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  • 1
    Online Resource
    Online Resource
    Internet Data Center IP Identification and Connection Relationship Analysis Based on Traffic Connection Behavior Analysis
    Institute of Electronics, Information and Communications Engineers (IEICE) ; 2017
    In:  IEICE Transactions on Communications Vol. E100.B, No. 4 ( 2017), p. 510-517
    Details
    In: IEICE Transactions on Communications, Institute of Electronics, Information and Communications Engineers (IEICE), Vol. E100.B, No. 4 ( 2017), p. 510-517
    Type of Medium: Online Resource
    ISSN: 0916-8516 , 1745-1345
    URL: Article
    DOI: 10.1587/transcom.2016EBP3273
    Language: English
    Publisher: Institute of Electronics, Information and Communications Engineers (IEICE)
    Publication Date: 2017
    detail.hit.zdb_id: 2214509-6
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  • 2
    Online Resource
    Online Resource
    Graph analysis of network flow connectivity behaviors
    The Scientific and Technological Research Council of Turkey (TUBITAK-ULAKBIM) - DIGITAL COMMONS JOURNALS ; 2019
    In:  TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES
    Details
    In: TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, The Scientific and Technological Research Council of Turkey (TUBITAK-ULAKBIM) - DIGITAL COMMONS JOURNALS
    Abstract: Graph-based approaches have been widely employed to facilitate in analyzing network flow connectivity behaviors, which aim to understand the impacts and patterns of network events. However, existing approaches suffer from lack of connectivity-behavior information and loss of network event identification. In this paper, we propose network flow connectivity graphs (NFCGs) to capture network flow behavior for modeling social behaviors from network entities. Given a set of flows, edges of a NFCG are generated by connecting pairwise hosts who communicate with each other. To preserve more information about network flows, we also embed node-ranking values and edge-weight vectors into the original NFCG. After that, a network flow connectivity behavior analysis framework is present based on NFCGs. The proposed framework consists of three modules: a graph simplification module based on diversified filtering rules, a graph feature analysis module based on quantitative or semiquantitative analysis, and a graph structure analysis module based on several graph mining methods. Furthermore, we evaluate our NFCG-based framework by using real network traffic data. The results show that NFCGs and the proposed framework can not only achieve good performance on network behavior analysis but also exhibit excellent scalability for further algorithmic implementations.
    Type of Medium: Online Resource
    ISSN: 1303-6203
    URL: Journal
    URL: Article
    DOI: 10.3906/electrical
    DOI: 10.3906/elk-1808-148
    Language: English
    Publisher: The Scientific and Technological Research Council of Turkey (TUBITAK-ULAKBIM) - DIGITAL COMMONS JOURNALS
    Publication Date: 2019
    detail.hit.zdb_id: 2046473-3
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  • 3
    Online Resource
    Online Resource
    Sparse representation for heterogeneous information networks
    Elsevier BV ; 2023
    In:  Neurocomputing Vol. 525 ( 2023-03), p. 111-122
    Details
    In: Neurocomputing, Elsevier BV, Vol. 525 ( 2023-03), p. 111-122
    Type of Medium: Online Resource
    ISSN: 0925-2312
    URL: Article
    DOI: 10.1016/j.neucom.2023.01.035
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 1012660-0
    detail.hit.zdb_id: 1479006-3
    detail.hit.zdb_id: 1055250-9
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  • 4
    Online Resource
    Online Resource
    Two new catechins from Zijuan green tea enhance the fitness and lifespan of Caenorhabditis elegans via insulin-like signaling pathways
    Royal Society of Chemistry (RSC) ; 2022
    In:  Food & Function Vol. 13, No. 18 ( 2022), p. 9299-9310
    Details
    In: Food & Function, Royal Society of Chemistry (RSC), Vol. 13, No. 18 ( 2022), p. 9299-9310
    Abstract: Green tea polyphenols show positive effects on human health and longevity. However, knowledge of the antiaging properties of green tea is limited to the major catechin epigallocatechin gallate (EGCG). The search for new ingredients in tea with strong antiaging activity deserves further study. Here we isolated and identified two new catechins from Zijuan green tea, named zijuanin E (1) and zijuanin F (2). Their structures were identified by extensive high-resolution mass spectroscopy (HR-MS), nuclear magnetic resonance (NMR), ultraviolet–vis (UV), infrared (IR) and circular dichroism (CD) spectroscopic analyses, and their 13 C NMR and CD data were calculated. We used the nematode Caenorhabditis elegans ( C. elegans ) to analyze the health benefits and longevity effects of 1 and 2. Compounds 1 and 2 (100 μM) remarkably prolonged the lifespan of C. elegans by 67.2% and 56.0%, respectively, delaying the age-related decline of phenotypes, enhancing stress resistance, and reducing ROS and lipid accumulation. Furthermore, 1 and 2 did not affect the lifespan of daf-16 , daf-2 , sir-2.1 , and skn-1 mutant worms, suggesting that they might work via the insulin/IGF and SKN-1/Nrf2 signaling pathways. Meanwhile, 1 and 2 also exhibited strong antioxidant activity in vitro . Surface plasmon resonance (SPR) evidence suggests that zijuanins E and F have strong human serum albumin (HSA) binding ability. Together, zijuanins E and F represent a new valuable class of tea components that promote healthspan and could be developed as potential dietary therapies against aging.
