Create codeql.yml

.github/workflows/codeql.yml

@@ -0,0 +1,90 @@
+# For most projects, this workflow file will not need changing; you simply need
+# to commit it to your repository.
+#
+# You may wish to alter this file to override the set of languages analyzed,
+# or to provide custom queries or build logic.
+#
+# ******** NOTE ********
+# We have attempted to detect the languages in your repository. Please check
+# the `language` matrix defined below to confirm you have the correct set of
+# supported CodeQL languages.
+#
+name: "CodeQL Advanced"
+
+on:
+  push:
+    branches: [ "master" ]
+  pull_request:
+    branches: [ "master" ]
+  schedule:
+    - cron: '16 10 * * 0'
+
+jobs:
+  analyze:
+    name: Analyze (${{ matrix.language }})
+    # Runner size impacts CodeQL analysis time. To learn more, please see:
+    #   - https://gh.io/recommended-hardware-resources-for-running-codeql
+    #   - https://gh.io/supported-runners-and-hardware-resources
+    #   - https://gh.io/using-larger-runners (GitHub.com only)
+    # Consider using larger runners or machines with greater resources for possible analysis time improvements.
+    runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }}
+    permissions:
+      # required for all workflows
+      security-events: write
+
+      # required to fetch internal or private CodeQL packs
+      packages: read
+
+      # only required for workflows in private repositories
+      actions: read
+      contents: read
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+        # CodeQL supports the following values keywords for 'language': 'c-cpp', 'csharp', 'go', 'java-kotlin', 'javascript-typescript', 'python', 'ruby', 'swift'
+        # Use `c-cpp` to analyze code written in C, C++ or both
+        # Use 'java-kotlin' to analyze code written in Java, Kotlin or both
+        # Use 'javascript-typescript' to analyze code written in JavaScript, TypeScript or both
+        # To learn more about changing the languages that are analyzed or customizing the build mode for your analysis,
+        # see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/customizing-your-advanced-setup-for-code-scanning.
+        # If you are analyzing a compiled language, you can modify the 'build-mode' for that language to customize how
+        # your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+
+    # Initializes the CodeQL tools for scanning.
+    - name: Initialize CodeQL
+      uses: github/codeql-action/init@v3
+      with:
+        languages: ${{ matrix.language }}
+        build-mode: ${{ matrix.build-mode }}
+        # If you wish to specify custom queries, you can do so here or in a config file.
+        # By default, queries listed here will override any specified in a config file.
+        # Prefix the list here with "+" to use these queries and those in the config file.
+
+        # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
+        # queries: security-extended,security-and-quality
+
+    # If the analyze step fails for one of the languages you are analyzing with
+    # "We were unable to automatically build your code", modify the matrix above
+    # to set the build mode to "manual" for that language. Then modify this step
+    # to build your code.
+    # ℹ️ Command-line programs to run using the OS shell.
+    # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
+    - if: matrix.build-mode == 'manual'
+      shell: bash
+      run: |
+        echo 'If you are using a "manual" build mode for one or more of the' \
+          'languages you are analyzing, replace this with the commands to build' \
+          'your code, for example:'
+        echo '  make bootstrap'
+        echo '  make release'
+        exit 1
+
+    - name: Perform CodeQL Analysis
+      uses: github/codeql-action/analyze@v3
+      with:
+        category: "/language:${{matrix.language}}"

CODE_OF_CONDUCT.md

@@ -1,4 +1,186 @@
-## Code of Conduct
-This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct).
-For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact
-[email protected] with any additional questions or comments.
+Lexxy2422
+
+SIEM integrations with enterprise-grade logical 
+SIEM-specific configurations for enhanced detection and automated incident response. Below, I’ll outline a complete architecture for high-security environments, using **Elastic Stack**, **Splunk**, and **QRadar** with complex detection rules and multi-stage attack pattern matching.
