Make regular expression to detect SQL injection attacks in URL string. Take attention only for non-legitimate requests that damage or steal sensetive data
Creating a regular expression to detect SQL injection attacks within a URL string can be quite challenging, as SQL injection techniques can vary widely. However, you can create a regex pattern to catch some common patterns of SQL injection attempts. Remember, relying solely on regex for SQL injection detection is not recommended, as it may not capture all possible attacks and can produce false positives. It’s crucial to combine regex with other security measures, such as parameterized queries and prepared statements.

Here is an example of a regex pattern that targets some common SQL injection patterns:

(?i)(%27|'|–|;|%3b|%2d%2d|%23|%2f*|*/|xp_)|(union(\s+all)?(\s+select|(?=())|select.+\bfrom\b|\bdelete\b.+\bfrom\b|\binsert\b.+\binto\b|\bdrop\b.+\btables?\b|\bupdate\b.+\bset\b|\bexec\b(\s+xp_|sp_))


### Explanation:

1. (?i): Case-insensitive matching.

2. (%27|'|–|;|%3b|%2d%2d|%23|%2f*|*/|xp_):
- Matches common SQL injection patterns like single quotes ('), double dash comments (--), semicolons (;), URL-encoded equivalents (%27, %3b, %2d%2d, %23), and potential malicious functions (e.g., xp_).

3. (union(\s+all)?(\s+select|(?=())):
- Matches UNION SELECT or UNION ALL SELECT to detect attempts to combine query results, potentially extracting data.

4. select.+\bfrom\b:
- Matches queries with SELECT followed by FROM, indicating data retrieval, potentially attempting to access unauthorized data.

5. \bdelete\b.+\bfrom\b:
- Matches DELETE FROM queries, which could be used to attempt data deletion.

6. \binsert\b.+\binto\b:
- Matches INSERT INTO queries, which could indicate an attempt to insert malicious data.

7. \bdrop\b.+\btables?\b:
- Matches DROP TABLE or DROP TABLES to detect attempts to delete tables from the database.

8. \bupdate\b.+\bset\b:
- Matches UPDATE SET queries, indicating potential data manipulation.

9. \bexec\b(\s+xp_|sp_):
- Matches executions of stored procedures, which might be harmful if executed improperly.

### Note:
- SQL injection is a serious vulnerability, and using regex alone is not sufficient to secure your application. Always validate input, use parameterized queries (prepared statements), and utilize web application firewalls (WAF) for better protection.
- This regex is simplified and can generate false positives. It should be used in conjunction with other security practices rather than being relied upon as the sole protection method.
Give another variant
Certainly! Here’s another variant of a regular expression that aims to capture some of the typical patterns associated with SQL injection attempts. This version focuses on different keywords and structures that are often misused in SQL injection attacks:

(?i)(\b(select|insert|update|delete|drop|alter|exec|create|truncate|union|grant|revoke)\b\s+[^\s]+|('\s*–|;\s*–|/*|*/|xp_cmdshell|char()|(%27|%22|%3b|%2f*|%2d%2d|%7c))


### Explanation:

1. (?i): Enable case-insensitive matching.

2. (\b(select|insert|update|delete|drop|alter|exec|create|truncate|union|grant|revoke)\b\s+[^\s]+):
- Matches common SQL keywords such as SELECT, INSERT, UPDATE, DELETE, DROP, ALTER, EXEC, CREATE, TRUNCATE, UNION, GRANT, and REVOKE.
- Ensures these keywords are followed by a space and then some non-space characters to catch malicious uses of these commands.

3. ('\s–|;\s–|/*|*/|xp_cmdshell|char()**:
- Targets SQL comment indicators like ' -- and ; --.
- Also looks for patterns like / *, * /, dangerous stored procedures (xp_cmdshell), and functions (char() which could be abused.

4. (%27|%22|%3b|%2f*|%2d%2d|%7c):
- Matches URL-encoded versions of single quote (%27), double quote (%22), semicolon (%3b), comment indicators (%2f*, %2d%2d), and vertical bar or pipe symbol (%7c).