    Type of Medium: Online Resource
    ISSN: 2042-6496 , 2042-650X
    URL: Article
    DOI: 10.1039/D2FO01795D
    Language: English
    Publisher: Royal Society of Chemistry (RSC)
    Publication Date: 2022
    detail.hit.zdb_id: 2578152-2
    SSG: 21
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  • 5
    Online Resource
    Online Resource
    Toward Network Worm Victims Identification Based on Cascading Motif Discovery
    MDPI AG ; 2019
    In:  Electronics Vol. 8, No. 2 ( 2019-02-05), p. 183-
    Details
    In: Electronics, MDPI AG, Vol. 8, No. 2 ( 2019-02-05), p. 183-
    Abstract: Network worms spread widely over the global network within a short time, which are increasingly becoming one of the most potential threats to network security. However, the performance of traditional packet-oriented signature-based methods is questionable in the face of unknown worms, while anomaly-based approaches often exhibit high false positive rates. It is a common scenario that the life cycle of network worms consists of the same four stages, in which the target discovery phase and the transferring phase have specific interactive patterns. To this end, we propose Network Flow Connectivity Graph (NFCG) for identifying network worm victims. We model the flow-level interactions as graph and then identify sets of frequently occurring motifs related to network worms through Cascading Motif Discovery algorithm. In particular, a cascading motif is jointly extracted from graph target discovery phase and transferring phase. If a cascading motif exists in a connected behavior graph of one host, the host would be identified as a suspicious worm victim; the excess amount of suspicious network worm victims is used to reveal the outbreak of network worms. The simulated experiments show that our proposed method is effective and efficient in network worm victims’ identification and helpful for improving network security.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics8020183
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2662127-7
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  • 6
    Online Resource
    Online Resource
    Argus: Traffic Behavior Based Prediction of Internet User Demographics through Hierarchical Neural Network
    MDPI AG ; 2020
    In:  Electronics Vol. 9, No. 2 ( 2020-02-05), p. 271-
    Details
    In: Electronics, MDPI AG, Vol. 9, No. 2 ( 2020-02-05), p. 271-
    Abstract: Predicting internet user demographics based on traffic behavior analysis can provide effective clues for the decision making of network administrators. Nonetheless, most of the existing researches overly rely on hand-crafted features, and they also suffer from the shallowness of information mining and the limitation in prediction targets. This paper proposes Argus, a hierarchical neural network solution to the prediction of Internet user demographics through traffic analysis. Argus is a hierarchical neural-network structure composed of an autoencoder for embedding and a fully-connected net for prediction. In the embedding layer, the high-level features of the input data are learned, with a customized regularization method to enforce their discriminative power. In the classification layer, the embeddings are converted into the label predictions of the sample. An integrated loss function is provided to Argus for end-to-end learning and architecture control. Argus has exhibited promising performances in experiments based on real-world dataset, where most of the metrics outperform those achieved by common machine learning techniques on multiple prediction targets. Further experiments reveal that the integrated loss function is capable of promoting Argus performance, and the contribution of a specific loss component during the training process is validated. Empirical settings for hyper parameters are given according to the experiments.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics9020271
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2662127-7
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  • 7
    Online Resource
    Online Resource
    A Novel Method for Router-to-AS Mapping Based on Graph Community Discovery
    MDPI AG ; 2019
    In:  Information Vol. 10, No. 3 ( 2019-02-27), p. 87-
    Details
    In: Information, MDPI AG, Vol. 10, No. 3 ( 2019-02-27), p. 87-
    Abstract: The last decades have witnessed the progressive development of research on Internet topology at the router or autonomous systems (AS) level. Routers are essential components of ASes, which dominate their behaviors. It is important to identify the affiliation between routers and ASes because this contributes to a deeper understanding of the topology. However, the existing methods that assign a router to an AS, based on the origin AS of its IP addresses do not make full use of the information during the network interaction procedure. In this paper, we propose a novel method to assign routers to their owners’ AS, based on community discovery. First, we use the initial AS information along with router-pair similarities to construct a weighted router level graph; secondly, with the large amount of graph data (more than 2M nodes and 19M edges) from the CAIDA ITDK project, we propose a fast hierarchy clustering algorithm with time and space complexity, which are both linear for graph community discovery. Finally, router-to-AS mapping is completed, based on these AS communities. Experimental results show that the effectiveness and robustness of the proposed method. Combining with AS communities, our method could have the higher accuracy rate reaching to 82.62% for Routers-to-AS mapping, while the best accuracy of prior works is plateaued at 65.44%.
