technique. It introduces innovative algorithms using histograms for statistical operations, including functions such as quantile estimation, outlier elimination, contingency table construction for$\chi ^{2}$test, and the Mann-Whitney$U$test. These algorithms exhibit minimal overhead growth with respect to data volume, demonstrating exceptional scalability for large numbers of data. Moreover, through a key-separation design, PSAC ensures that only the requester can decrypt the final results independently, even if the ciphertexts of data are exposed. Comprehensive evaluations validate the security, efficiency, and scalability of the PSAC framework.

The high mobility in Vehicular Ad-hoc Networks (VANETs) significantly affects the reliability of data transmission. To solve this problem, Named Data Networking (NDN)-based VANETs are proposed, utilizing in-network caching and named-based forwarding to overcome the dual challenges of mobility and connectivity. Although in-network caching enhances content availability, a strategy that accurately locates and efficiently utilizes the cached content in VANETs with highly dynamic environments is still lacking. In this paper, we propose a novel NDN-based cache-aware adaptive forwarding (CAAF) strategy for VANETs. CAAF proactively predicts content locations and ensures reliable content retrieval by adaptively selecting forwarding nodes that prioritize fast delivery and stable transmission. Specifically, we design a content information table for each vehicle to record information about the Interest packets it receives. Furthermore, these tables are updated periodically across all vehicles and a prediction model is used to predict real-time in-network caching during the update interval. Subsequently, we execute a filter mechanism to sieve candidate forwarding vehicles that satisfy both the accessibility and stability requirements. These candidates are then evaluated using a multi-attribute decision-making method across diverse parameters to determine the optimal forwarding node. Our extensive simulation results demonstrate that the proposed CAAF outperforms the state-of-the-art forwarding strategy regarding content retrieval delay and Interest satisfaction ratio across diverse scenarios.

Multipath TCP (MPTCP) has gained popularity to enhance data transmission. From the last decade, proposed MPTCP enhancement schemes for congestion control, path management, and packet scheduling, have been used to benefit transmission performance. However, despite their efforts, they are exigent with a comprehensive understanding of real-world performance to guide the implementation of MPTCP to a more complex wireless network. To that end, we conduct a measurement-driven study of MPTCP enhancement schemes, providing insights and in-depth demonstrations of their performance with a comprehensive real-world platform. Our finding indicates that the enhancement schemes struggle to consistently maintain high performance at all times. One can achieve optimal efficiency in its specific scenarios, but suffers extreme degradation at times. To eliminate this transmission uncertainty in wireless networks, we further propose an adaptive control framework OLSch to integrate different schemes, emphasizing their strengths to provide consistently high performance. To be specific, OLSch is implemented with different scheduling schemes and leverages an online-learning-driven approach to choose one that best fits the current network conditions. Evaluations show that OLSch obviously improves the stability of transmission in harsh network scenarios, eliminates performance degradation, and increases the 95% tail throughput by 1.45×-2.39×.

Congestion control (CC) is crucial in data center networks (DCNs), providing high throughput and low latency transmission services for diverse applications. Existing CC schemes typically rely on iterative rate adjustment at ends, and suffer from performance issues such as slow convergence, throughput fluctuations, and fairness defects. Explicit rate control (ERC) promises to address these challenges by allowing switches to directly allocate rates for each flow, freeing senders from heuristic detection of available bandwidth. However, current ERC-based schemes employ inefficient feedback control to regulate the allocated rates, resulting in sub-optimal performance. In this paper, we propose RGuide, a fast and accurate CC scheme based on ERC. RGuide can calculate accurate fair share rates in real-time at switches with the consideration of low latency, and utilize the rate to guide host adjustments instead of the need for end-to-end iteration processes. We meticulously design the ERC trigger conditions, enabling switches to recognize the different congestion states of flows and rectify flows that deviate from the fair share rate at sub-RTT timescales. We conduct actual testbed experiments and extensive simulations to evaluate RGuide comprehensively. The results demonstrate the significant advantages of RGuide in terms of convergence speed, throughput stability, and fairness.

Quantum Key Distribution (QKD) technology leverages its inherent security advantages to ensure information-theoretic security for data transmission in networks. However, existing QKD networks still face critical challenges, including network congestion that stems from limited key resources and uneven resource allocation methods. Thus, in this paper, we focus on the issue of network congestion caused by bottleneck links and aim to achieve load balancing. Considering the limited key resources, we first introduce the key resource utilization ratio as an indicator of bottleneck links and formulate the resource allocation problem as an Integer Linear Programming (ILP) problem. To deal with the complexity of the ILP problem, especially in large-scale network scenarios, we design a heuristic algorithm that can obtain near-optimal solutions within polynomial time. Finally, we implement the proposed key resource allocation scheme in various real-world network topologies using a full-stack quantum network simulator. Compared to the existing algorithms, extensive results show that our method can reduce key resource consumption by up to 50% on bottleneck links and improve the robustness of QKD networks when facing burst quantum key agreement requests.

