Federated deep Q-learning networks for service-based anomaly detection and classification in edge-to-cloud ecosystems

IF 1.8 4区 计算机科学 Q3 TELECOMMUNICATIONS
Mays AL-Naday, Vlad Dobre, Martin Reed, Salman Toor, Bruno Volckaert, Filip De Turck
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引用次数: 0

Abstract

The diversity of services and infrastructure in metropolitan edge-to-cloud network(s) is rising to unprecedented levels. This is causing a rising threat of a wider range of cyber attacks coupled with a growing integration of a constrained range of infrastructure, particularly seen at the network edge. Deep reinforcement-based learning is an attractive approach to detecting attacks, as it allows less dependency on labeled data with better ability to classify different attacks. However, current approaches to learning are known to be computationally expensive (cost), and the learning experience can be negatively impacted by the presence of outliers and noise (quality). This work tackles both the cost and quality challenges with a novel service-based federated deep reinforcement learning solution, enabling anomaly detection and attack classification at a reduced data cost and with better quality. The federated settings in the proposed approach enable multiple edge units to create clusters that follow a bottom-up learning approach. The proposed solution adapts a deep Q-learning network (DQN) for service-tunable flow classification and introduces a novel federated DQN (FDQN) for federated learning. Through such targeted training and validation, variation in data patterns and noise is reduced. This leads to improved performance per service with lower training cost. Performance and cost of the solution, along with sensitivity to exploration parameters, are evaluated using examples of publicly available datasets (UNSW-NB15 and CIC-IDS2018). Evaluation results show the proposed solution to maintain detection accuracy in the range of ≈75–85% with lower data supply while improving the classification rate by a factor of ≈2.

Abstract Image

用于边缘到云生态系统中基于服务的异常检测和分类的联合深度 Q-learning 网络
大都市边缘到云网络中的服务和基础设施的多样性正在上升到前所未有的水平。这导致更广泛的网络攻击威胁不断增加,与此同时,受限基础设施的整合范围也在不断扩大,尤其是在网络边缘。基于深度强化的学习是检测攻击的一种有吸引力的方法,因为它可以减少对标记数据的依赖,并能更好地对不同攻击进行分类。然而,众所周知,目前的学习方法计算成本高昂(成本),而且学习体验会受到异常值和噪声的负面影响(质量)。这项研究通过一种新颖的基于服务的联合深度强化学习解决方案来应对成本和质量挑战,从而以更低的数据成本和更高的质量实现异常检测和攻击分类。拟议方法中的联合设置使多个边缘单元能够创建遵循自下而上学习方法的集群。所提出的解决方案采用深度 Q 学习网络(DQN)进行服务可调流量分类,并引入了用于联合学习的新型联合 DQN(FDQN)。通过这种有针对性的训练和验证,数据模式的变化和噪声得以减少。这就提高了每项服务的性能,降低了培训成本。我们使用公开数据集(UNSW-NB15 和 CIC-IDS2018)的示例对该解决方案的性能和成本以及对探索参数的敏感性进行了评估。评估结果表明,在数据供应较少的情况下,所提出的解决方案可将检测准确率保持在 ≈75-85% 的范围内,同时将分类率提高了 ≈2。
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来源期刊
Annals of Telecommunications
Annals of Telecommunications 工程技术-电信学
CiteScore
5.20
自引率
5.30%
发文量
37
审稿时长
4.5 months
期刊介绍: Annals of Telecommunications is an international journal publishing original peer-reviewed papers in the field of telecommunications. It covers all the essential branches of modern telecommunications, ranging from digital communications to communication networks and the internet, to software, protocols and services, uses and economics. This large spectrum of topics accounts for the rapid convergence through telecommunications of the underlying technologies in computers, communications, content management towards the emergence of the information and knowledge society. As a consequence, the Journal provides a medium for exchanging research results and technological achievements accomplished by the European and international scientific community from academia and industry.
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