Unknown network attack detection based on open-set recognition and active learning in drone network,Transactions on Emerging Telecommunications Technologies

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Unknown network attack detection based on open-set recognition and active learning in drone network
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2021-01-05 , DOI: 10.1002/ett.4212
Zhao Zhang ₁ , Yong Zhang _{1,

2} , Jie Niu ₁ , Da Guo ₁

Affiliation

With the technical support of 5G, the drone network plays a critical role in the autonomous and digital era. However, due to wireless and autonomy characteristics, the drone network is prone to diverse malicious attacks, so it's vital to deploy network intrusion detection system to detect network attacks. For a real open drone network environment, unknown attacks will occur constantly, but the existing intrusion detection methods are usually designed for a static and closed-set scenario and will fail to recognize the unknown attacks correctly, threatening the security of drone network. Therefore, we design an intrusion detection system to detect unknown network attacks in the drone network. Based on open-set recognition, we propose the Open-CNN model to implement intrusion detection and detect unknown attacks. Further to detect unknown attacks, we also propose an active learning (AL) approach for unknown attacks based on the least confidence query strategy, allowing the intrusion detection model to learn efficiently from the unknown attack instances detected by Open-CNN at small labeling budgets. Extensive experiments demonstrate the effectiveness of Open-CNN in detecting unknown attacks, with the accuracy improvement of 9% to 30% over the compared methods, and the proposed AL approach achieves good performance when retraining with only 1% of labeled unknown attack samples.

中文翻译：

基于开放集识别和主动学习的无人机网络未知网络攻击检测

在 5G 的技术支持下，无人机网络在自主和数字化时代发挥着至关重要的作用。然而，无人机网络由于无线和自治的特点，容易受到多种恶意攻击，因此部署网络入侵检测系统来检测网络攻击至关重要。对于真正开放的无人机网络环境，未知攻击会不断发生，而现有的入侵检测方法通常是针对静态封闭场景设计的，无法正确识别未知攻击，威胁无人机网络的安全。因此，我们设计了一个入侵检测系统来检测无人机网络中的未知网络攻击。基于开放集识别，我们提出了Open-CNN模型来实现入侵检测和检测未知攻击。进一步检测未知攻击，我们还提出了一种基于最小置信度查询策略的未知攻击的主动学习（AL）方法，允许入侵检测模型在较小的标记预算下从 Open-CNN 检测到的未知攻击实例中有效地学习。大量实验证明了 Open-CNN 在检测未知攻击方面的有效性，与比较方法相比，准确率提高了 9% 到 30%，并且所提出的 AL 方法在仅使用 1% 的标记未知攻击样本进行再训练时取得了良好的性能。

更新日期：2021-01-05

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