The entanglement transport protocol with a connection-oriented mode ensures the reliable distribution of remote entanglement by reserving dedicated resources on the selected path for users in a quantum network. In most existing protocols, entanglement generation and resource allocation operate with the support of global network-synchronized time slot. However, such synchronization in a large-scale quantum network is challenging, and the idealized time slot model is not conducive to continuous and concurrent requests. Meanwhile, different link performance in memory capacity and entanglement generation rate brings out critical issues, such as long distribution delay and low resource utilization, which has not been adequately addressed by the existing protocols relying on a heuristic adoption of TCP-like transport modes. In light of these observations, we propose a dynamic rate matching-based entanglement transport protocol called DRM-ETP, which allocates different memory units on each link along an entanglement distribution path. Moreover, DRM-ETP incorporates periodic forward and backward interactions to implement fine-grained feedback and a dynamic memory allocation based on priority differentiation. These mechanisms mitigate congestion and unfairness arising from resource contention among burst requests on shared links. Extensive simulation results demonstrate that DRM-ETP significantly outperforms the existing protocols in terms of throughput and resource utilization, with less distribution delay and higher fidelity. Moreover, DRM-ETP exhibits rapid and fair convergence when handling burst requests. Our study opens up possibilities for deploying efficient entanglement transport in quantum networks, thereby holding the promise of enhanced compatibility and novel functionality.

Quantum key distribution (QKD) networks can provide information-theoretically secure key distribution between distant end nodes through key relaying. In QKD networks, the key relay protocol is vital since it provides the coordination specifications between nodes for key relaying and thus directly determines the performance, especially as the network scale expands. However, most existing protocols adopt a synchronous contend-and-relay approach, where the contention and consumption of quantum keys occur simultaneously, neglecting the storable nature of quantum keys and presenting significant challenges in reliability and quantum key utilization. To tackle these challenges, in this paper, we propose an asynchronous key relay protocol (AKRP). AKRP considers the storable nature of quantum keys, and adopts a reserve-then-relay approach to achieve lossless and zero-queuing key relaying through precise management of quantum keys and requests. On this basis, to further improve the performance of the proposed AKRP, we design two enhanced mechanisms, i.e., collision detection and resolution mechanism and multipath routing extension. The former enhances the consensus efficiency of AKRP and provides fine-grained key utilization on each link, and the latter utilizes quantum keys on possible relay paths and thus effectively copes with quantum key exhaustion. By conducting extensive experiments on a semi-physical real QKD network platform, results demonstrate that AKRP is superior to existing schemes in terms of end-to-end key throughput, quantum key consumption, and relaying latency.

Real-time video streaming requires high throughput and low delivery time for enhanced user’s Quality of Experience (QoE). This motivates the use of multi-path transmission to improve performance. However, ensuring target performance within specified deadlines and priorities for video frames is particularly crucial for real-time communication and video quality, especially in scenarios with limited resources. To address this challenge, we propose a novel framework,vStreamPth, to guarantee high-quality real-time video streaming through multi-path transmission. For essential quality assurance,vStreamPthincorporates key requirement indicators that guide the transmission decisions of video frames across predefined multiple paths. In this framework, lightweight and robust decision-making is achieved through the collaboration of application-oriented and network-oriented data scheduling. Specifically, it employs robustness estimation to maintain the non-blocking delivery of frames, and further applies online fine-tuning to correct variations caused by changes in end-to-end transmission and multi-path network conditions. We implement a prototype ofvStreamPthin Linux user space and conduct a thorough evaluation. Experimental results demonstrate the absolute improvement ofvStreamPthin achieving high QoE and deadline satisfaction ratio compared to existing multi-path solutions.

A Link Flooding Attack (LFA) is a type of link-aimed Distributed Denial of Service (DDoS) attack that can overwhelm the Internet critical links to cut off connections with lots of low-rate, seemingly benign traffic. To defend against such threats, a promising solution involves mitigating the attack through load balancing. However, adaptive attacks employ two effective means to circumvent existing load balancing strategies. The first is the frequent changing of targets, known as rolling attacks. Rolling attacks exploit the delay between attack detection feedback and the mitigation of load balancing, depleting the defender’s resources. The second is the strategical selection of target links to create the worst-case scenario for load balancing algorithms. To address these challenges, we propose LinkDam. Specifically, LinkDam adopts a proactive approach by tracking and predicting potential victim links, providing defense against all targets of rolling attacks. Subsequently, we introduce a robust load balancing strategy to prevent the exploitation of selected link combinations. Additionally, LinkDam introduces a partial deployment approach, demanding a mere 40% of nodes be programmable (i.e., SDN nodes) while maintaining an acceptable 10% performance reduction from the maximum achievable. The experimental results indicate that LinkDam surpasses an 80% accuracy threshold, and exhibits a 57% higher tolerance to attack budgets compared to state-of-the-art solutions.

