{
  "format": "dataset-manifest/1",
  "slug": "government-cybersecurity-operations",
  "version": 2,
  "seed": "government-cybersecurity-operations:1",
  "spec": {
    "format": "dataset/1",
    "slug": "government-cybersecurity-operations",
    "name": "Government Cybersecurity Operations",
    "tagline": "Trusted vendors, evaluations, protected devices, scans, and detected threats in government cybersecurity operations.",
    "description": "Government cybersecurity operations focus on safeguarding sensitive information and infrastructure from various cyber threats, ensuring that critical data remains secure and accessible. Cybersecurity teams assess and manage trusted vendors, conduct routine scans, and respond to any detected threats that may compromise the integrity of their systems. Regular monitoring and patching of devices help maintain security, although legacy systems often present vulnerabilities that can be exploited. The operations also include overseeing protective measures for devices, ensuring that only secure hardware and software are utilized within government networks.\n\nIn this dataset, the `trusted_vendors` table outlines the vendors approved for government contracts, detailing their compliance and the security measures they implement. The `vendor_evaluations` table provides insights into the assessment processes used to determine the reliability and security of these vendors. The `protected_devices` table catalogs the devices that require heightened security measures, while the `routine_scans` table tracks regular security checks performed to ensure compliance and identify potential vulnerabilities. Finally, the `detected_threats` table records any cybersecurity incidents that occur, offering valuable data on the frequency and nature of attacks over time.\n\nRelationships between the tables are key to understanding the operations. For example, each vendor in the `trusted_vendors` table can be linked to their evaluations in the `vendor_evaluations` table, tying their security performance directly to the vendors being assessed. The `protected_devices` table connects with `routine_scans`, indicating which devices are being monitored and scanned regularly. When threats are detected, they can be cross-referenced with the devices and vendors involved, illustrating how vulnerabilities can emerge from specific systems.\n\nAnalysts can use this synthetic dataset to explore various questions about the efficacy of vendor security measures, the frequency of device vulnerabilities, and the correlation between routine scans and detected threats. This practice dataset provides an excellent framework for developing analytical skills in cybersecurity, enabling users to identify trends, assess risk, and understand the complexities of maintaining a secure government infrastructure.",
    "domain": "government",
    "shape": "slowly-changing",
    "useCases": [
      "etl-pipelines",
      "teaching"
    ],
    "keywords": [
      "cybersecurity",
      "government",
      "threat detection",
      "vendor management",
      "device protection"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "trusted_vendors": {
        "description": "This table lists government-approved vendors who provide cybersecurity solutions. Each vendor has an evaluation score based on their performance and trustworthiness.",
        "rows": 100,
        "columns": {
          "vendor_id": {
            "kind": "pk",
            "description": "Unique identifier for each vendor."
          },
          "vendor_name": {
            "kind": "fullName",
            "description": "Name of the vendor."
          },
          "evaluation_score": {
            "kind": "decimal",
            "min": 0,
            "max": 100,
            "description": "Score out of 100 representing the vendor's performance.",
            "distribution": "high"
          }
        }
      },
      "vendor_evaluations": {
        "description": "This table records evaluations of trusted vendors over time, linked to the vendor table by vendor_id. Each evaluation captures the score and feedback for a specific assessment period.",
        "rows": {
          "per": "trusted_vendors",
          "via": "vendor_id",
          "min": 1,
          "max": 3,
          "skew": "long-tail"
        },
        "columns": {
          "evaluation_id": {
            "kind": "pk",
            "description": "Unique identifier for each evaluation."
          },
          "vendor_id": {
            "kind": "fk",
            "ref": "trusted_vendors.vendor_id",
            "description": "Identifier for the vendor being evaluated."
          },
          "evaluation_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date of the evaluation.",
            "trend": "growing"
          },
          "feedback": {
            "kind": "sentence",
            "description": "Textual feedback on the vendor's performance."
          }
        }
      },
      "protected_devices": {
        "description": "This table contains records of devices that are under protection, including their status and the vendor providing the protection.",
        "rows": 300,
        "columns": {
          "device_id": {
            "kind": "pk",
            "description": "Unique identifier for each protected device."
          },
          "device_name": {
            "kind": "productName",
            "description": "Name of the protected device."
          },
          "vendor_id": {
            "kind": "fk",
            "ref": "trusted_vendors.vendor_id",
            "nullable": true,
            "description": "Identifier for the vendor protecting the device."
          },
          "status": {
            "kind": "enum",
            "values": [
              "active",
              "inactive",
              "under_maintenance"
            ],
            "description": "Current status of the device."