+
+### **1. Advanced SIEM Detection: Multi-Stage Attack Detection using MITRE ATT&CK**
+This solution monitors for a sequence of behaviors based on the MITRE ATT&CK framework. For example, it can detect a potential attack chain comprising `Initial Access → Privilege Escalation → Lateral Movement
+
+Python + Splunk Implementation: Multi-Stage Detection with Correlation Analysis**
+
+Features:
+- Correlates multiple suspicious events across different logs.
+- Uses state-based detection (maintains attack context).
+- Sends alerts to SIEM or triggers automated playbooks.
+
+python
+import requests
+import json
+import time
+from datetime import datetime, timedelta
+
+ Splunk Configuration
+splunk_url = "https://splunk-server:8089/services/search/jobs"
+splunk_token = "YOUR_SPLUNK_API_TOKEN"
+headers = {"Authorization": f"Bearer {splunk_token}"}
+
+MITRE ATT&CK Stages
+attack_chain = {
+    "Initial Access": {"query": "search index=main sourcetype=windows:security EventCode=4625", "state": False, "timestamp": None},
+    "Privilege Escalation": {"query": "search index=main sourcetype=windows:security EventCode=4672", "state": False, "timestamp": None},
+    "Lateral Movement": {"query": "search index=main sourcetype=windows:security EventCode=4624", "state": False, "timestamp": None}
+}
+
+ Define the correlation window (e.g., 30 mins)
+correlation_window = timedelta(minutes=30)
+
+Function to execute Splunk queries
+def splunk_query(query):
+    payload = {"search": query, "output_mode": "json"}
+    response = requests.post(splunk_url, headers=headers, data=payload, verify=False)
+    if response.status_code == 201:
+        job_id = response.json()["sid"]
+        return job_id
+    else:
+        print(f"[!] Splunk query failed: {response.text}")
+        return None
+
+Function to check for attack stages in the logs
+def check_attack_stage(stage):
+    job_id = splunk_query(attack_chain[stage]["query"])
+    if job_id:
+        # Check job status
+        status_url = f"{splunk_url}/{job_id}/results"
+        time.sleep(5)  # Allow some time for Splunk to process the query
+        response = requests.get(status_url, headers=headers, verify=False)
+        if response.status_code == 200:
+            results = response.json()["results"]
+            if results:
+                attack_chain[stage]["state"] = True
+                attack_chain[stage]["timestamp"] = datetime.utcnow()
+                print(f"[+] Stage '{stage}' detected at {attack_chain[stage]['timestamp']}.")
+        else:
+            print(f"[!] Failed to get results for job {job_id}")
+    else:
+        print("[!] No job ID returned for the query.")
+
+Function to correlate multi-stage attacks
+def correlate_attack_chain():
+    print("[*] Correlating attack stages based on MITRE ATT&CK framework...")
+    initial_access = attack_chain["Initial Access"]["timestamp"]
+    privilege_escalation = attack_chain["Privilege Escalation"]["timestamp"]
+    lateral_movement = attack_chain["Lateral Movement"]["timestamp"]
+
+    if all([initial_access, privilege_escalation, lateral_movement]):
+        if (privilege_escalation - initial_access) <= correlation_window and (lateral_movement - privilege_escalation) <= correlation_window:
+            print("[!] Multi-stage attack detected! Possible APT detected.")
+            # Trigger alert or automated response
+            alert_payload = {
+                "time": str(datetime.utcnow().timestamp()),
+                "event": "Multi-stage attack detected. Potential APT activity.",
+                "attack_chain": attack_chain
+            }
+            requests.post(splunk_url, headers=headers, data=json.dumps(alert_payload))
+            reset_attack_chain()  # Reset after detection
+        else:
+            print("[*] No correlation found within the given time window.")
+    else:
+        print("[*] Waiting for all attack stages to be detected...")