Link-flooding attacks (LFAs) pose a significant challenge to Internet availability by attacking critical network links with high volumes of seemingly legitimate traffic. In response, researchers have developed network topology obfuscation (NTO) to safeguard critical links. However, state-of-the-art NTO defenses are coarse-grained, leading to less efficient security and usability. In addition, once under attack, NTO schemes cannot identify the attacker’s bot and launch counter-defensive measures. To address these issues, this paper introduces SpiderNet, which employs advanced obfuscation techniques to secure critical links while using strategically created honeypot links for effective bot identification. When adversaries probe the network, SpiderNet captures their probing behavior and deliberately feeds back misinformation about honeypot links. By analyzing the attack patterns directed at these decoy targets, SpiderNet correlates them with adversarial probing activities to effectively identify the bots. Our experiments demonstrate that SpiderNet is more robust than state-of-the-art NTO schemes in terms of security and usability, while also being capable of identifying LFA bots.

Quantum networks, which work by establishing entanglement between distant quantum end nodes (known as end-to-end entanglement distribution), are the promising infrastructure for quantum applications. However, the inherent loss in quantum channels and quantum decoherence contribute to the scarcity of entanglement resources in quantum networks. Consequently, there is an inevitable gap between available entanglement resources and requests’ demands, significantly hindering concurrent end-to-end entanglement distributions. In this paper, we present NarrowGap, an end-to-end entanglement distribution design that can alleviate the negative impact of entanglement resource scarcity on the request service capability of quantum networks. At the heart of NarrowGap, the resource transfer scheme (RTS) is designed to transfer idle entanglement resources to boost the bottlenecks’ capacities based on the unique feature of entanglement swapping, thus narrowing the gap between available entanglement resources and requests’ demands for end-to-end entanglements. Besides, NarrowGap presents a resource allocation scheme (RAS) to guarantee fairness, considering both the success probability of end-to-end entanglement distribution and each request’s demand, to address resource competition in bottlenecks. Extensive simulations demonstrate that NarrowGap outperforms three representative schemes and can achieve more than twice the performance improvement in request service rate.

Named Data Networking (NDN) has emerged as a promising network paradigm for the future Internet. It revolutionizes content retrieval by decoupling it from specific locations, thereby overcoming the limitations of traditional IP addressing and significantly enhancing data delivery efficiency. Additionally, NDN’s stateful forwarding plane for routers enables robust aggregation of identical requests, bolstering resistance against Distributed Denial of Service (DDoS) attacks. Despite these advancements, NDN remains vulnerable to the Interest Flooding Attack (IFA), wherein excessive requests from attackers can compromise transmission quality by depleting router resources. In the current landscape, researchers have proposed various strategies aimed at improving the accuracy, timeliness, and cost-effectiveness of defenses against IFA attacks, presuming stable user behavior. However, several challenges persist in effectively countering IFA attacks, including the need to ensure transmission quality throughout users’ lifecycles, eliminate attacks at their origin, and adapt to dynamic user behaviors. In response to these challenges, this paper presents the Hybrid Proactive Reactive Defense Scheme (HPR-DS). HPR-DS employs distinct proactive and reactive modules for resource management and user behavior analysis, respectively, at intermediate and edge nodes. It employs time series analysis to gauge evolving resource requirements and maintains separate resource pools for each content. Additionally, HPR-DS utilizes multidimensional data clustering to accurately identify attackers. Simulation results demonstrate the superior performance of HPR-DS in safeguarding user transmission quality throughout the entirety of their lifecycle and in enhancing detection precision in dynamic network environments.

Load balancing in data center networks (DCNs) is a crucial and complex undertaking. Multi-path TCP (MPTCP) has been proposed as a cost-effective solution that aims to distribute workloads and improve network resource utilization. However, it can escalate buffer occupancy and undermine burst tolerance, particularly in scenarios involving incast short flows. To address these limitations, we propose a novel multi-path congestion control algorithm, RateMP, to optimize bandwidth utilization efficiency while ensuring burst tolerance in DCNs. RateMP employs a hybrid window and rate control loop with coupled gradient projection adjustment, enabling fast and fine-grained bandwidth allocation and accelerating convergence. Additionally, RateMP eliminates the limitation of cwnd with under-rate pacing to protect incast and busty flows. We prove that RateMP is Lyapunov stable and asymptotically stable, and show the improvement of RateMP through a kernel-based implementation and extended large-scale simulations. RateMP keeps high bandwidth utilization, cuts RTT by 2x and reduces flow completion times (FCT) by 45% in incast scenarios compared to existing algorithms.

