The Examining of the Impact of Artificial Intelligence on Threat Detection and Response Systems
DOI:
https://doi.org/10.58445/rars.2142Keywords:
machine learning, Data Analysis, pattern recognition, anomoly detectionAbstract
This paper explores how artificial intelligence (AI) is reshaping cybersecurity, especially in detecting threats and responding to incidents. AI-driven systems use techniques like machine learning to sift through massive amounts of data and spot unusual patterns that might signal an attack, even when these threats are complex and hard for traditional methods to catch. AI also helps reduce false alarms, making it easier for security teams to focus on real issues. In responding to attacks, AI enables fast, automated actions that contain threats before they cause significant harm.
The paper discusses how various AI technologies, including supervised learning, neural networks, and natural language processing, are employed in the field of cybersecurity. It also examines some challenges, such as the necessity for high-quality data and continuous model updates. Additionally, the paper touches on emerging trends like quantum-resistant AI and federated learning, which could potentially enhance cybersecurity even further. The overall message is that AI plays a crucial role in helping organizations stay protected against evolving cyber threats.
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