Integration of simulated annealing into pigeon inspired optimizer algorithm for feature selection in network intrusion detection systems.

ABSTRACT: In the context of the 5G network, the proliferation of access devices results in heightened network traffic and shifts in traffic patterns, and network intrusion detection faces greater challenges. A feature selection algorithm is proposed for network intrusion detection systems that uses an improved binary pigeon-inspired optimizer (SABPIO) algorithm to tackle the challenges posed by the high dimensionality and complexity of network traffic, resulting in complex models, reduced accuracy, and longer detection times. First, the raw dataset is pre-processed by uniquely one-hot encoded and standardized. Next, feature selection is performed using SABPIO, which employs simulated annealing and the population decay factor to identify the most relevant subset of features for subsequent review and

SUBMITTER: Huang W 

PROVIDER: S-EPMC11322994 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature