1. System Overview
Database risk discovery systems are designed to identify and mitigate potential risks in databases, such as SQL injection, unauthorized access, data breaches, etc. The system monitors database activity in real time through automation tools, analyzes logs, recognizes abnormal behaviors, and provides repair suggestions.
2. System architecture
The system consists of the following modules:
- Data collection module: collects database logs, network traffic, user behavior and other data.
- Data analysis module: Use rules engines and machine learning algorithms to analyze data and identify exceptions.
- Risk Assessment Module: Assess the identified risks and determine the severity.
- Alarm and response module: Trigger alarms and take response measures, such as blocking connections or notifying administrators.
- Reporting and visualization module: generate risk reports and provide a visual interface to display risk status.
3. Key technologies
1. Data acquisition technology:
- Log collection: Obtain operation records through the database log interface.
- Network traffic analysis: Use network sniffing tools to capture database traffic.
- User behavior monitoring: record user login, query and other behaviors.
2. Data analysis technology:
- Rule Engine: Detect risks based on predefined rules (such as SQL injection features).
- Machine learning: train models through historical data to identify unknown risk patterns.
3. Risk assessment technology:
- Risk score: Score based on risk type, frequency, impact and other factors.
- Priority sort: Sort by ratings and prioritize high risk.
4. Alarm and response technology:
- Real-time alarm: notify the administrator through email, text messages, etc.
- Automatic response: Automatically block malicious IP or pause suspicious users.
5. Reporting and visualization technology:
- Report generation: Generate risk reports regularly to provide detailed analysis and recommendations.
- Visual interface: Show risk status and trends through charts.
4. System implementation
Development languages and tools:
- Python/Java: for data processing and analysis.
- Elasticsearch/Kibana: for log storage and visualization.
- Machine learning libraries: such as Scikit-learn, TensorFlow, used for model training.
Database support:
- Mainstream databases: such as MySQL, PostgreSQL, Oracle, SQL Server, etc.
- NoSQL databases: such as MongoDB, Cassandra, etc.
The following is a simplified version of Python implementation, covering core functions such as data acquisition, rule engine, risk assessment, alarm and visualization. This sample code is for demonstration purposes only, and the actual production environment requires more complex implementations and optimizations.
import logging
import time
from datetime import datetime
from collections import defaultdict
import pandas as pd
import as plt
#Configuration log(level=, format='%(asctime)s - %(levelname)s - %(message)s')
# Simulate database logsclass DatabaseLogger:
def __init__(self):
= []
def log_query(self, user, query, timestamp=None):
if not timestamp:
timestamp = ().strftime("%Y-%m-%d %H:%M:%S")
log_entry = {"user": user, "query": query, "timestamp": timestamp}
(log_entry)
(f"Logged query: {log_entry}")
def get_logs(self):
return
# Rule Engineclass RuleEngine:
def __init__(self):
= [
{"name": "SQL Injection", "pattern": ["' OR '1'='1", ";--", "UNION SELECT"]},
{"name": "Sensitive Data Access", "pattern": ["SELECT * FROM users", "SELECT * FROM credit_cards"]},
{"name": "Brute Force", "threshold": 5} # 5 queries within 10 seconds
]
def analyze_logs(self, logs):
risks = []
user_query_count = defaultdict(int)
for log in logs:
user = log["user"]
query = log["query"]
timestamp = log["timestamp"]
# Rule 1: SQL injection detection for rule in :
if "pattern" in rule:
for pattern in rule["pattern"]:
if pattern in query:
({
"user": user,
"query": query,
"timestamp": timestamp,
"risk": rule["name"],
"severity": "High"
})
# Rule 2: Cracking detection if "threshold" in rule:
user_query_count[user] += 1
if user_query_count[user] > rule["threshold"]:
({
"user": user,
"query": query,
"timestamp": timestamp,
"risk": rule["name"],
"severity": "Medium"
})
return risks
# risk assessmentclass RiskAssessor:
@staticmethod
def assess_risks(risks):
risk_summary = defaultdict(int)
for risk in risks:
risk_summary[risk["risk"]] += 1
return risk_summary
# Alarm systemclass AlertSystem:
@staticmethod
def send_alert(risk):
(f"ALERT: Risk detected - {risk}")
# Visualization moduleclass Visualizer:
@staticmethod
def plot_risks(risk_summary):
risks = list(risk_summary.keys())
counts = list(risk_summary.values())
(risks, counts, color='red')
('Risk Type')
('Count')
('Database Risk Summary')
()
# Main systemclass DatabaseRiskDiscoverySystem:
def __init__(self):
= DatabaseLogger()
self.rule_engine = RuleEngine()
self.risk_assessor = RiskAssessor()
self.alert_system = AlertSystem()
= Visualizer()
def run(self):
# Simulate log data .log_query("admin", "SELECT * FROM users WHERE id = 1")
.log_query("hacker", "SELECT * FROM users WHERE id = 1 OR '1'='1'")
.log_query("hacker", "SELECT * FROM credit_cards")
.log_query("hacker", "SELECT * FROM users;--")
.log_query("hacker", "SELECT * FROM users")
.log_query("hacker", "SELECT * FROM users")
.log_query("hacker", "SELECT * FROM users")
.log_query("hacker", "SELECT * FROM users")
# Get logs and analyze risks logs = .get_logs()
risks = self.rule_engine.analyze_logs(logs)
# Assess risk risk_summary = self.risk_assessor.assess_risks(risks)
# Send an alarm for risk in risks:
self.alert_system.send_alert(risk)
# Visualize risks .plot_risks(risk_summary)
# Run the systemif __name__ == "__main__":
system = DatabaseRiskDiscoverySystem()
()5. Code description
:
Simulate database logging and record user query operations.
:
Use the rule engine to detect risks such as SQL injection, sensitive data access, etc.
:
The detected risks are summarized and evaluated.
:
Send a risk alarm.
:
Use Matplotlib to draw risk charts.
:
Main system, integrate all modules and run.
This is the end of this article about using Python to implement risk identification of databases. For more related Python database risk identification content, please search for my previous articles or continue browsing the related articles below. I hope everyone will support me in the future!