网络协议2天集训

Related tags

Deep Learningprotocol-2day-training
Overview

网络协议2天集训

抓包工具安装

Wireshark

wireshark下载地址

Tcpdump

  1. CentOS
yum install tcpdump -y
  1. Ubuntu
apt-get install tcpdump -y

k8s抓包测试环境

查看虚拟网卡veth pair

查看网桥cni0上的虚拟网卡

[master]# yum install bridge-utils -y
[master]# brctl show
bridge name     bridge id               STP enabled     interfaces
cni0            8000.822a0551fe51       no              veth01d2bc26
                                                        veth1b7415be
                                                        veth48059492
                                                        veth6174f7d6
                                                        veth6a56ab55
                                                        vethf3807a14
                                                        vethfbd1eb75
docker0         8000.024218847f20       no

查找容器网卡对应的主机上veth pair

比如，容器tea-6fb46d899f-4zkt2的IP地址是10.244.0.60：

[[email protected] conf]# kubectl describe pod tea-6fb46d899f-4zkt2 | grep IP
IP:           10.244.0.60

它的MAC地址是B2:AD:3A:6E:3A:4F，如下：

[[email protected] conf]# kubectl exec -it tea-6fb46d899f-4zkt2 -- sh
/ $ ip addr
3: [email protected]: <BROADCAST,MULTICAST,UP,LOWER_UP,M-DOWN> mtu 1450 qdisc noqueue state UP 
    link/ether b2:ad:3a:6e:3a:4f brd ff:ff:ff:ff:ff:ff
    inet 10.244.0.60/24 brd 10.244.0.255 scope global eth0
       valid_lft forever preferred_lft forever
    inet6 fe80::b0ad:3aff:fe6e:3a4f/64 scope link 
       valid_lft forever preferred_lft forever

注意，它的eth0序号是17。那么，它对应的主机veth pair虚拟网卡就是vetha1f852ea：

[[email protected] wp]# ip link show | egrep "veth" | awk -F":" '{print $1": "$2}'
14:  [email protected]
15:  [email protected]
16:  [email protected]
17:  [email protected]
18:  [email protected]
19:  [email protected]
20:  [email protected]
21:  [email protected]

当需要抓包时，用tcpdump -i vetha1f852ea即可抓取到容器报文：

[[email protected] wp]# tcpdump -i vetha1f852ea
tcpdump: verbose output suppressed, use -v or -vv for full protocol decode
listening on vetha1f852ea, link-type EN10MB (Ethernet), capture size 262144 bytes


19:51:49.847648 IP 10.244.0.60.54477 > 10.96.0.10.domain: 58561+ A? www.baidu.com.default.svc.cluster.local. (57)
19:51:49.847731 IP 10.244.0.60.54477 > 10.96.0.10.domain: 59710+ AAAA? www.baidu.com.default.svc.cluster.local. (57)
19:51:49.849113 IP 10.244.0.58.domain > 10.244.0.60.54477: 59710 NXDomain*- 0/1/0 (150)
19:51:49.849268 IP 10.244.0.58.domain > 10.244.0.60.54477: 58561 NXDomain*- 0/1/0 (150)

跨L3三层vxlan网络抓包

当172.27.0.11主机上访问172.27.16.10主机上的10.244.1.3容器时，IP、MAC地址的获取如下：

Underlay层的IP与MAC地址

在源主机上执行ifconfig，从eth0上即可看到Underlay源IP为172.27.0.11，以及Underlay源MAC为52:54:00:c2:ee:db：

[[email protected] wp]# ifconfig eth0
eth0: flags=4163<UP,BROADCAST,RUNNING,MULTICAST>  mtu 1500
        inet 172.27.0.11  netmask 255.255.240.0  broadcast 172.27.15.255
        inet6 fe80::5054:ff:fec2:eedb  prefixlen 64  scopeid 0x20<link>
        ether 52:54:00:c2:ee:db  txqueuelen 1000  (Ethernet)
        RX packets 783420  bytes 872472212 (832.0 MiB)
        RX errors 0  dropped 0  overruns 0  frame 0
        TX packets 462834  bytes 135019947 (128.7 MiB)
        TX errors 0  dropped 0 overruns 0  carrier 0  collisions 0

