An optimized ensemble model with advanced feature selection for network intrusion detection.

ABSTRACT: In today's digital era, advancements in technology have led to unparalleled levels of connectivity, but have also brought forth a new wave of cyber threats. Network Intrusion Detection Systems (NIDS) are crucial for ensuring the security and integrity of networked systems by identifying and mitigating unauthorized access and malicious activities. Traditional machine learning techniques have been extensively employed for this purpose due to their high accuracy and low false alarm rates. However, these methods often fall short in detecting sophisticated and evolving threats, particularly those involving subtle variations or mutations of known attack patterns. To address this challenge, our study presents the "Optimized Random Forest (Opt-Forest)," an innovative ensemble model that combines d

SUBMITTER: Ahmed A 

PROVIDER: S-EPMC11623070 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature