Location
Comstock Memorial Union, MSUM
Document Type
Poster
Event Website
https://www.mnstate.edu/sac/
Start Date
15-4-2025 12:00 AM
End Date
15-4-2025 12:00 AM
Publication Date
4-15-2025
Description
Background: With the rapid advancement of technology, cybersecurity threats have become more sophisticated, making traditional security measures less effective. The increasing volume of data generated in digital environments makes it difficult to monitor and detect attacks in real time. As cyber threats evolve, there is a growing need for more advanced security solutions capable of identifying and mitigating threats proactively. Research Purpose: This study explores the role of Artificial Intelligence (AI) in threat detection and response, focusing on its ability to enhance cybersecurity by addressing the limitations of traditional security approaches. The research aims to analyze how AI-driven systems improve threat identification, automate responses, and reduce the impact of cyberattacks. Methods: The study involves a comprehensive review of existing AI-based security models, including machine learning techniques, anomaly detection, and behavioral analysis. Case studies and real-world applications of AI in cybersecurity are examined to assess their effectiveness in detecting and mitigating cyber threats. Findings: The research indicates that AI-driven threat detection significantly enhances cybersecurity by processing large datasets, identifying patterns, and detecting anomalies in real time. Machine learning models improve over time, allowing for adaptive security measures against evolving threats. Automated response systems reduce reaction time, minimizing damage from cyberattacks. Conclusion: AI has emerged as a crucial tool in modern cybersecurity, offering a proactive approach to threat detection and response. Its ability to analyze vast amounts of data, detect anomalies, and automate security processes makes it superior to traditional security methods. Future research should focus on refining AI models to enhance accuracy, reduce false positives, and address ethical concerns related to AI-driven security solutions.
AI for Threat Detection and Response
Comstock Memorial Union, MSUM
Background: With the rapid advancement of technology, cybersecurity threats have become more sophisticated, making traditional security measures less effective. The increasing volume of data generated in digital environments makes it difficult to monitor and detect attacks in real time. As cyber threats evolve, there is a growing need for more advanced security solutions capable of identifying and mitigating threats proactively. Research Purpose: This study explores the role of Artificial Intelligence (AI) in threat detection and response, focusing on its ability to enhance cybersecurity by addressing the limitations of traditional security approaches. The research aims to analyze how AI-driven systems improve threat identification, automate responses, and reduce the impact of cyberattacks. Methods: The study involves a comprehensive review of existing AI-based security models, including machine learning techniques, anomaly detection, and behavioral analysis. Case studies and real-world applications of AI in cybersecurity are examined to assess their effectiveness in detecting and mitigating cyber threats. Findings: The research indicates that AI-driven threat detection significantly enhances cybersecurity by processing large datasets, identifying patterns, and detecting anomalies in real time. Machine learning models improve over time, allowing for adaptive security measures against evolving threats. Automated response systems reduce reaction time, minimizing damage from cyberattacks. Conclusion: AI has emerged as a crucial tool in modern cybersecurity, offering a proactive approach to threat detection and response. Its ability to analyze vast amounts of data, detect anomalies, and automate security processes makes it superior to traditional security methods. Future research should focus on refining AI models to enhance accuracy, reduce false positives, and address ethical concerns related to AI-driven security solutions.
https://red.mnstate.edu/sac/2025/cbac/7