在目的主机上执行同样步骤，获取到Underlay目的IP为172.27.16.10：

[[email protected] ~]# ifconfig eth0
eth0: flags=4163<UP,BROADCAST,RUNNING,MULTICAST>  mtu 1500
        inet 172.27.16.10  netmask 255.255.240.0  broadcast 172.27.31.255
        inet6 fe80::5054:ff:fe4e:502  prefixlen 64  scopeid 0x20<link>
        ether 52:54:00:4e:05:02  txqueuelen 1000  (Ethernet)
        RX packets 39103520  bytes 6692916434 (6.2 GiB)
        RX errors 0  dropped 0  overruns 0  frame 0
        TX packets 1169194048  bytes 117270853999 (109.2 GiB)
        TX errors 0  dropped 0 overruns 0  carrier 0  collisions 0

需要注意，Underlay目的MAC并不是52:54:00:4e:05:02！Underlay目的MAC实际上是交换机的MAC地址fe:ee:32:07:ea:07：

[[email protected] net]# arp -v
Address                  HWtype  HWaddress           Flags Mask            Iface
gateway                  ether   fe:ee:32:07:ea:07   C                     eth0

这样，Underlay层的4个地址都已得到！

Overlay层的IP与MAC地址

目标容器的IP地址是10.244.1.3，但MAC地址却不能是容器的MAC地址，而必须是flannel.1的地址，因为flannel程序需要将Underlay层剥离，同时修改Overlay层，所以目标MAC地址其实是2a:3c:a0:e1:a9:b6：

[[email protected] net]# arp -v
Address                  HWtype  HWaddress           Flags Mask            Iface
10.244.1.0               ether   2a:3c:a0:e1:a9:b6   CM                    flannel.1

而源IP地址与MAC要根据路由规则来。比如，访问10.244.1.3是通过flannel.1网卡进行的：

[[email protected] net]# ip route
default via 172.27.0.1 dev eth0
10.244.1.0/24 via 10.244.1.0 dev flannel.1 onlink
172.27.0.0/20 dev eth0 proto kernel scope link src 172.27.0.11

而flannel.1虚拟网卡的IP地址则是10.244.0.0：

[[email protected] net]# ifconfig flannel.1
flannel.1: flags=4163<UP,BROADCAST,RUNNING,MULTICAST>  mtu 1450
        inet 10.244.0.0  netmask 255.255.255.255  broadcast 10.244.0.0
ether 8e:5c:79:80:cd:cc  txqueuelen 0  (Ethernet)
[[email protected] net]# ifconfig eth0
eth0: flags=4163<UP,BROADCAST,RUNNING,MULTICAST>  mtu 1500
        inet 172.27.0.11  netmask 255.255.240.0  broadcast 172.27.15.255
ether 52:54:00:c2:ee:db  txqueuelen 1000  (Ethernet)

它的MAC地址则是8e:5c:79:80 💿 cc。

常见网络编码

ASCII编码

参见wiki，如果打不开，可以查看下表:

控制字符

二进制 十进制 十六进制 缩写 Unicode表示法 脱出字符表示法 名称／意义
0000 0000 0 00 NUL ␀ ^@ 空字符（Null）
0000 0001 1 01 SOH ␁ ^A 标题开始
0000 0010 2 02 STX ␂ ^B 本文开始
0000 0011 3 03 ETX ␃ ^C 本文结束
0000 0100 4 04 EOT ␄ ^D 传输结束
0000 0101 5 05 ENQ ␅ ^E 请求
0000 0110 6 06 ACK ␆ ^F 确认回应
0000 0111 7 07 BEL ␇ ^G 响铃
0000 1000 8 08 BS ␈ ^H 退格
0000 1001 9 09 HT ␉ ^I 水平定位符号
0000 1010 10 0A LF ␊ ^J 换行键
0000 1011 11 0B VT ␋ ^K 垂直定位符号
0000 1100 12 0C FF ␌ ^L 换页键
0000 1101 13 0D CR ␍ ^M CR (字符)
0000 1110 14 0E SO ␎ ^N 取消变换（Shift out）
0000 1111 15 0F SI ␏ ^O 启用变换（Shift in）
0001 0000 16 10 DLE ␐ ^P 跳出数据通讯
0001 0001 17 11 DC1 ␑ ^Q 设备控制一（XON 激活软件速度控制）
0001 0010 18 12 DC2 ␒ ^R 设备控制二
0001 0011 19 13 DC3 ␓ ^S 设备控制三（XOFF 停用软件速度控制）
0001 0100 20 14 DC4 ␔ ^T 设备控制四
0001 0101 21 15 NAK ␕ ^U 确认失败回应
0001 0110 22 16 SYN ␖ ^V 同步用暂停
0001 0111 23 17 ETB ␗ ^W 区块传输结束
0001 1000 24 18 CAN ␘ ^X 取消
0001 1001 25 19 EM ␙ ^Y 连线介质中断
0001 1010 26 1A SUB ␚ ^Z 替换
0001 1011 27 1B ESC ␛ ^[ 退出键
0001 1100 28 1C FS ␜ ^\ 文件分割符
0001 1101 29 1D GS ␝ ^] 组群分隔符
0001 1110 30 1E RS ␞ ^^ 记录分隔符
0001 1111 31 1F US ␟ ^_ 单元分隔符
0111 1111 127 7F DEL ␡ ^? Delete字符

