Robust signal enhancement under non-stationary and low SNR conditions remains challenging, as methods based on the short-time Fourier transform (STFT) with fixed resolution struggle to represent complex and time–frequency structures. While leveraging the fractional domain as an auxiliary view offers flexibility in modeling time-frequency structures, existing methods typically adopt fixed transform orders and overlook alignment between views, hindering effective integration of complementary representations and leaving frequency domain misalignment unresolved. Therefore, we propose FracFusion, a novel framework that integrates a learnable short-time fractional Fourier Transform (STFrFT) module to generate dynamic auxiliary views, combined with two stage alignment-aware fusion modules: Pearson Channel Fusion for correlation-guided consistency and Efficient Align Fusion for fine-grained, frequency aligned interaction. Experiments on speech and electromagnetic (EM) datasets show that FracFusion consistently outperforms state-of-the-art baselines across diverse noise levels and signal types, demonstrating robust adaptability across domains.

The rapid growth of the Internet of Things (IoT) has introduced significant security challenges, particularly in detecting intrusions within complex IoT networks. This paper presents TS-Net, a robust dual-channel model that combines temporal and spatial feature learning to enhance IoT intrusion detection. By partitioning network traffic into temporal and spatial features, TS-Net processes them through separate channels. The temporal channel utilizes Bidirectional Gated Recurrent Units (BiGRU) paired with a self-attention mechanism to capture dynamic sequential dependencies, while the spatial channel employs multi-scale dilated convolutions to extract patterns from varying spatial perspectives. These two channels are then fused to improve the model accuracy in detecting anomalous traffic. Experimental results on three publicly available datasets demonstrate that TS-Net outperforms existing intrusion detection models, achieving higher precision, recall, and F1-scores, demonstrating its effectiveness in addressing the unique security needs of IoT networks.

Deep learning-based speech enhancement (SE) methods focus on reconstructing speech from the time or frequency domain. However, these domains cannot provide enough information to capture the dynamics of non-stationary signals accurately. To enrich information, this work proposes a multi-view fusion SE method (MFSE). Specifically, MFSE extends the representation space of speech to the dynamic domain (also called fractional domain) between the time and frequency domains by using the short-time fractional Fourier transform (STFrFT). Subsequently, we construct inputs as modes of the primary short-time Fourier transform (STFT) spectrum and the auxiliary STFrFT spectrum views and adaptively identify the optimal fractional STFrFT spectrum from the infinitely continuous fractional domain by leveraging the average spectral centroids. The framework extracts potential features through multiple designed convolutional modules and captures the correlation between different speech frequencies through multi-granularity attention. Experimental results show that the proposed method significantly improves performance in several metrics compared to existing single-channel SE methods based on time and frequency domains. Furthermore, the results of its generalizability evaluation show that the multi-view method outperforms the single-view method under a wide range of SNR conditions.

The complexity of routing requirements leads to increasingly intricate routing metrics. Existing routing algebra theories have demonstrated that convergent and optimal routing algorithms can be designed only when path metrics satisfy certain properties such as monotonicity and isotonicity. Furthermore, some non-isotonic metrics can be converted into isotonic forms on partial orders through reduction. However, practical scenarios often involve non-commutative algebraic properties, which are overlooked by existing theories. For these problems, there lacks a unified framework to study their solvability, a systematic method for their reduction, and an efficient algorithm to compute optimal routes. In this work, we extend routing algebra to accommodate non-commutative routing problems, propose general reduction methods for them, and explore their solvability. In addition, we design a link state algorithm that converge fast on a reduced partial order. All these discussions are supported by concrete examples, theoretical proofs, and simulations on various network topologies.

Smart home IoT devices have always been the target of various cyber attacks. By leveraging the smart home monitoring infrastructure, event-based anomaly detection is effective to detect anomalies that cause unfavorable working state of IoT devices. However, IoT events are proven to be vulnerable to event-targeted attacks which could be achieved by exploiting the vulnerabilities embedded in IoT devices, protocols and/or platforms. Thus, existing event-based anomaly detection is not robust in the case of unreliable input. To address this issue, our insight is that the embedded microphone components in many off-the-shelf home devices (e.g., smart doorbells, speakers, cameras, tablets, laptops, etc.) could be utilized to gather acoustic information to help increase the reliability and capability of smart home security monitoring systems. To verify this idea, we propose an audio-assisted framework IoTAudMon for detecting event-targeted attacks. Considering the heterogeneity and sparsity nature of smart homes IoT devices and events, we employ transfer learning to design a practical pipeline for extracting semantic information from audio, eliminating the requirement of human labeling and mitigating the cold start issue in existing solutions. Experiments on public datasets and real devices demonstrate the effectiveness of IoTAudMon.