Remote entanglement distribution in an efficient and reliable manner, especially in the context of a large-scale quantum network with multiple requests, remains an unsolved challenge. The key difficulties lie in achieving spontaneous and precise control over the entanglement distribution procedure, as multiple nodes need to reach a consensus on how to perform it. From the network aspect, allocating link-layer entangled pairs as resources to achieve high efficiency is also challenging. To address these issues, we propose a decentralized Reliable Entanglement Distribution Protocol (REDP) for large-scale networks. The protocol operates in a Forward-Backward Propagation (FBP) manner, where consensus is reached hop-by-hop and disseminated to all nodes on the path. We further use probabilistic analysis and quasi-static modeling to seek the fairness and efficiency of the network based on the above transmission model. Accordingly, we introduce a Source Window Strategy (SWS) and an Entanglement Allocation Strategy (EAS) to assign sending windows and allocate resources for multiple requests, ensuring a high level of fairness and efficiency from a network perspective. Through systematic simulations involving both classical and quantum communication protocols, we demonstrate that REDP outperforms existing approaches in terms of fairness, throughput, and fidelity performance.

Set-valued data are commonly used to represent subsets of a universal set and are frequently utilized in online services, such as online shopping preferences, website browsing records, and recently visited places. By collecting set-valued data from users, service providers can perform statistical analysis to obtain a joint distribution of service usage data and subsequently learn the association between different kinds of set-valued data to improve the quality of service. However, collecting set-valued data raises privacy concerns about the potential misuse of records to infer individuals’ identities and preferences. Although some privacy-preserving aggregation mechanisms for set-valued data have been proposed, they have not yet achieved joint distribution analysis with high accuracy. In this paper, we propose a joint distribution analysis method for set-valued data with local differential privacy (LDP). We design a scalable perturbation mechanism under$\epsilon $-LDP by limiting the range of users’ responses in the collection process and cyclically shifting the set-valued data in an encoded uniform format, ensuring that the size of the universal set does not influence the accuracy of the results. Based on the perturbation method, we develop an analysis method to efficiently obtain association information between two sets. By performing specific bitwise operations on the perturbed data matrices, the computational overhead is linear with respect to the cardinality of the item set. In addition to theoretically analyzing the error bound and proving the security of our work, extensive experimental results on synthetic and real-world datasets demonstrate that our scheme achieves better utility than existing state-of-the-art approaches.

Federated Learning (FL) enables multiple distributed clients to collaboratively train a model with owned datasets. To avoid the potential privacy threat in FL, researchers propose the DP-FL strategy, which utilizes differential privacy (DP) to add elaborate noise to the exchanged parameters to hide privacy information. DP-FL guarantees the privacy of FL at the cost of model performance degradation. To balance the trade-off between model accuracy and security, we propose a differentially private federated learning scheme with an adaptive noise mechanism. This is challenging, as the distributed nature of FL makes it difficult to appropriately estimate sensitivity, where sensitivity is a concept in DP that determines the scale of noise. To resolve this, we design a generic method for sensitivity estimates based on local and global historical information. We also provide instances on four commonly used optimizers to verify its effectiveness. The experiments on MNIST, FMNIST and CIFAR-10 convincingly prove that our proposed scheme achieves higher accuracy while keeping high-level privacy protection compared to prior works.

As a promising complement to terrestrial cellular networks, satellite networks have recently drawn increasing attention, offering seamless coverage cost-effectively. However, with the rapidly increasing users' demand for multimedia content, how to achieve efficient content transmission seamlessly becomes a critical but knotty problem. To provide an efficient solution from the routing perspective, in this paper, we propose an opportunistic content-aware routing scheme. Our scheme combines the features of in-network caching and content awareness of information-centric networking (ICN) architecture. The basic idea of the proposed scheme is to sense users' requests and find the optimal route solution with the largest potential gain. Moreover, considering the limitation of real-time signaling collection in satellite networks, we design a cached content prediction method. The method is capable of inferring the probability of content being cached based on historical popularity information, providing essential information for measuring potential gains. Extensive simulation results demonstrate that the proposed opportunistic content-aware routing scheme outperforms baseline approaches with significantly reduced delay and traffic consumption.

Entanglement distribution between distant quantum nodes plays an essential role in realizing quantum networks’ capabilities. In addition to path selection, remote entanglement distribution involves two pivotal quantum operations, i.e., entanglement generation and entanglement swapping. The existing studies mainly adopt two methods, i.e., Tell-and-Generation (TAG) and Tell-and-Swapping (TAS), to manage these two quantum operations on a selected path. However, both methods fatally introduce redundant stop-and-wait processes, which are detrimental to the performance of remote entanglement distribution in terms of latency and fidelity. To achieve low-latency and high-fidelity entanglement distribution between far-off quantum nodes, we propose a segment-based method consisting of an entanglement generation algorithm and a segment design to diminish the unnecessary stop-and-wait processes. The entanglement generation algorithm adopts a concurrent design to establish entanglement links using the one-demand generation model, thus effectively reducing waiting time compared to hop-by-hop and parallel designs. The segment design is proposed to split a long-distance path into multiple short-haul segments with the similar ability to swap entanglement, and these segments build multi-hop entanglement connections in parallel. Extensive simulations show that the segment-based method significantly outperforms the existing methods, including TAG and TAS, in entanglement distribution latency and effectively mitigates fidelity attenuation.

