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Enhancing collaborative intrusion detection via disagreement-based semi-supervised learning in IoT environments
Journal of Network and Computer Applications ( IF 8.7 ) Pub Date : 2020-03-27 , DOI: 10.1016/j.jnca.2020.102631
Wenjuan Li , Weizhi Meng , Man Ho Au

Collaborative intrusion detection systems (CIDSs) are developing to improve the detection performance of a single detector in Internet of Things (IoT) networks, through exchanging and sharing data. For anomaly detection, machine learning is an important and essential tool to help identify the deviation between current events and pre-built profile. For a traditional supervised learning classifier, there is a need to provide training examples with ground-truth labels in advance. However, labeled instances are quite limited in real-world IoT scenarios, while unlabeled data/instances are widely available. This is because data labeling is a very expensive process that requires huge human efforts and knowledge inputs. To mitigate this issue, the use of semi-supervised learning algorithms is a promising solution, which can leverage unlabeled data to label data automatically without human intervention. In this work, we focus on semi-supervised learning and design DAS-CIDS, by applying disagreement-based semi-supervised learning algorithm for CIDSs. In the evaluation, we investigate the performance of DAS-CIDS using both datasets and in real IoT network environments, in the aspects of both detection performance and false alarm reduction. The experimental results show that as compared with traditional supervised classifiers, our approach is more effective in detecting intrusions and reducing false alarms by automatically leveraging unlabeled data.



中文翻译:

通过物联网环境中基于分歧的半监督学习来增强协作入侵检测

正在开发协作式入侵检测系统(CIDS),以通过交换和共享数据来提高物联网(IoT)网络中单个检测器的检测性能。对于异常检测,机器学习是一种重要且必不可少的工具,可帮助您识别当前事件和预建轮廓之间的偏差。对于传统的监督学习分类器,需要事先提供带有真实标签的训练示例。但是,在实际的物联网场景中,带标签的实例非常有限,而无标签的数据/实例则广泛可用。这是因为数据标记是一个非常昂贵的过程,需要大量的人力和知识投入。为了缓解这个问题,使用半监督学习算法是一种很有前途的解决方案,它可以利用未标记的数据自动标记数据,而无需人工干预。在这项工作中,我们将重点放在半监督学习和设计DAS-CIDS上,方法是对CIDS应用基于分歧的半监督学习算法。在评估中,我们在检测性能和减少误报方面都研究了使用数据集和实际物联网网络环境中DAS-CIDS的性能。实验结果表明,与传统的监督分类器相比,我们的方法通过自动利用未标记的数据,在检测入侵和减少误报方面更加有效。我们在检测性能和减少误报方面都研究了使用数据集和实际物联网网络环境中DAS-CIDS的性能。实验结果表明,与传统的监督分类器相比,我们的方法通过自动利用未标记的数据,在检测入侵和减少误报方面更加有效。我们在检测性能和减少误报方面都研究了使用数据集和实际物联网网络环境中DAS-CIDS的性能。实验结果表明,与传统的监督分类器相比,通过自动利用未标记的数据,我们的方法在检测入侵和减少误报方面更加有效。

更新日期:2020-03-27
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