This analysis focuses a UDP hping trace. The data is [here][Pandas plotting]
Big Data Analysis with Pandas - Hping packets |
Data
The data used is [here]
No,Mac SRC,Mac Dest,IP Src,IP Dest,IP Proto,TCP Src,TCP Dest,UDP Src,UDP Dest,Len 1,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2228,0,46 2,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2229,0,46 3,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2230,0,46 4,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2231,0,46 5,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2232,0,46 6,00:0c:29:6b:0e:96,00:50:56:c0:00:08,,,,,,,,42 7,00:50:56:c0:00:08,00:0c:29:6b:0e:96,,,,,,,,42 8,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2233,0,46 9,00:0c:29:6b:0e:96,01:00:5e:00:00:fb,192.168.75.138,224.0.0.251,17,,,5353,5353,82 10,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2234,0,46 11,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2235,0,46 12,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2236,0,46 13,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2237,0,46 14,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2238,0,46 15,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2239,0,46 16,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2240,0,46 17,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2241,0,46 18,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2242,0,46 19,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2243,0,46 20,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2244,0,46 21,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2245,0,46 22,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2246,0,46 23,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2247,0,46 24,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2248,0,46 25,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2249,0,46 26,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2250,0,46 27,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2251,0,46 28,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2252,0,46 29,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2253,0,46 30,00:0c:29:6b:0e:96,00:50:56:c0:00:08,,,,,,,,42 31,00:50:56:c0:00:08,00:0c:29:6b:0e:96,,,,,,,,42 32,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2254,0,46 33,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2255,0,46 34,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2256,0,46 35,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2257,0,46 36,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2258,0,46 37,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2259,0,46 38,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2260,0,46 39,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2261,0,46 40,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2262,0,46 41,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2263,0,46 42,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2264,0,46 43,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2265,0,46 44,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2266,0,46 45,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2267,0,46 46,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2268,0,46 47,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2269,0,46 48,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2270,0,46 49,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2271,0,46 50,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2272,0,46 51,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2273,0,46 52,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2274,0,46 53,00:0c:29:6b:0e:96,00:50:56:c0:00:08,,,,,,,,42 54,00:50:56:c0:00:08,00:0c:29:6b:0e:96,,,,,,,,42 55,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2275,0,46 56,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2276,0,46 57,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2277,0,46 58,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2278,0,46 59,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2279,0,46 60,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2280,0,46 61,00:0c:29:6b:0e:96,00:50:56:c0:00:08,192.168.75.138,192.168.75.1,17,,,2281,0,46
Code
An outline of the code is:
import numpy as np import pandas as pd import sys import statsmodels.api as sm