{
  "format": "dataset-manifest/1",
  "slug": "cybersecurity-support-insights",
  "version": 4,
  "seed": "cybersecurity-support-insights:1",
  "engine": 1,
  "engineVersion": "1.0.0",
  "spec": {
    "format": "dataset/1",
    "slug": "cybersecurity-support-insights",
    "name": "Cybersecurity Support Insights",
    "tagline": "User interactions, scan results, blocked websites, vulnerabilities, and network events in cybersecurity support.",
    "description": "Support teams meticulously monitor user interactions, ensuring protection against online threats while enhancing the overall software experience. This dataset captures a variety of interactions in the cybersecurity domain, detailing results from scans, records of blocked websites, assessments of vulnerabilities, and logs of network events. Analysts can leverage this information to track the frequency of threats faced by users, the effectiveness of protective measures deployed, and the devices and VPN servers that users frequently engage with. \n\nIn the 'users' table, each row represents an individual user along with their unique identifiers and relevant attributes. The 'scans' table logs details of security scans performed on users' devices, linking back through user IDs to show which individual underwent each assessment. 'blocked_websites' connects to both users and scans, documenting instances of websites that were denied access due to security protocols. Meanwhile, the 'vulnerabilities' table provides an in-depth look at specific security flaws associated with users' devices, creating insights into how often significant vulnerabilities are encountered. Lastly, 'network_events' table details the various network activities, including connection attempts and alerts—allowing analysts to discern patterns in user behavior and security responses. \n\nThis data answers a multitude of questions regarding user safety, threat management, and overall software performance. It is an excellent resource for practicing analysis techniques, such as identifying trends in cybersecurity incidents or evaluating the effectiveness of response strategies. The interactions captured within this dataset are synthetic, providing a rich environment for developers to refine their data analysis skills.",
    "domain": "customer-support",
    "shape": "slowly-changing",
    "useCases": [
      "etl-pipelines",
      "teaching"
    ],
    "keywords": [
      "cybersecurity",
      "support",
      "user interactions",
      "scan results",
      "vulnerabilities",
      "network events",
      "threat management"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "users": {
        "description": "This table captures user information, including their unique IDs and device types. Each user can have multiple interactions logged in the system.",
        "rows": 200,
        "columns": {
          "user_id": {
            "kind": "pk",
            "description": "A unique identifier for each user."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The full name of the user."
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "The email address associated with the user."
          },
          "device_type": {
            "kind": "enum",
            "values": [
              "desktop",
              "mobile",
              "tablet"
            ],
            "description": "The type of device used by the user.",
            "weights": [
              6,
              3,
              1
            ]
          }
        }
      },
      "scans": {
        "description": "This table records the results of scans conducted for each user, detailing the number of threats detected and the date of the scan.",
        "rows": 400,
        "columns": {
          "scan_id": {
            "kind": "pk",
            "description": "A unique identifier for each scan."
          },
          "user_id": {
            "kind": "fk",
            "ref": "users.user_id",
            "description": "The user associated with this scan."
          },
          "scan_date": {
            "kind": "datetime",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "The date and time when the scan was conducted.",
            "dayProfile": "business"
          },
          "threats_detected": {
            "kind": "int",
            "min": 0,
            "max": 50,
            "description": "The number of threats detected during this scan.",
            "distribution": "long-tail"
          },
          "scan_duration_minutes": {
            "kind": "int",
            "min": 5,
            "max": 120,
            "distribution": "normal",
            "description": "Duration of the scan in minutes."
          },
          "threat_level": {
            "kind": "enum",
            "values": [
              "low",
              "medium",
              "high"
            ],
            "weights": [
              50,
              30,
              20
            ],
            "description": "Overall threat level based on threats detected."
          }
        }
      },
      "blocked_websites": {
        "description": "This table contains information about websites that were blocked for users during scans, along with reasons for blocking.",
        "rows": 300,
        "columns": {
          "block_id": {
            "kind": "pk",
            "description": "A unique identifier for each blocked website entry."
          },
          "scan_id": {
            "kind": "fk",
            "ref": "scans.scan_id",
            "description": "The scan during which the website was blocked."
          },
          "website": {
            "kind": "url",
            "description": "The URL of the blocked website."
          },
          "block_reason": {
            "kind": "enum",
            "values": [
              "malware",
              "phishing",
              "spam"
            ],
            "description": "The reason for blocking the website.",
            "weights": [
              6,
              2,
              1
            ]
          },
          "block_timestamp": {
            "kind": "datetime",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "The exact timestamp when the website was blocked."
          },
          "user_feedback": {
            "kind": "enum",
            "values": [
              "helpful",
              "not helpful"
            ],
            "weights": [
              70,
              30
            ],
            "nullable": true,
            "description": "User feedback on the blocking action."
          }
        }
      },
      "vulnerabilities": {
        "description": "This table captures significant vulnerabilities identified during scans, detailing their severity and resolution status.",
        "rows": 250,
        "columns": {
          "vulnerability_id": {
            "kind": "pk",
            "description": "A unique identifier for each vulnerability."
