INCIDENT RESPONSE AUTOMATION
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Zenodo
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
| Publicado: |
Zenodo
2026
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| _version_ | 1866901980137390080 |
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| author | ijesat |
| author_facet | ijesat |
| contents | <p>The increasing frequency and sophistication of cyberattacks have made incident response a critical component of modern cybersecurity strategies. Traditional incident response processes are often manual, time-consuming, and prone to human error, which can delay threat mitigation and increase potential damage. This project proposes an Incident Response Automation System that leverages advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Security Orchestration, Automation, and Response (SOAR) to streamline and enhance the incident handling process. The system aims to detect, analyze, and respond to security incidents in real time with minimal human intervention. The proposed system collects security data from multiple sources such as network logs, system events, intrusion detection systems, and endpoint monitoring tools. This data is processed and analyzed using machine learning algorithms to identify anomalies and potential threats. Once an incident is detected, the system automatically classifies the severity and type of attack. Based on predefined rules and intelligent decisionmaking models, appropriate response actions are triggered, such as isolating affected systems, blocking malicious IP addresses, or notifying security teams. The system also maintains detailed logs for auditing and forensic analysis. The implementation of incident response automation significantly reduces response time, improves accuracy, and minimizes the impact of cyber threats. The system ensures consistency in handling incidents and reduces dependency on manual processes. Additionally, the integration of continuous learning mechanisms allows the system to adapt to evolving attack patterns. Although challenges such as false positives and integration complexity exist, the overall framework provides a scalable and efficient solution for modern cybersecurity environments. This project highlights the importance of automation in strengthening organizational security and ensuring rapid and effective incident management.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19509228 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | INCIDENT RESPONSE AUTOMATION ijesat Incident Response Automation, Cybersecurity, Machine Learning, SOAR, Threat Detection, Security Analytics, Intrusion Detection, Anomaly Detection, Automated Response, Network Security <p>The increasing frequency and sophistication of cyberattacks have made incident response a critical component of modern cybersecurity strategies. Traditional incident response processes are often manual, time-consuming, and prone to human error, which can delay threat mitigation and increase potential damage. This project proposes an Incident Response Automation System that leverages advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Security Orchestration, Automation, and Response (SOAR) to streamline and enhance the incident handling process. The system aims to detect, analyze, and respond to security incidents in real time with minimal human intervention. The proposed system collects security data from multiple sources such as network logs, system events, intrusion detection systems, and endpoint monitoring tools. This data is processed and analyzed using machine learning algorithms to identify anomalies and potential threats. Once an incident is detected, the system automatically classifies the severity and type of attack. Based on predefined rules and intelligent decisionmaking models, appropriate response actions are triggered, such as isolating affected systems, blocking malicious IP addresses, or notifying security teams. The system also maintains detailed logs for auditing and forensic analysis. The implementation of incident response automation significantly reduces response time, improves accuracy, and minimizes the impact of cyber threats. The system ensures consistency in handling incidents and reduces dependency on manual processes. Additionally, the integration of continuous learning mechanisms allows the system to adapt to evolving attack patterns. Although challenges such as false positives and integration complexity exist, the overall framework provides a scalable and efficient solution for modern cybersecurity environments. This project highlights the importance of automation in strengthening organizational security and ensuring rapid and effective incident management.</p> |
| title | INCIDENT RESPONSE AUTOMATION |
| topic | Incident Response Automation, Cybersecurity, Machine Learning, SOAR, Threat Detection, Security Analytics, Intrusion Detection, Anomaly Detection, Automated Response, Network Security |
| url | https://doi.org/10.5281/zenodo.19509228 |