Data Extraction and Analysis in Network Security Anti-attack Detection Based on Artificial Neural Network Algorithm

The significance of accurate detection rules is paramount in identifying network security incidents, as precision plays a pivotal role in minimizing both false positives and false negatives. To achieve this goal, the establishment of effective detection rules becomes imperative. The advent of artificial neural network intrusion detection systems has significantly bolstered the effectiveness of identifying such breaches. Capitalizing on neural networks' capacity to learn and categorize data, the utilization of elastic propagation neural networks for identifying simulated attacks has gained substantial traction. The traditional manual configuration of detection rules is not only time-intensive and arduous but also lacks efficiency. Recent times have witnessed the emergence of intrusion detection as a novel and proactive defense mechanism, assuming a crucial position in the realm of network security technology. Building upon a comprehensive analysis of existing intrusion detection research, this study delves into data analysis and adaptive modeling within intrusion detection systems, subsequently scrutinizing and enhancing the shortcomings of optimized neural networks. This study proposes a refined neural network, and through integration with a feature extraction approach and the enhanced algorithm, formulates an intrusion detection model that exhibits commendable accuracy, as validated through experimentation.