利用快速持续对比发散进行网络异常现象检测的研究

Symmetry Pub Date : 2024-09-17 DOI:10.3390/sym16091220
Jaeyeong Jeong, Seongmin Park, Joonhyung Lim, Jiwon Kang, Dongil Shin, Dongkyoo Shin
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引用次数: 0

摘要

随着网络技术的发展,网络攻击不仅日益频繁,而且越来越复杂。为了主动检测和预防这些网络攻击,研究人员正在利用机器学习和深度学习技术开发入侵检测系统(IDS)。然而,这些高级模型面临的一个重大挑战是,随着模型复杂性的增加,训练时间也会增加,而且必须考虑到性能与训练时间之间的对称性。为了解决这个问题,本研究提出了一种基于快速持久-对比-发散的深度信念网络(FPCD-DBN),它既能提供高准确度,又能提供快速的训练时间。该模型结合了对比发散的效率和深度信念网络强大的特征提取能力。传统的深度信念网络使用对比发散(CD)算法,而 FPCD 算法则通过将每个检测层的结果传递给下一层来提高模型的性能。此外,使用快速权重和连续链进行参数更新的组合使模型既快速又准确。我们在多个基准数据集上评估了所提出的 FPCD-DBN 模型的性能,包括 NSL-KDD、UNSW-NB15 和 CIC-IDS-2017。结果证明,所提出的方法是一种可行的解决方案,因为该模型表现出色,准确率达 89.4%,F1 得分为 89.7%。通过在多个数据集上取得优异的性能,该方法在增强网络安全和提供针对不断演变的网络威胁的强大防御方面显示出巨大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Study on Network Anomaly Detection Using Fast Persistent Contrastive Divergence
As network technology evolves, cyberattacks are not only increasing in frequency but also becoming more sophisticated. To proactively detect and prevent these cyberattacks, researchers are developing intrusion detection systems (IDSs) leveraging machine learning and deep learning techniques. However, a significant challenge with these advanced models is the increased training time as model complexity grows, and the symmetry between performance and training time must be taken into account. To address this issue, this study proposes a fast-persistent-contrastive-divergence-based deep belief network (FPCD-DBN) that offers both high accuracy and rapid training times. This model combines the efficiency of contrastive divergence with the powerful feature extraction capabilities of deep belief networks. While traditional deep belief networks use a contrastive divergence (CD) algorithm, the FPCD algorithm improves the performance of the model by passing the results of each detection layer to the next layer. In addition, the mix of parameter updates using fast weights and continuous chains makes the model fast and accurate. The performance of the proposed FPCD-DBN model was evaluated on several benchmark datasets, including NSL-KDD, UNSW-NB15, and CIC-IDS-2017. As a result, the proposed method proved to be a viable solution as the model performed well with an accuracy of 89.4% and an F1 score of 89.7%. By achieving superior performance across multiple datasets, the approach shows great potential for enhancing network security and providing a robust defense against evolving cyber threats.
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