Cyber Security Attacks Dataset

The Cyber Security Attacks Dataset provides structured data for analyzing different aspects of cybersecurity events. The dataset was created by Team Incribo and contains simulated security activity designed to represent realistic cybersecurity scenarios.

The dataset contains 40,000 records and 25 metrics. These records cover information such as timestamps, source and destination network addresses, ports, protocols, packet characteristics, traffic types, attack types, severity levels, and security alerts.

As a result, the dataset can support exploratory analysis, cybersecurity dashboards, machine learning experiments, and research into attack detection.

Key Features

• 40,000 cybersecurity records
• 25 metrics covering security and network activity
• Synthetic cybersecurity data
• Network traffic information
• Source and destination IP addresses
• Source and destination ports
• Network protocols
• Packet and traffic characteristics
• Attack type information
• Severity levels
• Malware indicators
• Security alerts and warnings
• Firewall and IDS/IPS information

Data Included

The dataset contains a broad range of cybersecurity and network-related attributes. Key fields include:

• Timestamp – Time associated with the network activity
• Source IP Address – Source network address
• Destination IP Address – Destination network address
• Source Port – Source communication port
• Destination Port – Destination communication port
• Protocol – Network communication protocol
• Packet Length – Size of the network packet
• Packet Type – Type of packet
• Traffic Type – Type of network traffic
• Payload Data – Data carried by network traffic
• Malware Indicators – Indicators associated with malicious activity
• Anomaly Scores – Scores representing unusual activity
• Alerts/Warnings – Security alerts associated with events
• Attack Type – Classification of the attack
• Attack Signature – Identifying characteristics of an attack
• Action Taken – Security response or action
• Severity Level – Severity associated with the security event
• User Information – User-related security information
• Device Information – Information about the involved device
• Network Segment – Network segment associated with the event
• Geo-location Data – Geographic information
• Proxy Information – Proxy-related information
• Firewall Logs – Firewall event information
• IDS/IPS Alerts – Intrusion detection and prevention alerts
• Log Source – Source of the security log

These fields make the dataset useful for analyzing relationships between network behavior, security alerts, attack characteristics, and response actions.

Cybersecurity Analysis Applications

The dataset can support a range of cybersecurity and data science applications. For example, researchers can analyze network activity to identify patterns associated with different attack types.

It can also support:

• Cyberattack analysis
• Intrusion detection research
• Network traffic analysis
• Anomaly detection
• Malware-related analysis
• Attack classification
• Security event analysis
• Cybersecurity dashboards
• Machine learning
• Deep learning
• Exploratory data analysis
• Security analytics

Furthermore, the Attack Type field can support multiclass classification experiments, while network and security attributes can provide features for developing attack-detection models.

Machine Learning Applications

The Cyber Security Attacks Dataset can support supervised machine learning experiments after appropriate preprocessing and feature engineering.

Researchers can explore tasks such as:

• Attack type classification
• Anomaly detection
• Security event classification
• Severity prediction
• Network traffic analysis
• Malware detection research
• Intrusion detection
• Feature importance analysis

For instance, researchers have used the dataset for cybersecurity attack classification with DDoS, Intrusion, and Malware as attack-type classes.

Why Use the Cyber Security Attacks Dataset?

Cybersecurity systems generate large amounts of network and security data. Therefore, structured datasets can help researchers study attack patterns and evaluate analytical approaches.

This dataset combines network-level information with security-related fields such as malware indicators, attack signatures, severity, firewall logs, and IDS/IPS alerts. As a result, it provides a broad foundation for cybersecurity analytics and machine learning experiments.

Who Can Use This Dataset?

The dataset can benefit:

• Cybersecurity researchers
• Data scientists
• Machine learning engineers
• Network security professionals
• Cybersecurity students
• AI researchers
• Security analysts
• Developers building intrusion detection systems
• Researchers working on anomaly detection

Conclusion

The Cyber Security Attacks Dataset provides 40,000 synthetic cybersecurity records with 25 metrics covering network activity, attack characteristics, security alerts, and system information.

Its combination of network and security attributes makes it useful for cybersecurity analysis, intrusion detection, anomaly detection, attack classification, and machine learning research. Researchers and developers can use the dataset to explore cybersecurity patterns and develop analytical or AI-based security solutions.

This dataset is sourced from kaggle