可显示字符

二进制 十进制 十六进制 图形
0010 0000 32 20 (space)
0010 0001 33 21 !
0010 0010 34 22 "
0010 0011 35 23 #
0010 0100 36 24 $
0010 0101 37 25 %
0010 0110 38 26 &
0010 0111 39 27 '
0010 1000 40 28 (
0010 1001 41 29 )
0010 1010 42 2A *
0010 1011 43 2B +
0010 1100 44 2C ,
0010 1101 45 2D -
0010 1110 46 2E .
0010 1111 47 2F /
0011 0000 48 30 0
0011 0001 49 31 1
0011 0010 50 32 2
0011 0011 51 33 3
0011 0100 52 34 4
0011 0101 53 35 5
0011 0110 54 36 6
0011 0111 55 37 7
0011 1000 56 38 8
0011 1001 57 39 9
0011 1010 58 3A :
0011 1011 59 3B ;
0011 1100 60 3C <
0011 1101 61 3D =
0011 1110 62 3E >
0011 1111 63 3F ?
二进制 十进制 十六进制 图形
0100 0000 64 40 @
0100 0001 65 41 A
0100 0010 66 42 B
0100 0011 67 43 C
0100 0100 68 44 D
0100 0101 69 45 E
0100 0110 70 46 F
0100 0111 71 47 G
0100 1000 72 48 H
0100 1001 73 49 I
0100 1010 74 4A J
0100 1011 75 4B K
0100 1100 76 4C L
0100 1101 77 4D M
0100 1110 78 4E N
0100 1111 79 4F O
0101 0000 80 50 P
0101 0001 81 51 Q
0101 0010 82 52 R
0101 0011 83 53 S
0101 0100 84 54 T
0101 0101 85 55 U
0101 0110 86 56 V
0101 0111 87 57 W
0101 1000 88 58 X
0101 1001 89 59 Y
0101 1010 90 5A Z
0101 1011 91 5B [
0101 1100 92 5C \
0101 1101 93 5D ]
0101 1110 94 5E ^
0101 1111 95 5F _
二进制 十进制 十六进制 图形
0110 0000 96 60 `
0110 0001 97 61 a
0110 0010 98 62 b
0110 0011 99 63 c
0110 0100 100 64 d
0110 0101 101 65 e
0110 0110 102 66 f
0110 0111 103 67 g
0110 1000 104 68 h
0110 1001 105 69 i
0110 1010 106 6A j
0110 1011 107 6B k
0110 1100 108 6C l
0110 1101 109 6D m
0110 1110 110 6E n
0110 1111 111 6F o
0111 0000 112 70 p
0111 0001 113 71 q
0111 0010 114 72 r
0111 0011 115 73 s
0111 0100 116 74 t
0111 0101 117 75 u
0111 0110 118 76 v
0111 0111 119 77 w
0111 1000 120 78 x
0111 1001 121 79 y
0111 1010 122 7A z
0111 1011 123 7B {
0111 1100 124 7C
0111 1101 125 7D }
0111 1110 126 7E ~
Owner
GitHub Repository
Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark

Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark Yong

19 Dec 17, 2022
This is a yolo3 implemented via tensorflow 2.7

YoloV3 - an object detection algorithm implemented via TF 2.x source code In this article I assume you've already familiar with basic computer vision

2 Jan 17, 2022
Differentiable molecular simulation of proteins with a coarse-grained potential

Differentiable molecular simulation of proteins with a coarse-grained potential This repository contains the learned potential, simulation scripts and

UCL Bioinformatics Group 44 Dec 10, 2022
Action Segmentation Evaluation

Reference Action Segmentation Evaluation Code This repository contains the reference code for action segmentation evaluation. If you have a bug-fix/im

5 May 22, 2022
Code for the Weighted, Accelerated and Restarted Primal-dual algorithm. This algorithm achieves stable linear convergence for reconstruction from undersampled noisy measurements under an approximate sharpness condition. See the paper for details.