Decentralized Exchanges (DEX) allow cryptocurrencies to trade autonomously with each other without involving any centralized financial intermediaries. Among these DEX models, Automated Market Maker (AMM) is most commonly used by major platforms like Uniswap and Curve. However, a typical AMM suffers three main challenges. First, arbitrage trading may cause AMM-based liquidity providers to lose liquidity in assets. Second, adversaries extract on a monthly basis over 10 million USD from AMM traders via sandwich attacks. Third, the volatility of asset prices in AMM may violate the fairness of trading. In this work, we propose a new AMM design, Dynamic Curve-based Automated Market Maker (DCAMM), which utilizes a price oracle with real-time market price feedback to automatically adjust the pool's asset price to match the market price. In DCAMM, there is no space for price manipulation, and traders' slippage losses are converted into equal gains for the liquidity pool. Thus, DCAMM provides the resistance to arbitrage trading and sandwich attacks. Moreover, DCAMM provides a more stable asset price through a strict price adjustment, benefiting traders and safeguarding trading fairness.

Inter-DataCenter Wide Area Network (Inter-DC WAN) that connects geographically distributed data centers is becoming one of the most critical network infrastructures. Due to limited bandwidth and inevitable link failures, it is highly challenging to guarantee network availability for services, especially those with stringent bandwidth demands, over inter-DC WAN. We present$\mathsf {TEDAT}$, a novel Traffic Engineering (TE) framework for Diverse Availability Targets (DAT), where a Service Level Agreement (SLA) is defined to ensure that each bandwidth demand must be satisfied with a stipulated probability, when subjected to the network capacity and possible failures of the inter-DC WAN.$\mathsf {TEDAT}$has two core components, i.e., traffic scheduling and failure recovery, which are crystalized through different mathematical models and theoretically analyzed. They are also extensively compared against state-of-the-art TE schemes, using a testbed as well as real trace driven simulations across different topologies, traffic matrices and failure scenarios. Our evaluations show that, compared with the optimal admission strategy,$\mathsf {TEDAT}$can speed up the online admission control by$30\times $at the expense of less than 4% false rejections. On the other hand, compared with the latest TE schemes like FFC and TEAVAR,$\mathsf {TEDAT}$can meet the bandwidth availability SLAs for 23%~60% more demands under normal loads, and when network failure causes SLA violations, it can retain 10%~20% more profit under a pricing and refunding model.

Segment Routing (SR) is a source routing paradigm that routes a packet through an ordered list of instructions called segments. It is widely used in Traffic Engineering (TE) because of its simplicity and scalability. Although there are lots of research about TE with SR (SR-TE), fewer consider network failures. The reactive approaches may suffer from latency and update issues, and the proactive approaches don't perform very well because the objectives aren't carefully designed. Besides, although different types of failures are considered, the failure probabilities are ignored. In this paper, we take failure probabilities in to consideration, and propose a proactive 2-SR model 2SRPF to handle SR-TE problem with network failures, aiming at minimizing maximum link utilization (MLU). Considering that severe failures are more noteworthy, we use probability as a severity threshold, and minimize the expectation of the larger MLUs whose corresponding failure states have probabilities sum to a specific threshold value. We solve it with probabilistic risk management. Experiments show that 2SRPF performs well with one threshold setting for different topologies consistently, and gets close to optimal results when network fails.

