Related Experiment Videos
AER-DCWGAN: Adversarial Encoder-Regularized Dual-Conditional Wasserstein GAN for Imbalanced Network Intrusion
Mingqi Wang1, Yu Yang1, Minna Gao2
1School of Information Engineering, Engineering University of PAP, Xi'an 710086, China.
Sensors (Basel, Switzerland)
|July 28, 2026
Summary
Class imbalance in network intrusion detection is challenging. The proposed AER-DCWGAN improves augmentation for some rare attack types but struggles with ultra-rare classes, showing class-dependent performance.
Area of Science:
- Cybersecurity
- Machine Learning
- Network Intrusion Detection Systems (NIDS)
Background:
- Class imbalance is a significant challenge in network intrusion detection, especially for Internet of Things (IoT) and sensor networks.
- Rare attack categories are often underrepresented by high-dimensional traffic samples, hindering reliable detection.
Purpose of the Study:
- To propose an advanced generative adversarial network framework for minority-class augmentation in network intrusion detection.
- To enhance the detection of rare attack categories by improving data augmentation techniques.
Main Methods:
- Introduced the adversarial encoder-regularized dual-conditional Wasserstein generative adversarial network (AER-DCWGAN), a class-aware latent-consistency framework.
- The framework operates in a normalized, feature-selected space, jointly modeling traffic features, latent codes, and class embeddings for improved consistency.
- Integrated components like a Wasserstein critic, encoder-guided reconstruction, adversarial prior alignment, and label-consistency filtering to mitigate issues like latent drifting.
Main Results:
- AER-DCWGAN demonstrated class-dependent improvements on NSL-KDD and CIC-IDS2017 datasets.
- Significant F1-score increases were observed for Remote-to-Local (R2L) attacks (0.501 to 0.823) and Web Attacks (0.952 to 0.983).
- However, performance gains for User-to-Root (U2R) attacks were limited (0.124 to 0.204), and some classes like Bot and PortScan showed slight decreases, indicating challenges with ultra-rare classes.
Conclusions:
- The AER-DCWGAN framework effectively alleviates moderate class imbalance for network intrusion detection classes with sufficient representation.
- The study highlights that while the model shows promise, it does not fully resolve the detection of ultra-rare attack categories.
- Further research is needed to address the persistent challenges in detecting extremely infrequent threats in network traffic.
Related Concept Videos
01:22Force Classification
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
01:22Multi-input and Multi-variable systems
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
In the absence of...