Online Anomaly Detection Leveraging Stream-Based Clustering and Real-Time Telemetry

计算机科学 异常检测 聚类分析 试验台 数据挖掘 遥测 僵尸网络 实时计算 故障排除 离群值 数据库扫描 人工智能 模糊聚类 计算机网络 互联网 电信 树冠聚类算法 操作系统 万维网
作者
Andrian Putina,Dario Rossi
出处
期刊:IEEE Transactions on Network and Service Management [Institute of Electrical and Electronics Engineers]
卷期号:18 (1): 839-854 被引量:23
标识
DOI:10.1109/tnsm.2020.3037019
摘要

Recent technology evolution allows network equipment to continuously stream a wealth of "telemetry" information, which pertains to multiple protocols and layers of the stack, at a very fine spatial-grain and high-frequency. This deluge of telemetry data clearly offers new opportunities for network control and troubleshooting, but also poses a serious challenge for what concerns its real-time processing. We tackle this challenge by applying streaming machine-learning techniques to the continuous flow of control and data-plane telemetry data, with the purpose of real-time detection of anomalies. In particular, we implement an anomaly detection engine that leverages DenStream, an unsupervised clustering technique, and apply it to features collected from a large-scale testbed comprising tens of routers traversed up to 3Terabit/s worth of real application traffic. We contrast DenStream with offline algorithms such as DBScan and Local Outlier Factor (LOF), as well as online algorithms such as the windowed version of DBScan, ExactSTORM, Continuous Outlier Detection (COD) and Robust Random Cut Forest (RRCF). Our experimental campaign compares these seven algorithms under both accuracy and computational complexity viewpoints: results testify that DenStream (i) achieves detection results on par with RRCF, the best performing algorithm and (ii) is significantly faster than other approaches, notably over two orders of magnitude faster than RRCF. In spirit with the recent trend toward reproducibility of results, we make our code available as open source to the scientific community.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Lucas应助万物安生采纳,获得10
1秒前
健忘跳跳糖完成签到,获得积分20
1秒前
2秒前
2秒前
C陈完成签到,获得积分10
4秒前
5秒前
suger发布了新的文献求助10
6秒前
7秒前
干雅柏完成签到,获得积分10
8秒前
八九完成签到,获得积分10
9秒前
10秒前
干雅柏发布了新的文献求助10
11秒前
Stardust发布了新的文献求助10
11秒前
黑白和完成签到 ,获得积分10
12秒前
yang完成签到,获得积分10
13秒前
金蛋蛋发布了新的文献求助10
14秒前
量子星尘发布了新的文献求助10
16秒前
20秒前
25秒前
淡定的电源完成签到,获得积分10
28秒前
28秒前
lm发布了新的文献求助10
31秒前
33秒前
善学以致用应助孤独问旋采纳,获得10
33秒前
孙燕应助霸气安筠采纳,获得30
34秒前
李健应助科研通管家采纳,获得10
34秒前
汉堡包应助科研通管家采纳,获得10
34秒前
SYLH应助科研通管家采纳,获得20
34秒前
SYLH应助科研通管家采纳,获得10
34秒前
上官若男应助科研通管家采纳,获得10
34秒前
烟花应助科研通管家采纳,获得10
34秒前
丘比特应助科研通管家采纳,获得10
34秒前
SYLH应助科研通管家采纳,获得10
35秒前
CAOHOU应助科研通管家采纳,获得10
35秒前
SYLH应助科研通管家采纳,获得10
35秒前
CAOHOU应助科研通管家采纳,获得10
35秒前
SYLH应助科研通管家采纳,获得10
35秒前
科研通AI2S应助科研通管家采纳,获得10
35秒前
JamesPei应助科研通管家采纳,获得10
35秒前
ding应助科研通管家采纳,获得10
35秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989378
求助须知:如何正确求助?哪些是违规求助? 3531442
关于积分的说明 11254002
捐赠科研通 3270126
什么是DOI,文献DOI怎么找? 1804887
邀请新用户注册赠送积分活动 882087
科研通“疑难数据库(出版商)”最低求助积分说明 809173