Accurate Recovery of Internet Traffic Data Under Variable Rate Measurements

Xie, Kun; Peng, Can; Wang, Xin; Xie, Gaogang; Wen, Junhai; Cao, Jiannong; Zhang, Dafang; Qin, Zheng

doi:10.1109/tnet.2018.2819504

Cited by 39 publications

(7 citation statements)

References 39 publications

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“…Let S be denoted as S = {S t } T t=1 , where S t is shown in Eq. (11). The steps of the pruning strategy are as follows:…”

Section: Pseudo-anomaly Pruning Strategymentioning

confidence: 99%

“…Anomaly detection involves finding values that are inconsistent with most of the values in a dataset, and it has many important applications, such as fraud detection for mobile devices or credit cards [1], intrusion detection for network services [2]- [4], and fraud detection in online social media and wireless sensor networks [5]- [11]. Although anomalous behaviors in these cases are rare, they can provide more important information than other behaviors.…”

Section: Introductionmentioning

confidence: 99%

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Diversity Aware-Based Sequential Ensemble Learning for Robust Anomaly Detection

Chen

2020

IEEE Access

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Anomaly detection has become a popular topic in many domains because anomalies can provide valuable information. Recently, ensemble learning has been applied to improve the generalization ability of existing anomaly detection methods. In an anomaly ensemble framework, diversity is essential for building a powerful ensemble method to obtain impressive performance. However, most studies use heuristic techniques to improve the diversity of ensembles, and it generally leads to limited diversity. To obtain improved diversity, we propose a diversity aware-based sequential ensemble method (called D-SEM) for anomaly detection. Specifically, our proposed method divides the ensemble diversity into two parts: sample diversity and model diversity. For sample diversity, we introduce the subsampling technique to implement preliminary generation of diverse datasets for training. For model diversity, we design an ensemble-based optimization model to learn base classifiers with improved diversity. Furthermore, an unsupervised diversity measure is proposed to quantitatively assess diversity and an anomaly pruning strategy is utilized to successively eliminate pseudo-anomalies. Based on the inclusion of sample diversity and model diversity, the proposed D-SEM method obtains better generalization ability for anomaly detection. The experimental results based on real-world datasets suggest that the proposed method has superior performance compared with various state-of-the-art methods.

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“…Let S be denoted as S = {S t } T t=1 , where S t is shown in Eq. (11). The steps of the pruning strategy are as follows:…”

Section: Pseudo-anomaly Pruning Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diversity Aware-Based Sequential Ensemble Learning for Robust Anomaly Detection

Chen

2020

IEEE Access

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show abstract

“…Note that traffic matrix interpolation is a related, but different problem, with different properties. Xie et al [52], [51], [50] exploit hidden spatial and temporal structures with three-dimensional low-rank tensors, which effectively reduces the estimation error. Zhang et al [56] interpolate incomplete traffic matrices with structure regularized low-rank matrix factorization and local interpolation procedures.…”

Section: Related Workmentioning

confidence: 99%

A Skewness-Aware Matrix Factorization Approach for Mesh-Structured Cloud Services

Barlet‐Ros³

et al. 2019

IEEE/ACM Trans. Networking

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Online cloud services need to fulfill clients' requests scalably and fast, otherwise, users' experience and providers' revenue could be severely degraded. State-of-the-art cloud services are increasingly deployed as a distributed service mesh. Service to service communication is frequent in the mesh. Unfortunately, problematic events may occur between any pair of nodes in the mesh, therefore, it is vital to maximize the network visibility for efficient network troubleshooting and application optimization. A state-of-the-art approach is to model pairwise RTTs based on a latent factor model represented as a low-rank matrix factorization. A latent factor corresponds to a rank-1 component in the factorization model, and is shared by all node pairs. However, different node pairs usually experience a skewed set of hidden factors like divergent routing paths, dynamic path conditions, or transient network congestions, which cannot be captured by existing latent factor models. In this paper, we propose a skewness-aware matrix factorization method named SMF. We decompose the matrix factorization into the basic units of rank-one latent factors, and progressively combine rank-one factors for different node pairs. We present a unifying framework to automatically and adaptively select the rank-one factors for each node pair, which not only preserves the low rankness of the matrix model, but also adapts to skewed network latency distributions. Over real-world RTT data sets, SMF significantly improves the relative error by a factor of 0.2x to 10x, converges fast and stably, and compactly captures fine-grained local and global network latency structures.

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“…There are multiple destinations for multicast. It has an extensive and applicable future, and plays an important role in software updates and data monitoring [23][24][25]. The bandwidth consumption of the backbone network is reduced by more than 50% with multicast [26].…”

Section: Introductionmentioning

confidence: 99%

Multi-Source Reliable Multicast Routing with QoS Constraints of NFV in Edge Computing

Xie

Zhou

et al. 2019

Electronics

Self Cite

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Edge Computing (EC) allows processing to take place near the user, hence ensuring scalability and low latency. Network Function Virtualization (NFV) provides the significant convenience of network layout and reduces the service operation cost in EC and data center. Nowadays, the interests of the NFV layout focus on one-to-one communication, which is costly when applied to multicast or group services directly. Furthermore, many artificial intelligence applications and services of cloud and EC are generally communicated through groups and have special Quality of Service (QoS) and reliable requirements. Therefore, we are devoted to the problem of reliable Virtual Network Function (VNF) layout with various deployment costs in multi-source multicast. To guarantee QoS, we take into account the bandwidth, latency, and reliability constraints. Additionally, a heuristic algorithm, named Multi-Source Reliable Multicast Tree Construction (RMTC), is proposed. The algorithm aims to find a common link to place the Service Function Chain (SFC) in the multilevel overlay directed (MOD) network of the original network, so that the deployed SFC can be shared by all users, thereby improving the resource utilization. We then constructed a Steiner tree to find the reliable multicast tree. Two real topologies are used to evaluate the performance of the proposed algorithm. Simulation results indicate that, compared to other heuristic algorithms, our scheme effectively reduces the cost of reliable services and satisfies the QoS requirements.

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Accurate Recovery of Internet Traffic Data Under Variable Rate Measurements

Cited by 39 publications

References 39 publications

Diversity Aware-Based Sequential Ensemble Learning for Robust Anomaly Detection

Diversity Aware-Based Sequential Ensemble Learning for Robust Anomaly Detection

A Skewness-Aware Matrix Factorization Approach for Mesh-Structured Cloud Services

Multi-Source Reliable Multicast Routing with QoS Constraints of NFV in Edge Computing

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