CyberGuard: A Novel Ensemble Learning Framework for Anomaly-Oriented Intrusion Detection

Abstract

: As cybersecurity challenges grow more intricate, it becomes increasingly difficult to protect contemporary computer systems effectively. Traditional intrusion detection mechanisms depend largely on patterns for effectiveness in detecting known intrusions; however, they fall short when faced with novel or changing security challenges. Despite their ability to identify unusual patterns indicative of unknown threats, anomaly detection methods frequently generate numerous incorrect alerts due to sensitivity issues. In order to address these challenges, this document introduces CyberGuard, an integrated system combining ensemble machine learning techniques with neural network architectures for detecting intrusions. The cybersecurity tool CyberGuard combines various machine learning algorithms with advanced neural networks to improve its ability to identify threats accurately while reducing incorrect alarms. Studies conducted using typical data sets reveal that the suggested approach demonstrates superior performance over conventional techniques, underscoring its viability in handling practical network security threats efficiently.