Applications of sketches in network traffic measurement: A survey

Accurate and timely network traffic measurement is essential for network status monitoring, network fault analysis, network intrusion detection, and network security management. With the rapid development of the network, massive network traffic brings severe challenges to network traffic measurement. However, existing measurement methods suffer from many limitations for effectively recording and accurately analyzing big-volume traffic. Recently, sketches, a family of probabilistic data structures that employ hashing technology for summarizing traffic data, have been widely used to solve these problems. However, current literature still lacks a thorough review on sketch-based traffic measurement methods to offer a comprehensive insight on how to apply sketches for fulfilling various traffic measurement tasks. In this paper, we provide a detailed and comprehensive review on the applications of sketches in network traffic measurement. To this end, we classify the network traffic measurement tasks into four categories based on the target of traffic measurement, namely cardinality estimation, flow size estimation, change anomaly detection, and persistent spreader identification. First, we briefly introduce these four types of traffic measurement tasks and discuss the advantages of applying sketches. Then, we propose a series of requirements with regard to the applications of sketches in network traffic measurement. After that, we perform a fine-grained classification for each sketch-based measurement category according to the technologies applied on sketches. During the review, we evaluate the performance, advantages and disadvantages of current sketch-based traffic measurement methods based on the proposed requirements. Through the thorough review, we gain a number of valuable implications that can guide us to choose and design proper traffic measurement methods based on sketches. We also review a number of general sketches that are highly expected in modern network systems to simultaneously perform multiple traffic measurement tasks and discuss their performance based on the proposed requirements. Finally, through our serious review, we summarize a number of open issues and identify several promising research directions.     

The International Society for the Study of Vascular Anomalies (ISSVA) ontology

The International Society for the Study of Vascular Anomalies (ISSVA) provides a classification for vascular anomalies that enables specialists to unambiguously classify diagnoses. This classification is only available in PDF format and is not machine-readable, nor does it provide unique identifiers that allow for structured registration. In this paper, we describe the process of transforming the ISSVA classification into an ontology. We also describe the structure of this ontology, as well as two applications of the ontology using examples from the domain of rare disease research. We used the expertise of an ontology expert and clinician during the development process. We semi-automatically added mappings to relevant external ontologies using automated ontology matching systems and manual assessment by experts. The ISSVA ontology should contribute to making data for vascular anomaly research more Findable, Accessible, Interoperable, and Reusable (FAIR). The ontology is available at https://bioportal.bioontology.org/ontologies/ISSVA.     

Diabetes during Pregnancy: A Maternal Disease Complicating the Course of Pregnancy with Long-Term Deleterious Effects on the Offspring. A Clinical Review

In spite of the huge progress in the treatment of diabetes mellitus, we are still in the situation that both pregestational (PGDM) and gestational diabetes (GDM) impose an additional risk to the embryo, fetus, and course of pregnancy. PGDM may increase the rate of congenital malformations, especially cardiac, nervous system, musculoskeletal system, and limbs. PGDM may interfere with fetal growth, often causing macrosomia, but in the presence of severe maternal complications, especially nephropathy, it may inhibit fetal growth. PGDM may also induce a variety of perinatal complications such as stillbirth and perinatal death, cardiomyopathy, respiratory morbidity, and perinatal asphyxia. GDM that generally develops in the second half of pregnancy induces similar but generally less severe complications. Their severity is higher with earlier onset of GDM and inversely correlated with the degree of glycemic control. Early initiation of GDM might even cause some increase in the rate of congenital malformations. Both PGDM and GDM may cause various motor and behavioral neurodevelopmental problems, including an increased incidence of attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). Most complications are reduced in incidence and severity with the improvement in diabetic control. Mechanisms of diabetic-induced damage in pregnancy are related to maternal and fetal hyperglycemia, enhanced oxidative stress, epigenetic changes, and other, less defined, pathogenic mechanisms.     

玲珑金矿田含矿断裂的基岩地球化学特征及找矿潜力评价

基岩地球化学方法广泛应用于地质找矿与生产实践,是开展深部找矿预测最直接的方法和手段。通过对玲珑金矿田井下含矿断裂和招平断裂的基岩剖面地球化学特征进行系统研究,得出以下认识：玲珑金矿田原生晕最佳指示元素组合为Au、Ag、Bi、As和Co,且Au-Ag、Au-Bi、Au-As及Au-Co元素之间呈较好的幂指数正相关。根据玲珑金矿田已探明矿脉最佳指示元素组合的统计结果,结合招平断裂基岩剖面地球化学测量结果,发现招平断裂在地表有2处金矿化异常,推测其为矿致异常,反映出该地段具有较好的找矿潜力,特别是前花园村东1021剖面中异常找矿潜力更大。     

