School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, China.
North Navigation Control Technology CO., LTD, Beijing, China.
PLoS One. 2023 Oct 10;18(10):e0291750. doi: 10.1371/journal.pone.0291750. eCollection 2023.
To address the problems of attack category omission and poor generalization ability of traditional Intrusion Detection System (IDS) when processing unbalanced input data, an intrusion detection strategy based on conditional Generative Adversarial Networks (cGAN) is proposed. The cGAN generates attack samples that approximately obey the distribution pattern of input data and are randomly distributed within a certain bounded interval, which can avoid the redundancy caused by mechanical data widening. The experimental results show that the strategy has better performance indexes and stronger generalization ability in overall performance, which can solve insufficient classification performance and detection omission caused by unbalanced distribution of data categories and quantities.
为了解决传统入侵检测系统（IDS）在处理不平衡输入数据时攻击类别遗漏和泛化能力差的问题，提出了一种基于条件生成对抗网络（cGAN）的入侵检测策略。cGAN 生成的攻击样本近似服从输入数据的分布模式，且在一定的有界区间内随机分布，可避免机械数据拓宽所造成的冗余。实验结果表明，该策略在整体性能的各项性能指标和泛化能力上均具有更好的表现，能够解决因数据类别和数量的不平衡分布而导致的分类性能不足和检测遗漏的问题。
