Hybrid evolutionary machine learning model for advanced intrusion detection architecture for cyber threat identification.

ABSTRACT: In response to the rapidly evolving threat landscape in network security, this paper proposes an Evolutionary Machine Learning Algorithm designed for robust intrusion detection. We specifically address challenges such as adaptability to new threats and scalability across diverse network environments. Our approach is validated using two distinct datasets: BoT-IoT, reflecting a range of IoT-specific attacks, and UNSW-NB15, offering a broader context of network intrusion scenarios using GA based hybrid DT-SVM. This selection facilitates a comprehensive evaluation of the algorithm's effectiveness across varying attack vectors. Performance metrics including accuracy, recall, and false positive rates are meticulously chosen to demonstrate the algorithm's capability to accurately identify and ada

SUBMITTER: Sharma A 

PROVIDER: S-EPMC11392230 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature