The use of Artificial Intelligence to improve Intrusion Detection and Prevention Systems
Mrs.Vandana Nainesh Chaurasia[1], Assistant Professor in V.K.K. Menon College, Bhandup (East), Mrs.Kalpana Dinesh Bandebuche[2],
Assistant Professor in V.K.K. Menon College, Bhandup (East)
Mr. Shriyans Dinesh Bandebuche[3]
Indala College Of Engineering, Kalyan
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Abstract
With cyber threats evolving constantly, it’s essential to safeguard network systems using Intrusion Detection
and Prevention Systems (IDPS). Traditional IDPS approaches, such as signature and anomaly-based methods,
often fall short when confronting complex attacks like APTs, polymorphic malware, or zero-day threats. One
of the key drawbacks of these methods is their tendency to generate too many false alerts and their limited
adaptability to unknown or subtle threats. One of the key drawbacks of these methods is their tendency to
generate too many false alerts and their limited adaptability to unknown or subtle threats. This paper explores
the use of AI to improve IDPS's proactive response capabilities as well as its detection accuracy. Without
depending exclusively on predetermined signatures, AI-based IDPS can independently learn from vast network
data, spot subtle irregularities, and uncover suspicious patterns linked to cyberattacks. AI-based IDPS can
independently learn from vast network data, spot subtle irregularities, and uncover suspicious patterns linked
to cyberattacks. We also go over the difficulties in putting AI-driven IDPS into practice, including the
requirement for huge datasets, problems with interpretability, and the possibility of adversarial assaults on AI
models. In conclusion, we suggest avenues for further research, such as hybrid approaches integrating AI with
conventional techniques, employing AI for threat intelligence prediction, and creating more transparent and
comprehensible AI models for cybersecurity.
The results of this study demonstrate the revolutionary potential of AI in creating IDPS that are more
intelligent, adaptable, and resilient and that can successfully counteract the ever-changing nature of modern
cyber threats.
Keywords: Artificial Intelligence (AI), Intrusion Detection and Prevention Systems (IDPS), Machine
Learning (ML), Deep Learning (DL), Cybersecurity, Anomaly Detection.
