As the current detection solutions of distributed denial of service attacks
(DDoS) need additional infrastructures to handle high aggregate data rates,
they are not suitable for sensor networks or the Internet of Things. Besides,
the security architecture of software-defined sensor networks needs to pay
attention to the vulnerabilities of both software-defined networks and sensor
networks. In this paper, we propose a network-aware automated machine learning
(AutoML) framework which detects DDoS attacks in software-defined sensor
networks. Our framework selects an ideal machine learning algorithm to detect
DDoS attacks in network-constrained environments, using metrics such as
variable traffic load, heterogeneous traffic rate, and detection time while
preventing over-fitting. Our contributions are two-fold: (i) we first
investigate the trade-off between the efficiency of ML algorithms and
network/traffic state in the scope of DDoS detection. (ii) we design and
implement a software architecture containing open-source network tools, with
the deployment of multiple ML algorithms. Lastly, we show that under the denial
of service attacks, our framework ensures the traffic packets are still
delivered within the network with additional delays.

提出了一种网络感知的自动化机器学习框架，用于在软件定义的传感器网络中检测分布式拒绝服务攻击（DDoS）。该框架通过选择理想的机器学习算法，根据可变流量负载、异构流量速率和检测时间等指标，能够在网络受限环境中检测到 DDoS 攻击并避免过拟合。