ENHANCED CLOUD COMPUTING INTRUSION DETECTION SYSTEM EMPLOYING DIFFERENT CLASSIFIERS
DOI:
https://doi.org/10.65009/kpp7a947Keywords:
Intrusion Detection Systems, Cloud Computing, Deep Learning, NSL, Metrics, etc.,,Abstract
Cloud computing has revolutionized the technical landscape due to its affordability and
scalability. However, it has also resulted in unique security challenges. System Aided Design (SAD) has
emerged as a crucial instrument for addressing security issues specific to cloud environments by
enhancing the classification of these issues. Cloud computing offers cost advantages and flexibility, but
because so much sensitive data is involved, privacy and data security are problems. Intrusion detection
systems (IDSs), although crucial to cloud security, face challenges due to the dynamic nature of the cloud.
The objective of this research project is to develop a cloud-based intrusion detection system (IDS) that
uses neuro-swarm intelligence techniques to efficiently analyze and classify network traffic while
adapting to the always changing cloud environment. This approach seems like a good way to safeguard
data and ensure secure cloud operations. An extensive evaluation of an intrusion detection system (IDS)
that employs G-ABC and DNN approaches has been conducted as part of this research effort.
Additionally, this study assesses the IDS's ability to identify U2R, R2L, and Probes attacks in addition to
the well-known DoS attacks using the NSL KDD and UNSW NB15 datasets. The accuracy, precision,
recall, and F-measure metrics of the investigation show how the IDS may enhance intrusion detection for
various attack types.
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