          }
        }
      },
      "routine_scans": {
        "description": "This table logs routine scans performed on protected devices, detailing the date and results of each scan.",
        "rows": {
          "per": "protected_devices",
          "via": "device_id",
          "min": 1,
          "max": 5,
          "skew": "heavy-tail"
        },
        "columns": {
          "scan_id": {
            "kind": "pk",
            "description": "Unique identifier for each scan."
          },
          "device_id": {
            "kind": "fk",
            "ref": "protected_devices.device_id",
            "description": "Identifier for the device being scanned."
          },
          "scan_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date of the scan.",
            "trend": "seasonal"
          },
          "scan_result": {
            "kind": "enum",
            "values": [
              "clean",
              "malware_detected"
            ],
            "description": "Result of the scan."
          }
        }
      },
      "detected_threats": {
        "description": "This table records threats detected during routine scans, linking threats to the devices on which they were found.",
        "rows": {
          "per": "protected_devices",
          "via": "device_id",
          "min": 0,
          "max": 10,
          "skew": "long-tail"
        },
        "columns": {
          "threat_id": {
            "kind": "pk",
            "description": "Unique identifier for each detected threat."
          },
          "device_id": {
            "kind": "fk",
            "ref": "protected_devices.device_id",
            "description": "Identifier for the device where the threat was detected."
          },
          "threat_type": {
            "kind": "words",
            "description": "Type of threat detected (e.g., malware, phishing).",
            "pool": [
              "malware",
              "phishing",
              "ransomware",
              "spyware",
              "trojan",
              "worm",
              "adware",
              "botnet",
              "keylogger",
              "exploit",
              "rootkit",
              "virus",
              "scareware",
              "backdoor",
              "credential",
              "flood",
              "DDoS",
              "social",
              "manipulation",
              "insider"
            ],
            "min": 1,
            "max": 3
          },
          "detection_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date when the threat was detected.",
            "trend": "growing"
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "categorical",
      "nullable",
      "timestamped",
      "high-cardinality"
    ],
    "analysisQuestions": [
      "How do vendor evaluations correlate with the frequency of detected threats?",
      "Which types of devices are most commonly targeted by threats?",
      "Are there specific periods when threats spike more significantly?",
      "What is the average evaluation score of trusted vendors over time?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Vendors with Evaluations",
        "sql": "SELECT v.vendor_name, e.evaluation_date, e.evaluation_score FROM trusted_vendors v JOIN vendor_evaluations e ON v.vendor_id = e.vendor_id;",
        "dialect": "postgres"
      },
      {
        "title": "Count Detected Threats by Device",
        "sql": "SELECT d.device_name, COUNT(t.threat_id) AS threat_count FROM protected_devices d LEFT JOIN detected_threats t ON d.device_id = t.device_id GROUP BY d.device_name;",
        "dialect": "postgres"
      },
      {
        "title": "Average Evaluation Score Over Time",
        "sql": "SELECT e.evaluation_date, AVG(e.evaluation_score) AS avg_score FROM vendor_evaluations e GROUP BY e.evaluation_date ORDER BY e.evaluation_date;",
        "dialect": "postgres"
      },
      {
        "title": "Threats Detected During Routine Scans",
        "sql": "SELECT r.scan_date, COUNT(t.threat_id) AS threats_detected FROM routine_scans r LEFT JOIN detected_threats t ON r.device_id = t.device_id GROUP BY r.scan_date;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Cybersecurity Training",
        "body": "In cybersecurity training programs, instructors can use this dataset to teach students about vendor evaluations and the importance of selecting trusted vendors for device protection. By analyzing vendor performance over time, students can understand how evaluations impact overall cybersecurity strategies."
      },
      {
        "title": "Threat Analysis Workshops",
        "body": "During workshops focused on threat analysis, participants can utilize this dataset to identify patterns in detected threats. By examining the types of threats associated with different devices, they can develop strategies to improve threat detection and response measures."
      },
      {
        "title": "ETL Pipeline Development",
        "body": "Data engineers can leverage this dataset to develop ETL pipelines that facilitate the extraction, transformation, and loading of cybersecurity data into analytical systems. The structured nature of the dataset allows for easy integration with data warehouses and BI tools."
      }
    ]
  },
  "counts": {
    "trusted_vendors": 100,
    "protected_devices": 300,
    "detected_threats": 917,
    "routine_scans": 549,
    "vendor_evaluations": 171
  },
  "stats": {
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          "nulls": 0,
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        "evaluation_date": {
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          "distinct": 158,
          "min": "2020-01-18",
          "max": "2023-12-26"
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