Segment Routing (SR) is a source routing paradigm which is widely used in Traffic Engineering (TE). By using SR, a node steers a packet through an ordered list of instructions called segments. By some extensions of interior gateway protocol, SR can be applied to IP/MPLS or IPv6 network without signal protocol. SR over IPv6 (SRv6) is attracting wide attention because of its interoperation ability with IPv6. However, upgrading the existing IPv6 network directly to a full SRv6 one can be difficult, because large-scale equipment replacement or software upgrade may cause economic and technical problems. TE in partially deployed SR network is becoming a hot research topic. In this paper, we propose the TE algorithm Weight Adjustment-SRTE (WA-SRTE) in partially deployed SRv6 network, in which SRv6 capable nodes are dispersedly deployed. Our objective is to minimize the network's maximum link utilization. WA-SRTE converts the TE problem into a Deep Reinforcement Learning problem and optimizes the OSPF weight, SRv6 node deployment and traffic paths simultaneously. Besides, traffic variation is also considered and we use a representative Traffic Matrix (TM) to epitomize the traffic characteristics over a period of time. Experiments demonstrate that with 20% to 40% of the SRv6 nodes deployed, we can achieve TE performance as good as in a full SR network for the experiment topologies. The results with WA remarkably outperform the results without it. Our algorithm also gets near-optimal results with changing traffic.

Data center has become an important facility for hosting various applications. For data center networks, deadline missing rate and average flow completion time are two main metrics for the performance of applications. In this paper, we find deadline-aware methods can only reduce the percentage of flows missing deadline, while flowsize-aware and information-cumulative methods can only optimize the average flow completion time. However, traffic in data center is the mixture of various flows and focusing on the single goal is not enough. We advocate to incorporate deadline and flow duration time into flow rate control. Then we design DA&FD (Deadline-Aware and Flow Duration) based rate control mechanism and analyze its performance in theory. At last, we evaluate DA&FD under different topologies, real world traffic and load scenarios, both by simulation and in real testbed. Our results show that DA&FD performs close to D2TCP and about 15%, 25%, 30%, 35% better than Ameon, L2DCT, Karuna, DCTCP on deadline missing rate. For average FCT, the performance of DA&FD is similar to L2DCT and compared with Ameon, D2TCP, Karuna, DCTCP, DA&FD can reduce average FCT by 10%, 15%, 20%, 25%.

Traditional network resource management mechanisms are mainly flow or packet based. Recently, coflow has been proposed as a new abstraction to capture the communication patterns in a rich set of data parallel applications in data centers. Coflows effectively model the application-level semantics of network resource usage, so high-level optimization goals, such as reducing the transfer latency of applications, can be better achieved by taking coflows as the basic elements in network resource allocation or scheduling. Although efficient coflow scheduling methods have been studied, in this paper, we advocate to schedule weighted coflows as a further step in this direction, where weights are used to express the importances or priorities of different coflows or their corresponding applications. We propose the Weighted Coflow Completion Time (WCCT) minimization problem and a (2-2/n+1)-approximate optimal offline algorithm, where n is the concurrent number of coflows. We then design an information-agnostic online algorithm named IAOA to dynamically schedule coflows according to their weights and the instantaneous network condition. We also design and implement a coflow scheduling system named FlyTransfer, which can use the online algorithm as its scheduling method. We test the performance of FlyTransfer by trace-driven simulations as well as real deployment in openstack. Our evaluation results show that, compared to the latest information-agnostic coflow scheduling algorithms, FlyTransfer can reduce more than 40 percent of the WCCT, and more than 30 percent of the completion time for coflows with above-the-average level of importance. It even outperforms the most efficient clairvoyant coflow scheduling method by reducing around 30 percent WCCT, and 25- 30 percent of the completion time for coflows with above-the-average importance, respectively.

With the deployment of big data applications, more and more data are stored in the online storage. Erasure coding storage system has been widely used by companies such as Google and Facebook, since it provides space-optimal data redundancy to protect against data loss. In erasure coding storage system, (n, k) MDS erasure code is used to divide file into n chunks. When a user want to access the file, any subset of k out of n chunks will be needed to reconstruct the file. In this case, how to select k out of n chunks and how to let the chunks transfer quickly become important problems. In this paper, we joint the two problems together to optimize. Our optimization goal is to minimize average file access time (FAT). To achieve this, we propose smallest load first heuristic to do source selection and design an online algorithm to reduce chunks transfer latency. Base on this, we design and implement D-Target, a centralized scheduler that tries to minimize average FAT in distributed erasure coding storage system. We then test D-Target's performance by trace-driven simulation. Results show that, for the trace of AT&T, D-Target performs 2.5×, 1.7×, 1.8×, 3.6× better than TCP, Aalo, Barrat and pFabric respectively.