### Important Considerations:

- Security Layers: Just as before, this regex is not foolproof and should not be the only line of defense. It is important to use it as part of a multi-layered security approach. Sanitizing inputs, employing prepared statements, and using Web Application Firewalls (WAFs) should be your primary defenses against SQL injection attacks.

- Adaptability: Depending on your specific application and database setup, you may need to tailor this regex to better fit your security needs or expand it to cover additional SQL patterns specific to your environment.

- False Positives: Like any regex approach for detecting SQL injections, this can result in false positives. It is essential to have a strategy for evaluating and handling such cases, possibly by implementing whitelisting or more sophisticated intrusion detection systems.
Give another variant with minimized false positives
Minimizing false positives in detecting SQL injection using regex is challenging, as legitimate inputs can often resemble malicious ones. However, a more focused regex pattern might help reduce false positives by concentrating on suspicious and uncommon SQL syntax or constructs that are less likely to appear in legitimate requests.

Here’s an alternative regex pattern designed to minimize false positives while still capturing common SQL injection signatures:

(?i)(union\s+select|select\s+.\s+from|insert\s+into\s+\w+\s+(|update\s+\w+\s+set\s+\w+\s=|delete\s+from\s+\w+|drop\s+(table|database)|exec\s+\w+|sleep(\d+)|benchmark(\d+,\s*\w+)|or\s+1\s*=\s1|and\s+1\s=\s0|;\s–|;\s*#)


### Explanation:

1. (?i): Case-insensitive matching.

2. union\s+select:
- Targets the UNION SELECT injection pattern which is commonly used to concatenate results from different queries.

3. select\s+.*\s+from:
- Looks for typical data retrieval patterns SELECT ... FROM, which can indicate an attempt to extract data, especially in unexpected places within a URL.

4. insert\s+into\s+\w+\s+(:
- Catches INSERT INTO statements indicating an attempt to insert data into a specific table, especially when followed by a column list (.

5. update\s+\w+\s+set\s+\w+\s=:
- Searches for UPDATE ... SET ... = constructs used to modify existing database entries, often used in SQL injection to change data.

6. delete\s+from\s+\w+:
- Identifies DELETE FROM statements targeting specific tables, a potentially destructive operation.

7. drop\s+(table|database):
- Matches schema-altering commands DROP TABLE or DROP DATABASE, which could be used to destroy data.

8. exec\s+\w+:
- Captures executions of stored procedures or commands via EXEC, which might include malicious intent.

9. sleep(\d+)|benchmark(\d+,\s\w+):
- Detects time-based attack methods using SLEEP() or BENCHMARK(), often used to infer data through timing analysis.

10. or\s+1\s=\s1|and\s+1\s=\s*0*:
- Matches common tautologies (OR 1=1) and contradictions (AND 1=0) used to manipulate query logic.

11. ;\s–|;\s#**:
- Detects use of comment markers following semicolons, aiming to end a SQL statement prematurely or ignore parts of it.

### Important Considerations:

- Layered Security: Use this regex pattern as part of a comprehensive security strategy, which includes input validation, prepared statements, and contextual escaping to prevent SQL injections.

- Monitoring and Adjustment: Continuously monitor and adjust your detection mechanisms to address new methods of attack and false positives that you might encounter.

- Use Case: Tailor the regex according to the specific application’s context and the database technology in use to better fit the typical data you expect and manage exceptions appropriately.

This pattern might still generate some false positives but aims to reduce them by focusing on less likely legitimate constructs. It’s important to test thoroughly in the context of your application and adjust as necessary.
Basing on your regular expressions make Query DSL Elasticsearch rule using regexp and wildcards to detect SQL injections. Take attention only for non-legitimate requests that damage or steal sensetive data. Use the information above
Creating a Query DSL rule in Elasticsearch to detect SQL injection attempts involves constructing a query using regular expressions and wildcards to match potentially harmful patterns within the logs. Elasticsearch allows you to search your data with powerful full-text search features, including regular expression queries and wildcard searches.

