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similarity.py
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similarity.py
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# -*- codeing = utf-8 -*-
# @Time : 2023/7/12 8:16
# @Author : Lowry
# @File : similarity
# @Software : PyCharm
import numpy as np
import random
from tqdm import trange, tqdm
def sim_a(byte_array=None, seed_list=None, max_len=None):
"""
计算相似度度量a
:parameter byte_array: 字节序列
:parameter seed_list: 种子名序列
:parameter max_len: 字节序列最大长度
:return similarity_a_list: 相似度度量a结果
"""
similarity_a_list = []
byte_list = byte_array.tolist()
seed_num = len(seed_list)
for i in tqdm(range(len(byte_list)), desc='Similarity_a'):
similarity_a = []
for j in range(max_len):
byte_row = byte_array[:, j].tolist()
num = byte_row.count(byte_list[i][j])
sim = round(num / seed_num, 6)
similarity_a.append(sim)
similarity_a_list.append(round(sum(similarity_a), 6))
return similarity_a_list
def sim_b(byte_array=None, seed_list=None, max_len=None):
"""
计算相似度度量b
:parameter byte_array: 字节序列
:parameter seed_list: 种子名序列
:parameter max_len: 字节序列最大长度
:return similarity_b_list: 相似度度量b结果
"""
similarity_b_list = []
similarity_b_matrix = [] # 类上三角矩阵，每个元素记录第i、j种子的相似度
byte_list = byte_array.tolist()
seed_num = len(seed_list)
for i in range(seed_num):
similarity_b = []
for q in range(i): # 填充下三角元素，减少逐个遍历的时间
similarity_b.append(similarity_b_matrix[q][i])
similarity_b.append(1)
for j in tqdm(range(seed_num-i-1), desc=f'Similarity_b({i+1}/{seed_num})--->seed {seed_list[i][:9]}'):
j += (i + 1)
similarity_num = 0 # 相同位置字节大小相同的数量
for t in range(max_len):
if byte_list[i][t] == byte_array[j][t]:
similarity_num += 1
similarity_b.append(round(similarity_num/max_len, 6)) # 第i、j种子所有同一位置字节相同的比例
similarity_b_matrix.append(similarity_b) # 更新矩阵
similarity_b_list.append(round(sum(similarity_b), 6)) # 填充第i个种子的相似度度量b
return similarity_b_list
def similarity(byte_array=None, seed_list=None, max_len=None):
"""
计算自适应相似度similarity
:parameter byte_array: 字节序列
:parameter seed_list: 种子名序列
:parameter max_len: 字节序列最大长度
:return similarity_list: 相似度度量结果
"""
a_list = sim_a(byte_array=byte_array, seed_list=seed_list, max_len=max_len)
b_list = sim_b(byte_array=byte_array, seed_list=seed_list, max_len=max_len)
k = 0.5
# 超参数k(α)
similarity_list = [round(a_list[i] * k + b_list[i] * (1 - k), 6) for i in range(len(seed_list))]
return similarity_list
def get_index(ele=None, list_src=None):
"""
:param ele: 指定元素
:param list_src: 列表
:return index_list: 该元素存在于列表中的索引
"""
index_list = []
for i in range(len(list_src)):
if list_src[i] == ele:
index_list.append(i)
return index_list
def order_seed(similarity_list=None, seed_list=None):
"""
根据similarity从大到小对种子进行排序
:param similarity_list: 自适应相似度序列
:param seed_list: 种子名序列
:return seed_list_new: 排序结果
"""
seed_list_new = []
similarity_sort = sorted(similarity_list)
i = 0
temp = 0 # 记录当前相似度得分
while i < len(seed_list):
sim = similarity_sort[i]
if sim == temp:
i += 1
continue
else:
temp = sim
index_list = get_index(sim, similarity_list)
for j in index_list:
seed_list_new.append(seed_list[j])
i += 1
return seed_list_new
def similarity_re(byte_array=None, seed_list=None, max_len=None):
"""
主函数
:parameter byte_array: 字节序列
:parameter seed_list: 种子名序列
:parameter max_len: 字节序列最大长度
:return similarity_list: 相似度度量结果
"""
""" 计算相似度 """
similarity_list = similarity(byte_array=byte_array, seed_list=seed_list, max_len=max_len)
""" 排序 """
seed_list_new = order_seed(similarity_list=similarity_list, seed_list=seed_list)
return seed_list_new
def test():
byte_list = []
seed_list = []
for i in tqdm(range(100), desc='Initial seed'):
seed = 'id:' + str(i)
byte = [random.randint(0, 255)for j in range(10000)]
byte_list.append(byte)
seed_list.append(seed)
byte_array = np.array(byte_list)
similarity_re(byte_array=byte_array, seed_list=seed_list, max_len=10000)
print('finish')
# test()