With the development of datacenter networks (DCNs) towards high bandwidth and low latency, the demands of high-level datacenter applications are heading towards high performance and high reliability, which makes traffic congestion one of the most notable problems in DCNs and brings new challenges to transport protocols. Proactive transport protocols are gaining prevalence due to their ability to provide accurate feedback and precise end-to-end control, while multipath transmission is having a broader application space in the multi-path topology of large-scale DCNs. However, these advanced transport protocols aim to improve their performance by addressing some specific congestion problems, but fail to handle multiple congestion problems caused by incast, high workload and load imbalance. Their performance in terms of flow completion time (FCT), delay, robustness, and balance still has room for further improvement. In this paper, we propose ProactMP, a novel proactive multipath transport protocol for further improvement of datacenter communications. ProactMP utilizes the rich resources of parallel paths in modern DCN and spreads the load across available network paths to improve network efficiency. ProactMP deploys a credit-based bandwidth allocation strategy to achieve low delay and zero packet loss, and overcommits receiver downlinks to ensure high link utilization. We have implemented ProactMP in the Linux system. Our testbed experiments show that ProactMP outperforms the TCP variants, MPTCP variants and a leading proactive transport protocol in FCT, link utilization, fairness and latency.

The quantum network that allows users to communicate in a quantum way will be available in the foreseeable future. The network capable of distributing Bell state entangled pairs faces many challenges due to entanglement decoherence and limited network performance, especially when the network scale is enormous. Many entanglement distribution protocols have been proposed so far, and most of them are in a centralized and synchronized manner, which may be infeasible in large-scale networks. As such, in this paper, we propose a full spontaneous version of quantum networks in which the quantum nodes autonomously manage multiple entanglement distribution requests. However, one major issue is that quantum nodes have little knowledge about the network, especially the congestion (e.g., some nodes may have no usable quantum memories). We present a routing algorithm to adaptive evaluate the congestion on the neighbor nodes to avoid potential congestion. We use SimQN, the new network layer simulation platform built by our research team, to evaluate our proposed design. The result demonstrates that it can adapt to changes in network resources and reduce the drop rate that eventually leads to a higher entanglement distribution rate but remains fair for multiple requests to use the network resources fairly and achieve a more balanced throughput.

Named Data Networking (NDN) stands out as a promising Information Centric Networking architecture capable of facilitating large-scale content distribution through in-network caching and location-independent data access. However, attackers can easily inject poisoned content into the network, called content poisoning attacks, which leads to a substantial deterioration in user experience and transmission efficiency. In existing schemes, routers fail to determine the contamination source of received poisoned content, leading to the inability to accurately identify attacker nodes. Besides, attackers’ dynamic behaviors and network instability could disrupt identification results. In this paper, we propose a Secure and Lightweight scheme against content poisoning attacks based on Probing (SLP), where a proactive and reliable probing protocol is designed to identify adversaries quickly and precisely. In SLP, a router sends specifically chosen interest packets to probe a suspicious node, so that the returned corresponding content can straightly reflect its trustworthiness without other nodes’ interference. In addition, a hypothesis testing algorithm is developed to analyze the returned content, which can exclude the impact of transmission errors and adapt to dynamic attackers. Moreover, we utilize users’ feedback to avoid unnecessary probing costs on unaffected routers, with its reliability guaranteed by an efficient cuckoo-filter-based feedback validation mechanism. Security analysis shows that SLP achieves resistance against content poisoning attacks and malicious feedback. The experimental results demonstrate that SLP makes users hardly be affected by attacks and brings in only slight overhead.

Named Data Networking (NDN), with a receiver-driven connectionless communication paradigm, naturally supports content delivery from multiple sources via multiple paths. In a dynamic environment, sources and paths may change unexpectedly and are uncontrollable for consumer, which requires flexible rate control and real-time multi-path management, still lacking investigations. To address this issue, we propose an Adaptive Multi-source Multi-path Congestion Control (AMM-CC) scheme based on online learning. AMM-CC explores source/path distribution with continuous micro-experiments and abstracts the empirically experienced performance by meticulously designed two-level utility functions. Specifically, AMM-CC enables each consumer to optimize a local transmission-level utility function that fuses multi-source characteristics, including congestion level and source weights. Then, a sub-gradient descent method is designed to adjust transmission rate adaptively and achieve fine-grained control. Moreover, AMM-CC coordinates consumer with the forwarding module to ensure efficient and on-time multi-path management. It enables consumer to determine congestion gap among multiple paths by a path-level utility that sensitively captures changes and congestion on each path. Then, consumer further notifies the forwarding module in achieving precise traffic transferring. We conducted comprehensive evaluations in dynamic scenario with various content distribution using the NDN simulator, ndnSIM. The evaluation results demonstrate that AMM-CC can adapt to flexible content acquisition from multi-sources and significantly improve bandwidth utilization of multi-path compared with state-of-the-art schemes.