WARPd Code for the Weighted, Accelerated and Restarted Primal-dual algorithm. This algorithm achieves stable linear convergence for reconstruction fro

Matthew Colbrook 1 Apr 08, 2022
Code for the IJCAI 2021 paper "Structure Guided Lane Detection"

SGNet Project for the IJCAI 2021 paper "Structure Guided Lane Detection" Abstract Recently, lane detection has made great progress with the rapid deve

Jinming Su 27 Dec 08, 2022
Free-duolingo-plus - Duolingo account creator that uses your invite code to get you free duolingo plus

free-duolingo-plus duolingo account creator that uses your invite code to get yo

1 Jan 06, 2022
Create Data & AI apps in 20 lines of code with Shimoku

Install with: pip install shimoku-api-python Start with: from os import getenv import shimoku_api_python.client as Shimoku

Shimoku 5 Nov 07, 2022
Voice of Pajlada with model and weights.

Pajlada TTS Stripped down version of ForwardTacotron (https://github.com/as-ideas/ForwardTacotron) with pretrained weights for Pajlada's (https://gith

6 Sep 03, 2021
LibFewShot: A Comprehensive Library for Few-shot Learning.

LibFewShot Make few-shot learning easy. Supported Methods Meta MAML(ICML'17) ANIL(ICLR'20) R2D2(ICLR'19) Versa(NeurIPS'18) LEO(ICLR'19) MTL(CVPR'19) M

<a href=[email protected]&L"> 603 Jan 05, 2023
GazeScroller - Using Facial Movements to perform Hands-free Gesture on the system

GazeScroller Using Facial Movements to perform Hands-free Gesture on the system

2 Jan 05, 2022
Pairwise model for commonlit competition

Pairwise model for commonlit competition To run: - install requirements - create input directory with train_folds.csv and other competition data - cd

abhishek thakur 45 Aug 31, 2022
HALO: A Skeleton-Driven Neural Occupancy Representation for Articulated Hands

HALO: A Skeleton-Driven Neural Occupancy Representation for Articulated Hands Oral Presentation, 3DV 2021 Korrawe Karunratanakul, Adrian Spurr, Zicong

Korrawe Karunratanakul 43 Oct 07, 2022
Export CenterPoint PonintPillars ONNX Model For TensorRT

CenterPoint-PonintPillars Pytroch model convert to ONNX and TensorRT Welcome to CenterPoint! This project is fork from tianweiy/CenterPoint. I impleme

CarkusL 149 Dec 13, 2022
Pytorch implementation of Feature Pyramid Network (FPN) for Object Detection

fpn.pytorch Pytorch implementation of Feature Pyramid Network (FPN) for Object Detection Introduction This project inherits the property of our pytorc

Jianwei Yang 912 Dec 21, 2022
[AAAI-2021] Visual Boundary Knowledge Translation for Foreground Segmentation

Trans-Net Code for (Visual Boundary Knowledge Translation for Foreground Segmentation, AAAI2021). [https://ojs.aaai.org/index.php/AAAI/article/view/16

ZJU-VIPA 2 Mar 04, 2022
Trafffic prediction analysis using hybrid models - Machine Learning

Hybrid Machine learning Model Clone the Repository Create a new Directory as assests and download the model from the below link Model Link To Start th

1 Feb 08, 2022
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

ENet in Caffe Execution times and hardware requirements Network 1024x512 1280x720 Parameters Model size (fp32) ENet 20.4 ms 32.9 ms 0.36 M 1.5 MB SegN

Timo Sämann 561 Jan 04, 2023
Finite-temperature variational Monte Carlo calculation of uniform electron gas using neural canonical transformation.

CoulombGas This code implements the neural canonical transformation approach to the thermodynamic properties of uniform electron gas. Building on JAX,

FermiFlow 9 Mar 03, 2022
Audio-Visual Generalized Few-Shot Learning with Prototype-Based Co-Adaptation

Audio-Visual Generalized Few-Shot Learning with Prototype-Based Co-Adaptation The code repository for "Audio-Visual Generalized Few-Shot Learning with

Kaiaicy 3 Jun 27, 2022