新疆加甫沙尔苏地区中酸性小岩体型矿化蚀变遥感异常提取与找矿

中酸性小岩体的内部及附近的围岩中常形成与岩浆岩体有关的大型、超大型矿床,已成为找矿工作的一个重点方向。由于地质工作程度的限制,中酸性小岩体易被遗漏,严重制约了该类型矿化的发现。在新疆西准噶尔达尔布特断裂带下盘加甫沙尔苏地区找矿工作中,通过综合分析,提出了以遥感手段为主导的中酸性小岩体型矿化找矿模型:利用ASTER数据,通过图像增强处理、图像解译识别中酸性小岩体并提取蚀变遥感异常,结合地质、化探信息综合分析后确定重点检查区域。野外检查发现了加甫沙尔苏斑岩型钼矿化点,证实了该方法的有效性。该方法可以为西部地区中酸性小岩体型矿产资源勘查提供借鉴。     

军事电子网络对抗中网络流量异常识别

由于军事电子网络对抗中的流量具有海量、高速的特征,且异常流量具有较强的特征隐蔽性,因此,采用传统算法进行异常流量识别往往均有耗时大、有效识别率低的缺陷.为此,提出基于主分量分析与多时间序列数据挖掘算法相结合的军事电子网络对抗中网络流量异常识别方法.针对各维熵值时间序列之间具有关联性的特点,利用支持向量机算法进行多维流量数据分类,为异常流量的识别提供数据支持,依据主分量分析法对维数进行缩减,对信息熵值异常子空间与正常子空间进行有效分离,实现军事电子网络对抗中网络异常流量的有效识别.实验结果表明,采用改进算法进行军事电子网络对抗中网络流量异常识别,能够有效提高异常流量识别率及识别准确性,有效的保障了军事电子网络的安全性,具有显著的优越性.     

Networked Estimation of Multi‐Agent Systems Subject to Faults and Unreliable Information

In this paper, a novel framework for networked estimation of multi‐agent systems subject to presence of actuator faults is proposed. This framework is developed based on the notion of sub‐observers where within a group of sub‐observers each sub‐observer estimates certain states that are conditioned on a given input, output, and other state information. We model the overall estimation process by a weighted estimation (WE) digraph. By selecting an appropriate path in the WE digraph, an assigned supervisor can select and configure a set of sub‐observers to successfully estimate all the system states. In the presence of large intermittent disturbances, noise, and faults certain sub‐observers may become invalid, and consequently the supervisor reconfigures the set of sub‐observers by selecting a new path in the estimation digraph such that the impacts of these uncertainties are confined to only the local estimators. This will prevent the propagation of uncertainties on the estimation performance of the entire multi‐agent system. Simulation results provided for a five satellite formation flight system in deep space confirm the validity and applicability of our proposed analytical work.     

基于滑动窗口的直升机序列异常检测算法

无标签的序列在异常检测算法中往往存在着对数据的信息掌握不全面、不能合理使用的情况,而采用深度学习的技术实现检测时往往对其计算的解释性欠佳;对于攻克这些难题,以直升机飞行数据为例对时间序列的反常检测问题展开了深入研究,并利用Iforest技术和PCA算法,给出了一个采用滑动窗口的时间序列异常检测方法,利用从滑动窗口采集信息的时间变化状态等数据信息,将序列异常检测问题转换为点异常检测问题;同时以auc评分为衡量标准,从带有时刻特殊标志的多个信息集上检验了检测效率的提高;在无标签的直升机飞行数据集上进行实验,验证了算法的有效性,并通过对比检测过程中不同特征变量的变化情况,从算法层面和现实层面上阐述了算法的可解释性。     

Multidecadal Trends in Large-Scale Annual Mean SATa Based on CMIP5 Historical Simulations and Future Projections

Based on observations and Coupled Model Intercomparison Project Phase 5 (CMIP5) results, multidecadal variations and trends in annual mean surface air temperature anomalies (SATa) at global, hemispheric, and hemispheric land and ocean scales in the past and under the future scenarios of two representative concentration pathways (RCPs) are analyzed. Fifteen models are selected based on their performances in capturing the temporal variability, long-term trend, multidecadal variations, and trends in global annual mean SATa. Observational data analysis shows that the multidecadal variations in annual mean SATa of the land and ocean in the northern hemisphere (NH) and of the ocean in the southern hemisphere (SH) are similar to those of the global mean, showing an increase during the 1900–1944 and 1971–2000 periods, and flattening or even cooling during the 1945–1970 and 2001–2013 periods. These observed characteristics are basically reproduced by the models. However, SATa over SH land show an increase during the 1945–1970 period, which differs from the other hemispheric scales, and this feature is not captured well by the models. For the recent hiatus period (2001–2013), the projected trends of BCC-CSM1-1-m, CMCC-CM, GFDL-ESM2M, and NorESM1-ME at the global and hemispheric scales are closest to the observations based on RCP4.5 and RCP8.5 scenarios, suggesting that these four models have better projection capability in SATa. Because these four models are better at simulating and projecting the multidecadal trends of SATa, they are selected to analyze future SATa variations at the global and hemispheric scales during the 2006–2099 period. The selected multi-model ensemble (MME) projected trends in annual mean SATa for the globe, NH, and SH under RCP4.5 (RCP8.5) are 0.17 (0.29) °C, 0.22 (0.36) °C, and 0.11 (0.23) °C·decade⁻¹ in the 21st century, respectively. These values are significantly lower than the projections of CMIP5 MME without model selection.