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Leveraging Artificial Intelligence to Enhance Data Security and Combat Cyber Attacks

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  • Yijie Weng

  • Jianhao Wu

Abstract

This research paper examines the potential of artificial intelligence (AI) in strengthening data security and mitigating the growing threat of cyber-attacks. As digital threats continue to evolve and pose significant risks to businesses, organizations, government agencies, and individual users, there is an urgent need for more robust and adaptive security measures. This study explores how AI can be leveraged to enhance network and data security, focusing on its applications in threat detection, response automation, and predictive analysis. Through a comprehensive literature review and analysis of current AI-driven security solutions, this research aims to provide insights into the effectiveness of AI in cybersecurity and propose strategies for its implementation. The findings suggest that AI has the potential to significantly improve cybersecurity measures, offering faster threat detection, more accurate risk assessment, and enhanced response capabilities. However, challenges related to AI implementation, data privacy, and the need for human oversight are also addressed. This research contributes to the growing body of knowledge on AI applications in cybersecurity and provides valuable recommendations for organizations seeking to strengthen their security posture in an increasingly complex digital landscape.

Suggested Citation

  • Yijie Weng & Jianhao Wu, 2024. "Leveraging Artificial Intelligence to Enhance Data Security and Combat Cyber Attacks," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 5(1), pages 392-399.
  • Handle: RePEc:das:njaigs:v:5:y:2024:i:1:p:392-399:id:211
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    References listed on IDEAS

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    1. Peng Zhao & Keqin Li & Bo Hong & Armando Zhu & Jiabei Liu & Shuying Dai, 2024. "Task allocation planning based on hierarchical task network for national economic mobilization," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 5(1), pages 22-31.
    2. Shuying Dai & Keqin Li & Zhuolun Luo & Peng Zhao & Bo Hong & Armando Zhu & Jiabei Liu, 2024. "AI-based NLP section discusses the application and effect of bag-of-words models and TF-IDF in NLP tasks," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 5(1), pages 13-21.
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