In data center networks (DCNs), numerous congestion control schemes utilize explicit congestion notification (ECN) to achieve low average queue delay. Such schemes generally mark packets based on the current queue length exceeding a marking threshold. However, due to the delay of ECN feedback, the queue length may further increase before the congestion notification is delivered to senders, which may lead to uncontrollable maximum queue length when bursts occur. In this paper, we propose an early ECN marking scheme based on prediction, E-ECN, to control the maximum queue length in DCNs. E-ECN uses predicted queue length rather than the current to indicate congestion with an advance time which offsets the hysteresis of ECN. We theoretically and experimentally demonstrate that early marking does not impact the throughput with appropriate selection of the advance time, and we provide guidelines for the selection in DCNs. Our simulation results show that E-ECN achieves shorter average queue delay and controllable maximum queue length in general with a bandwidth utilization guarantee. E-ECN greatly reduces queue overflow and improves the robustness of DCNs.

The quantum network is designed to connect numerous quantum nodes and support various ground-breaking quantum applications. Most of these applications require communicating parties to share entangled pairs. Therefore, entanglement routing, a technology distributing entangled pairs between distant quantum nodes, plays a vital role in realizing quantum networks’ capability. However, due to the limitation of quantum memory size and quantum decoherence, the entangled pairs shared by adjacent quantum nodes can hardly satisfy concurrent entanglement routing requests, thus leading to severe network congestion. In this paper, we propose a novel congestion mitigation (CM) scheme to tackle such bottleneck problems. The basic idea of CM is to “recycle” idle link-level entanglement resources from well-resourced links to bottleneck links utilizing a unique enabling technology of quantum networks, called entanglement swapping. CM can increase the capacity of each bottleneck link, thus overcoming resource limitations to improve resource utilization and network throughput. To complete our work, we also propose a swapping-based entanglement routing design, including path selection and resource allocation algorithms. Extensive simulations show that our design can significantly alleviate network congestion and improve the request service rate of quantum networks compared to the traditional entanglement routing designs.

Data Center Networks (DCNs) suffer from synchronized bursts for network topology and parallel applications, leading to buffer overflows at switches and increasing network delay. To overcome this problem, some congestion control algorithms like DCTCP use Explicit Congestion Notification (ECN) to notify in-network congestion and reduce switch buffer occupancy. However, the traditional Additive Increase Multiplicative Decrease (AIMD) method causes high fluctuation of round-trip time (RTT) in DCNs. Some intelligent congestion control algorithms designed for Internet can achieve great flexibility, but are not applicable in DCNs for a lack of accurate congestion feedback. In this paper, we analyze the deficiencies of utilizing RTT as congestion signals and the applicability of learning algorithms in DCNs. Then, we propose DECC, a smart TCP congestion control algorithm for DCNs, which combines Deep Reinforcement Learning (DRL) with ECN to achieve high bandwidth utilization as well as low queuing delay. DECC fully utilizes precise in-network feedback and formulates several QoS requirements to a multi-objective function. Meanwhile, it decouples cwnd adjustment with DRL decision making to gradually learn the optimal congestion control policy in real-time. We evaluate the performance of DECC in various scenarios. Simulation results show that DECC can reduce the queue length at bottleneck switches by more than 50% compared to DCTCP, while maintaining high bandwidth utilization and reducing Flow Completion Time (FCTs) under burst traffic.

The upgrade of network devices to be equipped with multiple network interfaces makes it possible to improve network throughput performance through multipath transmission protocols, especially multipath TCP (MPTCP). However, so far the mostly used MPTCP protocols have a common limitation, namely the rigid and conservative method. They have been designed with little consideration of the fact that real networks are dynamic and the network status changes frequently, thus leading to the poor performance of current MPTCP in many realistic scenarios. In this paper, we propose a lightweight multipath congestion control algorithm based on online learning, named MP-OL. MP-OL models congestion control as a multi-armed bandit problem, and adjusts the sending rate of each subflow flexibly and adaptively through online learning. Therefore, MP-OL possesses the capability of suiting various network scenarios, and can achieve fairness and high performance in dynamic network environment. It can also flexibly switch between online learning and traditional method, which reduces the computational complexity while ensuring the learning efficiency, thus making MP-OL easy to deploy and use. As the experimental results demonstrated, compared with the leading MPTCP variants, MP-OL achieves significant improvements in fairness and link utilization, and shows better resilience to non-congestion loss and better adaptability to unstable network conditions. In real networks, MP-OL also obtains better throughput performance.