+
+Function to reset the attack chain state
+def reset_attack_chain():
+    for stage in attack_chain:
+        attack_chain[stage]["state"] = False
+        attack_chain[stage]["timestamp"] = None
+
+Main logic loop to periodically check each stage
+while True:
+    for stage in attack_chain:
+        if not attack_chain[stage]["state"]:
+            check_attack_stage(stage)
+    correlate_attack_chain()
+    time.sleep(60)  # Run the detection loop every 60 seconds
+
+
+Key Points:
+1. Correlates Events : Matches behavior patterns across different events (e.g., failed logins, privilege escalation).
+2. Alerts on Complete Chain: Only triggers when all stages are met, reducing false positives.
+3. Splunk Automation: Sends alerts to Splunk’s alerting module or triggers response actions.
+
+
+2. Advanced Network Threat Detection with ML Integration (Python + Zeek + Scikit-Learn)
+Use machine learning to analyze network traffic logs for anomalies that traditional rules might miss. This script uses Zeek’s network logs and Scikit-Learn’s anomaly detection models.
+
+Features:
+- Detects anomalous network patterns (e.g., data exfiltration).
+- Uses unsupervised learning to adapt to changing environments.
+- Can automatically tune its model based on new data.
+
+python
+import os
+import json
+import time
+import joblib
+import numpy as np
+from sklearn.ensemble import IsolationForest
+from elasticsearch import Elasticsearch
+
+# Define paths and files
+zeek_log_dir = "/opt/zeek/logs/current"
+http_log_file = os.path.join(zeek_log_dir, "http.log")
+
+ElasticSearch Configuration
+es = Elasticsearch(["https://elastic-server:9200"], http_auth=('elastic', 'password'), verify_certs=False)
+index_name = "network-anomalies"
+
+Load pre-trained ML model or train a new one
+model_path = "/opt/siem/ml_model/isolation_forest.joblib"
+if os.path.exists(model_path):
+    print("[*] Loading pre-trained anomaly detection model...")
+    model = joblib.load(model_path)
+else:
+    print("[*] Training a new anomaly detection model...")
+    model = IsolationForest(contamination=0.01)  # Set contamination rate for anomalies
+
+Function to extract network features from Zeek logs
+def extract_features(http_log):
+    features = []
+    # Example features: Request size, response size, duration
+    features.append(int(http_log.get("request_body_len", 0)))
+    features.append(int(http_log.get("response_body_len", 0)))
+    features.append(float(http_log.get("duration", 0.0)))
+    return np.array(features).reshape(1, -1)
+
+Monitor Zeek logs for new entries
+def detect_anomalies():
+    print("[*] Monitoring Zeek HTTP logs for anomalies...")
+    with open(http_log_file, "r") as log_file:
+        for line in log_file:
+            try:
+                # Parse JSON-formatted log entries
+                http_log = json.loads(line)
+                features = extract_features(http_log)
+                # Predict if the current log is an anomaly
+                prediction = model.predict(features)
+                if prediction == -1:  # Anomalous entry
+                    print(f"[!] Anomalous traffic detected: {http_log}")
+                    es.index(index=index_name, body=http_log)  # Send alert to Elastic
+            except json.JSONDecodeError:
+                continue
+
+Start monitoring for network anomalies
+detect_anomalies()
+
+
+Key Points :
+1. ML-Based Detection : Uses IsolationForest to flag outliers in network behavior.
+2. Feature Engineering: Extracts specific features like request size, response size, and duration.
+3. SIEM Integration : Pushes alerts to Elastic for correlation with other data sources.
+
+Next Steps:
+- Refine Models: Use a larger dataset to train more sophisticated models (e.g., clustering or deep learning).
+- Expand MITRE ATT&CK Coverage: Integrate other ATT&CK techniques for comprehensive detection.
+- SIEM Orchestration: Add automation playbooks for response (e.g., quarantine devices).
+
+Need further enhancements or integration? Let me know which area to drill into next!
+