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Anomaly detection on CSE_CIC_IDS2018 data set using generative adversial network, GAN-Based Network Intrusion Detection Method for Anomaly Detection
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Abstract:
The proposed method addresses the challenge of limited labeled samples in traditional network intrusion detection systems by utilizing a Generative Adversarial Network (GAN) to generate additional training data, thereby improving detection accuracy. By transforming the typical binary classification structure of GANs into a supervised multi-class classification model, the approach introduces a redesigned loss function and tailored training strategy to support effective multi-category intrusion detection. Experimental comparisons under the same conditions show that the proposed model achieves higher accuracy, better robustness, improved generalization ability, and more stable performance than existing methods.

Objective:
The objective of this study is to develop a network intrusion detection method based on a Generative Adversarial Network (GAN) that enhances detection accuracy by generating additional labeled samples for training, transforms the traditional binary classification framework into a supervised multi-class model, and improves the overall robustness, stability, and generalization ability of the detection system.

Existing System:
Traditional network intrusion detection systems primarily rely on supervised learning models, which require large volumes of labeled data for effective training. However, in real-world scenarios, acquiring sufficient labeled samples is often difficult and time-consuming. As a result, these systems struggle with limited training data, leading to lower detection accuracy, especially when identifying novel or complex attack patterns. Additionally, most existing models are based on binary classification, which limits their ability to distinguish between multiple types of network intrusions. These limitations hinder their effectiveness in dynamic and diverse network environments.

Proposed System:
The proposed system introduces a network intrusion detection method based on a Generative Adversarial Network (GAN) to overcome the limitations of traditional models. By leveraging the adversarial learning mechanism, the GAN continuously generates synthetic labeled samples, effectively expanding the training dataset and improving the detection accuracy. Unlike conventional binary classification approaches, this system is designed for supervised multi-class classification, allowing it to detect and distinguish between multiple types of network attacks. A redesigned loss function and optimized training strategy are employed to enhance model performance. The system demonstrates improved stability, robustness, generalization ability, and a higher detection rate in experimental evaluations compared to existing methods.


#AI #Cybersecurity #MachineLearning #GAN #networksecurity
#IntrusionDetection #AnomalyDetection #GANForCybersecurity #DeepLearningIDS #NetworkIntrusionDetection #ML Security Solutions #Smart Network Protection #Cyber Threat Intelligence