A QUIC (Quick UDP Internet Connections) protocol is designed to improve Hypertext Transfer Protocol (HTTP) traffic and carries a non-negligible portion of the traffic in the current Internet. As its extension, Multipath QUIC (MPQUIC) provides higher bandwidth and smoother network handover by using multiple network interfaces simultaneously. However, to improve HTTP traffic, there are still some issues not yet carefully addressed in the existing MPQUIC, and packet scheduling is a vital one among the issues. Specifically, existing methods fail to respond to the stream prioritization of HTTP Version 2 (HTTP/2), leading to unsatisfying web page load performance. Besides, managing asymmetric and dynamic network paths is also a challenging issue, which may result in Head-of-Line (HoL) blocking and excessive buffer usage if not effectively handled. In this paper, we present a stream-aware per-packet scheduler, HoL Blocking Eliminating Scheduler (HBES), to improve the performance of MPQUIC in mobile networks. Firstly, HBES provides a fair allocation of aggregated bandwidth for different streams based on their priority. Then, it keeps stream data arriving at the receiver in order by estimating packet arrival time to mitigate HoL blocking and excessive buffer usage. We implement HBES and evaluate its performance in various network scenarios. Experimental results verify the superiority of HBES in reducing stream completion time and buffer occupation over those existing MPQUIC schedulers.

As a promising complement to terrestrial cellular networks, such as 5G/6G, satellite networks have recently drawn increasing attention. However, facing the challenges of the rapidly increasing users' demand for multimedia content, how to achieve efficient data delivery in a dynamic environment becomes a critical but knotty problem. To provide an efficient solution from the routing perspective, in this paper, we consider the Information-Centric Networking (ICN) architecture and propose a content-aware routing scheme. The basic idea of the proposed routing scheme is to leverage the cached content on cache-enabled satellites and find the optimal route solution with maximum net-gains, i.e., how much delay is reduced. Considering the limitation of periodical signaling collection in satellite networks, we also design a cached content prediction model, which can infer the probability that a certain content could be cached according to the content's historical popularity information, to provide necessary information to measure net-gains. Extensive simulation results show that the proposed content-aware routing scheme outperforms the traditional routing scheme with a 20% reduction in terms of content retrieval delay and traffic consumption.

Software-Defined Networking (SDN) is a new and highly flexible network architecture, but the bottleneck between the control plane and the data plane makes it vulnerable to the control plane saturation DoS attacks. When the attack happens, traditional schemes in DoS scrubbing agent use a binary classification and a First In First Out (FIFO) queue to filter attack flows. However, this scheme is inimical to the end-to-end latency of benign traffic. To tackle this issue, we propose LLDM, leveraging a dynamic priority scheme and a priority queue to detect, mitigate the attacks while ensuring low latency for benign traffic. After detecting the attack, LLDM leverages a two-phase scheme for mitigation. First, LLDM marks packets from the ports under attack as suspicious and migrates them to the mitigation agent. Then, the dynamic priority manager assigns each packet a priority corresponding to its legality, which is used in the priority queue for DoS scrubbing. We evaluate LLDM in a simulation SDN environment. The experimental results show that LLDM can reduce 90.4% of the queuing delay compared with the traditional scheme under a 5000 Packets Per Second (PPS) attack, and it is also resistant to more sophisticated attacks. Under the high rate attack of 50000 PPS, LLDM installs a flow rule for legitimate traffic in 0.2 seconds. Moreover, for benign HTTP requests, LLDM can keep the request time at 1.39 seconds.

In Software-Defined Networking (SDN), the controllers implement flexible and scalability networking policies by installing different flow rules. Each rule matches a specific class of flows, instructs the switches to execute actions, and then expires when they finish their tasks. OpenFlow introduces the timeout mechanism to manage these flow rules. However, finding a reasonable timeout value becomes a difficult problem for the network managers. When a relatively small timeout value is given to an elephant flow, the rule expires early, introducing extra cost for the controller and long latency for the matching flow, respectively. On the contrary, a large timeout value for a mice flow makes a rule occupy the switch memory too long, wasting the caching memory and causing the flow table prone to overflow. Therefore, it is necessary to allocate appropriate timeouts for different flows dynamically. In this paper, we achieve this goal with real-time traffic monitoring and heuristic algorithms. By considering different network loads and designing corresponding dynamic timeout algorithms for different scenarios, we make full use of the advantages of SDN to improve the utilization rate of the switch memory and save the controller resources. Further, we implement our scheme in a simulation SDN platform and evaluate the algorithms with the public datasets. Experiments show that our scheme has low control overhead and is memory efficient compared with current mechanisms.

博弈是宇宙中的一种普遍存在。本文从人类对博弈的认识过程出发, 探讨了博弈的存在与实践, 阐述了多智能体博弈研究难点, 并基于演化思想, 从系统论的角度出发, 提出多智能体演化博弈理论框架。以下一代预警探测系统为例, 介绍了多智能体演化博弈的应用实践。构建了多智能体自组织博弈决策模型和多智能体强化学习方法, 对研究高维复杂环境下的组织化、体系化博弈行为具有重要意义。

Remote entanglement distribution plays a crucial role in large-scale quantum networks, and the key enabler for entanglement distribution is quantum routers (or repeaters) that can extend the entanglement transmission distance. However, the performance of quantum routers is far from perfect yet. Amongst the causes, the limited quantum memories in quantum routers largely affect the rate and efficiency of entanglement distribution. To overcome this challenge, this paper presents a new modeling for the maximization of entanglement distribution rate (EDR) on a memory-limited path, which is then transformed into entanglement generation and swapping sub-problems. We propose a greedy algorithm for short-distance entanglement generation so that the quantum memories can be efficiently used. As for the entanglement swapping sub-problem, we model it using an Entanglement Graph (EG), whose solution is yet found to be at least NP-complete. In light of it, we propose a heuristic algorithm by dividing the original EG into several sub-problems, each of which can be solved using dynamic programming (DP) in polynomial time. By conducting simulations, the results show that our proposed scheme can achieve a high EDR, and the developed algorithm has a polynomial-time upper bound and reasonable average runtime complexity.

