Compute entropy of a pickle file

Question

I'm working on the ISCXVPN2016 dataset, it consists of some pcap files (each pcap is captured traffic of a specific app such as skype, youtube, etc.) and I have converted them to pickle files and then write them into a text file using code below:

pkl = open("AIMchat2.pcapng.pickle", "rb")
with open('file.txt', 'w') as f:
    for Item in pkl:
        f.write('%s\n' %Item)

file.txt:

b'\x80\x03]q\x00(cnumpy.core.multiarray\n' b'_reconstruct\n' b'q\x01cnumpy\n' b'ndarray\n' b'q\x02K\x00\x85q\x03C\x01bq\x04\x87q\x05Rq\x06(K\x01K\x9d\x85q\x07cnumpy\n' b'dtype\n' b'q\x08X\x02\x00\x00\x00u1q\tK\x00K\x01\x87q\n' b'Rq\x0b(K\x03X\x01\x00\x00\x00|q\x0cNNNJ\xff\xff\xff\xffJ\xff\xff\xff\xffK\x00tq\rb\x89C\x9dE\x00\x00\x9dU\xbc@\x00\x80\x06\xd7\xc9\x83\xca\xf0W@\x0c\x18\xa74I\x01\xbb\t].\xc8\xf3*\xc51P\x18\xfa[)j\x00\x00\x17\x03\x02\x00p\x14\x90\xccY|\xa3\x7f\xd1\x12\xe2\xb4.U9)\xf20\xf1{\xbd\x1d\xa3W\x0c\x19\xc2\xf0\x8c\x0b\x8c\x86\x16\x99\xd8:\x19\xb0G\xe7\xb2\xf4\x9d\x82\x8e&a\x04\xf2\xa2\x8e\xce\xa4b\xcc\xfb\xe4\xd0\xde\x89eUU]\x1e\xfeF\x9bv\x88\xf4\xf3\xdc\x8f\xde\xa6Kk1q`\x94]\x13\xd7|\xa3\x16\xce\xcc\x1b\xa7\x10\xc5\xbd\x00\xe8M\x8b\x05v\x95\xa3\x8c\xd0\x83\xc1\xf1\x12\xee\x9f\xefmq\x0etq\x0fbh\x01h\x02K\x00\x85q\x10h\x04\x87q\x11Rq\x12(K\x01K.\x85q\x13h\x0b\x89C.E\x00\x00

My question is how I can compute the entropy of each pickle file?

(I have updated the question)

Answer 1

If I do nothing wrong this is the answer (based on How to calculate the entropy of a file? and Shannon entropy).

#!/usr/bin/env python3

import math


filename = "random_data.bin"

with open(filename, "rb") as file:
    counters = {byte: 0 for byte in range(2 ** 8)}  # start all counters with zeros

    for byte in file.read():  # read in chunks for large files
        counters[byte] += 1  # increase counter for specified byte

    filesize = file.tell()  # we can get file size by reading current position

    probabilities = [counter / filesize for counter in counters.values()]  # calculate probabilities for each byte

    entropy = -sum(probability * math.log2(probability) for probability in probabilities if probability > 0)  # final sum

    print(entropy)

Checked with ent program on Ubuntu 18.04 with Python 3.6.9:

$ dd if=/dev/urandom of=random_data.bin bs=1K count=16
16+0 records in
16+0 records out
16384 bytes (16 kB, 16 KiB) copied, 0.0012111 s, 13.5 MB/s
$ ent random_data.bin
Entropy = 7.988752 bits per byte.
...
$ ./calc_entropy.py
7.988751920202076

Tested with text file too.

$ ent calc_entropy.py
Entropy = 4.613356 bits per byte.
...
$ ./calc_entropy.py
4.613355601248316