          },
          "scan_id": {
            "kind": "fk",
            "ref": "scans.scan_id",
            "description": "The scan during which the vulnerability was identified."
          },
          "severity": {
            "kind": "enum",
            "values": [
              "low",
              "medium",
              "high"
            ],
            "description": "The severity level of the vulnerability.",
            "weights": [
              5,
              3,
              1
            ]
          },
          "status": {
            "kind": "enum",
            "values": [
              "open",
              "resolved"
            ],
            "description": "The current resolution status of the vulnerability.",
            "weights": [
              7,
              3
            ]
          },
          "discovery_date": {
            "kind": "datetime",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "The date when the vulnerability was discovered."
          },
          "resolution_time_days": {
            "kind": "int",
            "min": 0,
            "max": 30,
            "distribution": "normal",
            "nullable": true,
            "description": "Time taken to resolve the vulnerability in days."
          }
        }
      },
      "network_events": {
        "description": "This table logs network events related to user activity, detailing connection attempts and VPN usage.",
        "rows": 300,
        "columns": {
          "event_id": {
            "kind": "pk",
            "description": "A unique identifier for each network event."
          },
          "user_id": {
            "kind": "fk",
            "ref": "users.user_id",
            "description": "The user associated with this network event."
          },
          "event_date": {
            "kind": "datetime",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "The date and time when the network event occurred.",
            "dayProfile": "waking",
            "trend": "growing"
          },
          "vpn_used": {
            "kind": "bool",
            "trueRate": 0.6,
            "description": "Whether a VPN was used during the event."
          },
          "connection_type": {
            "kind": "enum",
            "values": [
              "wired",
              "wireless"
            ],
            "weights": [
              60,
              40
            ],
            "description": "Type of connection used during the event."
          },
          "data_transferred_mb": {
            "kind": "decimal",
            "min": 0,
            "max": 500,
            "scale": 2,
            "distribution": "heavy-tail",
            "description": "Amount of data transferred during the event in megabytes."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "timestamped",
      "nullable",
      "categorical"
    ],
    "analysisQuestions": [
      "What is the average number of threats detected per user scan?",
      "Which devices are most commonly used by users?",
      "How do the severity levels of vulnerabilities correlate with their resolution statuses?",
      "Which websites are most frequently blocked and for what reasons?",
      "What percentage of users utilize a VPN during network events?"
    ],
    "suggestedQueries": [
      {
        "title": "Join users and scans",
        "sql": "SELECT users.full_name, scans.scan_date, scans.threats_detected FROM users JOIN scans ON users.user_id = scans.user_id;",
        "dialect": "postgres"
      },
      {
        "title": "Count blocked websites per scan",
        "sql": "SELECT scan_id, COUNT(*) AS blocked_count FROM blocked_websites GROUP BY scan_id;",
        "dialect": "postgres"
      },
      {
        "title": "Average threats detected by device type",
        "sql": "SELECT users.device_type, AVG(scans.threats_detected) AS average_threats FROM scans JOIN users ON scans.user_id = users.user_id GROUP BY users.device_type;",
        "dialect": "postgres"
      },
      {
        "title": "Vulnerability resolution status by severity",
        "sql": "SELECT severity, COUNT(*) AS count, SUM(CASE WHEN status = 'resolved' THEN 1 ELSE 0 END) AS resolved_count FROM vulnerabilities GROUP BY severity;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Data Analysts Exploring Threat Trends",
        "body": "Data analysts in cybersecurity firms can utilize this dataset to explore trends in detected threats over time. By analyzing the frequency and severity of vulnerabilities, they can provide insights into areas of improvement for the software, helping to enhance user safety and experience."
      },
      {
        "title": "Teaching Data Analytics in Cybersecurity",
        "body": "Educators can leverage this dataset to teach students about data analytics in the context of cybersecurity. It provides a practical example of how to analyze user interactions and threat management, allowing students to practice SQL queries and understand the importance of cybersecurity."
      },
      {
        "title": "Support Teams Reviewing Performance",
        "body": "Support teams can review this dataset to assess their performance in managing user threats and vulnerabilities. By analyzing the number of blocked websites and the effectiveness of scans, they can identify patterns that require more focus or adjustments in their strategies."
      },
      {
        "title": "Product Managers Enhancing Features",
        "body": "Product managers can use this dataset to gather insights that inform feature enhancements in cybersecurity software. By understanding user behavior and the most common threats encountered, they can prioritize development efforts and improve overall product performance."
      }
    ]
  },
  "counts": {
    "users": 200,
    "network_events": 300,
    "scans": 400,
    "blocked_websites": 300,
    "vulnerabilities": 250
  },
  "stats": {
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        "full_name": {
          "nulls": 0,
          "distinct": 196,
          "min": "Alice Novak",
          "max": "Zoe Patel"
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        "email": {
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        "device_type": {
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    "scans": {
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        "website": {
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