编程实现基于属性点访问控制模型ABAC,条件:创建主体S,客体O,环境E,操作O的集合并写入文本数据库,创建访问控制策略,可信应用监控器启用,接收用户对权限的访问请求,进行决策,决策结果为拒绝或运行,出现冲突时拒绝优先,缺乏匹配的策略时默认决策为拒绝,主体,客体,环境属性各有10个元素,每一个策略中的主客体,环境相关规则由3-5个属性点布尔表达式组成(随机生成),每个用户请求中,主客体,环境相关属性3-5个(随机生成),策略数共10000个(5000正授权,5000负授权)随机生成,随机生成10000个请
时间: 2024-03-17 21:45:12 浏览: 7
题,使用Python语言实现。
首先,需要定义主体S、客体O、环境E和操作O的集合:
```python
subjects = ['S' + str(i) for i in range(1, 11)]
objects = ['O' + str(i) for i in range(1, 11)]
environments = ['E' + str(i) for i in range(1, 11)]
operations = ['read', 'write', 'execute']
```
然后,需要将这些集合写入文本数据库:
```python
with open('database.txt', 'w') as f:
f.write('Subjects:\n')
f.write('\n'.join(subjects))
f.write('\n\nObjects:\n')
f.write('\n'.join(objects))
f.write('\n\nEnvironments:\n')
f.write('\n'.join(environments))
f.write('\n\nOperations:\n')
f.write('\n'.join(operations))
```
接下来,需要随机生成10000个访问控制策略。每个策略中的主客体、环境相关规则由3-5个属性点布尔表达式组成:
```python
import random
attributes = ['attr1', 'attr2', 'attr3', 'attr4', 'attr5', 'attr6', 'attr7', 'attr8', 'attr9', 'attr10']
policies = []
for i in range(10000):
policy = {}
policy['target'] = {}
policy['target']['subject'] = random.sample(subjects, 1)[0]
policy['target']['object'] = random.sample(objects, 1)[0]
policy['target']['environment'] = random.sample(environments, 1)[0]
policy['rules'] = []
for j in range(random.randint(3, 5)):
rule = {}
rule['subject'] = random.sample(attributes, 1)[0]
rule['object'] = random.sample(attributes, 1)[0]
rule['environment'] = random.sample(attributes, 1)[0]
rule['value'] = random.choice([True, False])
policy['rules'].append(rule)
policy['decision'] = random.choice(['Permit', 'Deny'])
policies.append(policy)
```
随机生成10000个访问请求,每个请求中,主客体、环境相关属性也随机生成3-5个:
```python
requests = []
for i in range(10000):
request = {}
request['subject'] = {}
for j in range(random.randint(3, 5)):
request['subject'][random.sample(attributes, 1)[0]] = random.choice([True, False])
request['object'] = {}
for j in range(random.randint(3, 5)):
request['object'][random.sample(attributes, 1)[0]] = random.choice([True, False])
request['environment'] = {}
for j in range(random.randint(3, 5)):
request['environment'][random.sample(attributes, 1)[0]] = random.choice([True, False])
request['operation'] = random.sample(operations, 1)[0]
requests.append(request)
```
定义一个函数,实现访问控制决策:
```python
def access_control_decision(policy, request):
for rule in policy['rules']:
if rule['subject'] in request['subject'] and rule['object'] in request['object'] and rule['environment'] in request['environment']:
if request['subject'][rule['subject']] == rule['value'] and request['object'][rule['object']] == rule['value'] and request['environment'][rule['environment']] == rule['value']:
return policy['decision']
return 'Deny'
```
最后,对于每个访问请求,依次进行访问控制决策:
```python
with open('result.txt', 'w') as f:
for request in requests:
decision = 'Deny'
for policy in policies:
if access_control_decision(policy, request) == 'Permit':
decision = 'Permit'
break
f.write(str(request) + ': ' + decision + '\n')
```
注意,在实际应用中,访问控制策略的数量可能会更大,访问请求的频率也可能更高,因此需要对程序进行优化。