Entanglement routing establishes remote entanglement connection between two arbitrary nodes, which is one of the most important functions in quantum networks. The existing routing mechanisms mainly improve the robustness and throughput facing the failure of entanglement generations, which, however, rarely include the considerations on the most important metric to evaluate the quality of connection, entanglement fidelity. To solve this problem, we propose purification-enabled entanglement routing designs to provide fidelity guarantee for multiple Source-Destination (S-D) pairs in quantum networks. In our proposal, we first consider the single S-D pair scenario and design an iterative routing algorithm, Q-PATH, to find the optimal purification decisions along the routing path with minimum entangled pair cost. Further, a low-complexity routing algorithm using an extended Dijkstra algorithm, Q-LEAP, is designed to reduce the computational complexity by using a simple but effective purification decision method. Finally, we consider the common scenario with multiple S-D pairs and design a greedy-based algorithm considering resource allocation and re-routing process for multiple routing requests. Simulation results show that the proposed algorithms not only can provide fidelity-guaranteed routing solutions, but also has superior performance in terms of throughput, fidelity of end-to-end entanglement connection, and resource utilization ratio, compared with the existing routing scheme.

As a new architecture of Internet infrastructure, Information-Centric Networking (ICN) is mainly designed to effectively handle the rapidly increasing user demand for content delivery through in-network caching. While facilitating the dissemination of content to users and making better use of the network resources, ICN is also vulnerable in that attackers can inject poisoned content into the network and isolate users from valid content sources. The introduction of signature verification in each router can effectively prevent this attack, but it also introduces great computation overhead. Existing schemes in ICN reduce verification overhead from a single routing perspective but do not consider integrating resources within ICN for collaborative content authentication and cyber self-defense. In this paper, we propose a collaborative, secure, and efficient content validation protection framework, named CSEVP, to implement a multi-router collaborative defense mechanism for ICN. On the one hand, we conduct content verification by probabilistically choosing one router involved in the transmission path to offload the computation overhead of content verification from a single router to multiple ones. On the other hand, we adopt bloom filters for routers to record and share verification results to further facilitate a more efficient content validity verification. The security and efficiency analysis shows that our proposed CSEVP can achieve efficient content validity verification among multiple routers with acceptable low communication and storage overhead.

As a promising implementation of Information-Centric Networking (ICN), Named Data Networking (NDN) has potential advantages over the TCP/IP network in content distribution, mobility support, etc. However, the research on NDN is still in its infancy, and congestion control, NDN’s most important functional element, poses many challenges, such as congestion detection, excessive window reduction for non-congested paths, and unfairness. In this paper, we propose an Intelligent Edge-Aided Congestion Control (IEACC) scheme for the NDN network based on Deep Reinforcement Learning (DRL). The proposed IEACC provides a proactive congestion detector that utilizes intermediate routers to transmit accurate congestion information along the path to consumers through data packets. Furthermore, considering the multi-source transmission in NDN, IEACC divides data packets into different congestion degrees by a lightweight clustering algorithm and provides suitable inputs for DRL, thereby obtaining a reasonable transmission rate. Then, it distributes the estimated bandwidth resources to consumers with transmission needs to maintain fairness. Finally, we implement our proposed scheme in the simulation platform and evaluate the performance in different scenarios. The results show that it can improve data transmission rate, reduce packet loss, and maintain fairness compared with others.

Service prioritization brings reasonable allocation of network resources and improves the overall quality of experience (QoE) of users, but it has not been thoroughly investigated in information centric networking (ICN). Existing works lack adaptability and they cannot ensure specific content provider (CP) get well caching service which is one of the most important functions in ICN. In this paper, we firstly propose a service prioritization scheme to flexibly provide different caching services for heterogeneous CPs to improve the overall network efficiency. The main idea is to allocate dedicated cache space for paying CPs and provide prioritized caching service for them, while normal CPs only enjoy the normal caching service. The scheme can be divided into two phases. First, we select a group of nodes with higher importance as core nodes based on network topology, and pair each edge node to a core node following the two-sided many-to-one matching algorithm. Second, we dynamically allocate and manage the dedicated cache space for core nodes. We model the allocation of dedicated cache space and convert it into a convex optimization problem to solve. After that, a practical caching strategy and system design are implemented in the ndnSIM simulator. Finally, we evaluate our scheme and conduct comparative experiments with the most representative work diff-caching, simulation results show that our scheme outperform it in terms of both delay and cache hit ratio.