With the rapid development of smart grids, the number of various types of power IoT terminal devices has grown by leaps and bounds. An attack on either of the difficult-to-protect end devices or any node in a large and complex network can put the grid at risk. The traffic generated by Distributed Denial of Service (DDoS) attacks is characterised by short bursts of time, making it difficult to apply existing centralised detection methods that rely on manual setting of attack characteristics to changing attack scenarios. In this paper, a DDoS attack detection model based on Bidirectional Long Short-Term Memory (BiLSTM) is proposed by constructing an edge detection framework, which achieves bi-directional contextual information extraction of the network environment using the BiLSTM network and automatically learns the temporal characteristics of the attack traffic in the original data traffic. This paper takes the DDoS attack in the power Internet of Things as the research object. Simulation results show that the model outperforms traditional advanced models such as Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) in terms of accuracy, false detection rate, and time delay. It plays an auxiliary role in the security protection of the power Internet of Things and effectively improves the reliability of the power grid.

With the development of power IoTs (Internet of Things) technology, more and more intelligent devices access the network. Cloud computing is used to provide the resource storage and task computing services for power network. However, there are many problems with traditional cloud computing such as the long-time delay and resource bottleneck. Therefore, in this paper, a two-level resource management scheme is put forward based on the idea of edge computing. Furthermore, a new task scheduling algorithm is presented based on the ant colony algorithm, which realized the resource sharing and dynamic scheduling. The data of simulation show that this algorithm has a good effect on the performance of task execution time, power consumption, and so on.