To enhance the network reliability without incurring significant extra overhead, we propose a novel link protection scheme, Hybrid Link Protection (HLP), to achieve failure resilient routing. Compared to previous schemes, HLP ensures high network availability in a more efficient way, and also provides other features such as load balancing. HLP is implemented in two stages. Stage one provides Multiple Next-hop Protection (MNP), where only one single Shortest Path Tree (SPT) needs to be constructed on each node to find multiple next hops for any destination. Stage two provides Backup Path Protection (BPP), where only a minimum number of links need to be protected, using special paths and packet headers, to meet the network availability requirement. We evaluate these algorithms in a wide spread of relevant topologies, both real and synthetic, and the results reveal that HLP can achieve high network availability without introducing conspicuous overhead.

The diversity of QoS (Quality-of-Service) requirements of Internet applications motivates various QoS routing algorithms that take different QoS metrics into consideration. Routing algebra has been proposed as a framework to study the fundamental properties of QoS routing algorithms, such as their optimality and loop-freeness. However, for multipath QoS routing, little has been done in these aspects. Existing multipath QoS routing algorithms often take a rather conservative approach to guarantee loop-freeness, at the cost of efficiency. On the other hand, simply adapting existing efficient multipath routing algorithms to support various QoS metrics cannot guarantee correctness. In face of that, we propose a routing metric algebra for multipath QoS routing in link state networks, where a key property of the routing metrics called isotonicity, which plays an important role. To let routers efficiently and correctly find multiple next-hops for each destination, we also develop two distributed multipath QoS routing algorithms. The algorithms are run locally and independently, without exchanging messages other than the basic link states. They are specifically tailored for algebras with strict or non strict isotonicity, and their correctness are formally proved.

Current intra-domain routing protocols computes only shortest paths for any pair of nodes which cannot provide good fast reroute when network failures occur. Multipath routing can be fundamentally more efficient than the currently used single path routing protocols. It can significantly reduce congestion in network by shifting traffic to unused network resources. This improves network utilization and provides load balancing. To enhance failure resiliency we propose a new scheme More Nodes Have At Least Two Choices (MNTC) where the goal is how to maximize the number of nodes that have at least two next-hops towards their destinations. We evaluate the algorithm in a wide space of relevant topologies and the results show that it can achieve good reliability while keeping low stretch.

The Internet is playing an increasingly crucial role in both personal and business activities. Handling link failures is an important task in designing routing protocols. To enhance the network availability without incurring significant extra overhead, we propose a novel link protection algorithm, Hybrid Link Protection Scheme (HLP) to achieve failure resilient routing.

High reliability is always pursued by network designers. Multipath routing can provide multiple paths for transmission and failover, and is considered to be effective in the improvement of the network reliability. However, existing multipath routing algorithms focus on how to find as many paths as possible, rather than their computation or communication overhead. We propose a dynamic distributed multipath algorithm (DMPA) to help a router in a link-state network find multiple nexthops for each destination. A router runs the algorithm locally and independently, where only one single shortest path tree (SPT) needs to be constructed, and no message other than the basic link states is disseminated. DMPA maintains the SPT and dynamically adjusts it in response to network state changes, so the sets of nexthops can be incrementally and efficiently updated. At the same time, DMPA guarantees loop-freeness of the induced forwarding path by a partial order of the routers underpinning it. We evaluate DMPA and compare it with some latest multipath algorithms, using a set of real, inferred and synthetic topologies. The results show that DMPA can provide good reliability and fast recovery for the network with very low overhead.

High reliability is always pursued by network and protocol designers. Multipath routing can provide multiple paths for transmission and failover, and is considered to be effective in the improvement of network reliability. To compute multiple paths efficiently, we present MLSA, a tree based link-state multipath algorithm, to help a node to find multiple next hops for each destination. On each node, only a single tree needs to be maintained, locally and independently, while no other information than the basic link states need to be exchanged. We also prove the loop-freeness of MLSA, guaranteed by a underlying partial order established over the nodes. We evaluate MLSA with both real and synthetic topologies. The simulation results show that, MLSA can achieve comparable reliability as the naive algorithms based on shortest path trees, with much less computation overhead.