Given the previously discussed regex patterns, here’s how you can construct an Elasticsearch query using Query DSL to attempt to match these patterns:

{
  “query”: {
    “bool”: {
      “should”: [
        {
          “regexp”: {
            “request_url”: {
              “value”: “.(?i)(union\s+select|select\s+.\s+from|insert\s+into\s+\w+\s+\(|update\s+\w+\s+set\s+\w+\s*=|delete\s+from\s+\w+|drop\s+(table|database)|exec\s+\w+|sleep\(\d+\)|benchmark\(\d+,\s*\w+\)|or\s+1\s*=\s1|and\s+1\s=\s0|;\s–|;\s*#).",
              “flags”: “CASE_INSENSITIVE”
            }
          }
        },
        {
          “wildcard”: {
            “request_url”: "%27*%22*%3b*%2f*%2d%2d*%7c*”
          }
        }
      ],
      “minimum_should_match”: 1
    }
  }
}


### Explanation:

- bool: The bool query allows for the combination of different criteria. In this case, we use it to combine regular expression matches with wildcard matches.

- should: A should clause means that at least one of its clauses should match. This provides flexibility in pattern matching.

- regexp: The regexp field allows for matching regular expressions. It checks for the presence of SQL injection patterns in the request_url.

- wildcard: The wildcard query uses wildcard patterns to match strings, being less expressive than regex but sometimes useful for capturing URL-encoded patterns. Here, it searches for suspicious URL-encoded patterns (%27 for single quote, %22 for double quote, etc.).

- flags: The flags option in regexp is used to enable case-insensitive matching.

- minimum_should_match: This option specifies that at least one of the conditions in the should array must match.

### Notes:

- Performance Considerations: Regular expressions and wildcards can be resource-intensive. Ensure this query is tested against the performance capabilities of your Elasticsearch cluster.

- Field Availability: Ensure that the request_url field is available and properly indexed in your Elasticsearch data. You might need to adjust the field name based on the structure of your logs.

- Customization: Feel free to adapt the regular expressions and wildcards according to your specific dataset and security requirements.

Remember that detecting SQL injections purely based on request patterns in logs can be prone to false positives and might require tuning based on observed traffic patterns. Testing in a safe environment and iterative refinement are keys to improving detection accuracy.
Don’t do it in one line. Optimize it and make logically structurized Query DSL with wildcards and regexp for some situations to detect SQL injections in URL
To create a more structured and logically organized Elasticsearch Query DSL for detecting SQL injection patterns in URLs, you can break the query into distinct components using both wildcard and regexp queries. This approach allows for a modular strategy where specific patterns are targeted. Here’s how you can structure such a query:

{
  “query”: {
    “bool”: {
      “should”: [
        {
          “regexp”: {
            “request_url”: {
              “value”: “.\b(union\s+select|select\s+.\bfrom|insert\s+into\s+\w+\s*\(|update\s+\w+\s+set\s+\w+\s*=|delete\s+from\s+\w+|drop\s+(table|database)|exec\s+\w+|sleep\(\d+\)|benchmark\(\d+\s*,\s*\w+\))\b.",
              “flags”: “CASE_INSENSITIVE”
            }
          }
        },
        {
          “regexp”: {
            “request_url”: {
              “value”: ".\b(or\s+1\s*=\s1|and\s+1\s=\s0)\b.”,
              “flags”: “CASE_INSENSITIVE”
            }
          }
        },
        {
          “wildcard”: {
            “request_url”: “;–"
          }
        },
        {
          “wildcard”: {
            “request_url”: "%27*%22*%3b*%2f*%2d%2d*%7c*”
          }
        }
      ],
      “minimum_should_match”: 1
    }
  }
}

Please make your QueryDSL to detect SQL injeciton more logically structurized. Add must and should blocks. Rewrite long regexp as many smaller regexp or wildcards