    Type of Medium: Online Resource
    ISSN: 2078-2489
    URL: Article
    DOI: 10.3390/info10030087
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2599790-7
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  • 8
    Online Resource
    Online Resource
    Critical node identification in network cascading failure based on load percolation
    American Institute of Mathematical Sciences (AIMS) ; 2023
    In:  Electronic Research Archive Vol. 31, No. 3 ( 2023), p. 1524-1542
    Details
    In: Electronic Research Archive, American Institute of Mathematical Sciences (AIMS), Vol. 31, No. 3 ( 2023), p. 1524-1542
    Abstract: 〈abstract〉 〈p〉Identification of network vulnerability is one of the important means of cyberspace operation, management and security. As a typical case of network vulnerability, network cascading failures are often found in infrastructure networks such as the power grid system, communication network and road traffic, where the failure of a few nodes may cause devastating disasters to the whole complex system. Therefore, it is very important to identify the critical nodes in the network cascading failure and understand the internal laws of cascading failure in complex systems so as to fully grasp the vulnerability of complex systems and develop a network management strategy. The existing models for cascading failure analysis mainly evaluate the criticality of nodes by quantifying their importance in the network structure. However, they ignore the important load, node capacity and other attributes in the cascading failure model. In order to address those limitations, this paper proposes a novel critical node identification method in the load network from the perspective of a network adversarial attack. On the basis of obtaining a relatively complete topology, first, the network attack can be modeled as a cascading failure problem for the load network. Then, the concept of load percolation is proposed according to the percolation theory, which is used to construct the load percolation model in the cascading failure problem. After that, the identification method of critical nodes is developed based on the load percolation, which accurately identifies the vulnerable nodes. The experimental results show that the load percolation parameter can discover the affected nodes more accurately, and the final effect is better than those of the existing methods.〈/p〉 〈/abstract〉
    Type of Medium: Online Resource
    ISSN: 2688-1594
    URL: Article
    DOI: 10.3934/era.2023077
    Language: Unknown
    Publisher: American Institute of Mathematical Sciences (AIMS)
    Publication Date: 2023
    detail.hit.zdb_id: 3147960-1
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  • 9
    Online Resource
    Online Resource
    A recyclable biomass electrolyte towards green zinc-ion batteries
    Springer Science and Business Media LLC ; 2023
    In:  Nature Communications Vol. 14, No. 1 ( 2023-07-22)
    Details
    In: Nature Communications, Springer Science and Business Media LLC, Vol. 14, No. 1 ( 2023-07-22)
    Abstract: The operation of traditional aqueous-electrolyte zinc-ion batteries is adversely affected by the uncontrollable growth of zinc dendrites and the occurrence of side reactions. These problems can be avoided by the development of functional hydrogel electrolytes as replacements for aqueous electrolytes. However, the mechanism by which most hydrogel electrolytes inhibit the growth of zinc dendrites on a zinc anode has not been investigated in detail, and there is a lack of a large-scale recovery method for mainstream hydrogel electrolytes. In this paper, we describe the development of a recyclable and biodegradable hydrogel electrolyte based on natural biomaterials, namely chitosan and polyaspartic acid. The distinctive adsorptivity and inducibility of chitosan and polyaspartic acid in the hydrogel electrolyte triggers a double coupling network and an associated synergistic inhibition mechanism, thereby effectively inhibiting the side reactions on the zinc anode. In addition, this hydrogel electrolyte played a crucial role in an aqueous acid-based Zinc/MnO 2 battery, by maintaining its interior two-electron redox reaction and inhibiting the formation of zinc dendrites. Furthermore, the sustainable biomass-based hydrogel electrolyte is biodegradable, and could be recovered from the Zinc/MnO 2 battery for subsequent recycling.
    Type of Medium: Online Resource
    ISSN: 2041-1723
    URL: Article
    DOI: 10.1038/s41467-023-40178-0
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2553671-0
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  • 10
    Online Resource
    Online Resource
    ITDPM: An Internet Topology Dynamic Propagation Model Based on Generative Adversarial Learning
    Hindawi Limited ; 2021
    In:  Scientific Programming Vol. 2021 ( 2021-5-29), p. 1-9
    Details
    In: Scientific Programming, Hindawi Limited, Vol. 2021 ( 2021-5-29), p. 1-9
    Abstract: Network information propagation analysis is gaining a more important role in network vulnerability analysis domain for preventing potential risks and threats. Identifying the influential source nodes is one of the most important problems to analyze information propagation. Traditional methods mainly focus on extracting nodes that have high degrees or local clustering coefficients. However, these nodes are not necessarily the high influential nodes in many real-world complex networks. Therefore, we propose a novel method for detecting high influential nodes based on Internet Topology Dynamic Propagation Model (ITDPM). The model consists of two processing stages: the generator and the discriminator like the generative adversarial networks (GANs). The generator stage generates the optimal source-driven nodes based on the improved network control theory and node importance characteristics, while the discriminator stage trains the information propagation process and feeds back the outputs to the generator for performing iterative optimization. Based on the generative adversarial learning, the optimal source-driven nodes are then updated in each step via network information dynamic propagation. We apply our method to random-generated complex network data and real network data; the experimental results show that our model has notable performance on identifying the most influential nodes during network operation.
    Type of Medium: Online Resource
    ISSN: 1875-919X , 1058-9244
    URL: Article
    DOI: 10.1155/2021/2390466
    RVK:
    SA 7860
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2070004-0
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