The cloud boundary network environment is characterized by a passive defense strategy, discrete defense actions, and delayed defense feedback in the face of network attacks, ignoring the influence of the external environment on defense decisions, thus resulting in poor defense effectiveness. Therefore, this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent, designs the network structure of the intelligent agent attack and defense game, and depicts the attack and defense game process of cloud boundary network; constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment, and portrays the interaction process between intelligent agent and environment; establishes the reward mechanism based on the attack and defense gain, and encourage intelligent agents to learn more effective defense strategies. the designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics, interaction lag, and control dispersion in the defense decision process of cloud boundary networks, and improve the autonomy and continuity of defense decisions.

Enhancing the security capability of decentralized networks has been a focus of attention in the IoT academic community. Decentralized networks face problems such as lack of security resources, complex and heterogeneous difficulties in network security management, and dependence on the level of knowledge of human experts for security defense strategies and management effects. To tackle these challenges, this study presents a multi-agents deep reinforcement learning autonomous security management approach. The research builds a finite random game network attack-defense model that captures the dynamic adversarial nature of the attack-defense process. Leveraging reinforcement learning techniques, autonomous defense agent is designed to autonomously generate and adapt defense strategies. To enhance the defense capability, a network attack agent is developed. Moreover, drawing inspiration from MINIMAX Q-learning, a synchronized interactive training mechanism is discussed to address the issue of environment instability arising from the decoupling of observation space and action space among multiple coexisting offensive and defensive agents in the same environment. Experimental simulations validate the effectiveness of the proposed method in automated attack-defense scenarios, and analyse the generalization ability in networks of different scales.

Network attack identification effectively secures Internet of Things (IoT) application scenarios. However, dynamic scene changes, attack feature reliance, high data dimensions, and challenges with spatio-temporal feature fusion frequently pose limitations to attack traffic identification in IoT contexts. Definitive intelligent IoT attack identification enables intelligent algorithms to extract attack features for application scenarios with fixed topological environments but cannot construct the intricate changes of IoT application scenarios. Through the dynamic acquisition, feature awareness, and deep learning, intelligent digital twin-based attack detection can address these issues and enhance attack identification for IoT threats. Thus, this paper proposed an intelligent digital twin method based on spatio-temporal feature fusion for IoT attack behavior identification. Firstly, feature subsets are selected based on information gain to reduce the dimensionality of IoT data with high traffic; Secondly, a parallel spatio-temporal feature extraction model is designed unlike the existing tandem model, which uses a simplified Convolutional Neural Networks (CNN) model to learn the spatial features of the attack, a Bi-directional Long Short-Term Memory (BiLSTM) model to learn the temporal features of the attack, an attention mechanism to fuse the temporal and spatial features, and the (Deep Neural Networks) DNN to learn the combined features; Finally, the virtual instance space and topology of the attack scenario are simulated using digital twin (DT) to build a digital version of the complex system for IoT applications and tested in a simulation environment. Based on experimental results using the UNSW-NB15 and CICIDS2017 datasets, this paper shows that the proposed method can extract spatio-temporal features from network attack traffic and has a 5% improvement in test accuracy.

Gesture recognition is an important human-computer interaction interface. This article introduces a novel hand gesture recognition system based on Leap Motion gen.2. In this system, a spatial fuzzy matching (SFM) algorithm is first presented by matching and fusing spatial information to construct a fused gesture dataset. For dynamic hand recognition, an initial frame correction strategy based on SFM is proposed to fast initialize the trajectory of test gesture with respect to the gesture dataset. A notable feature of this system is that it can run on ordinary laptops due to the small size of the fused dataset, which accelerates the calculation of recognition rate. Experimental results show that the system recognizes static hand gestures at recognition rates of 94%-100% and over 90% of dynamic gestures using our collected dataset. This can greatly enhance the usability of Leap Motion.

Intrusion detection is an efficient method to detect the malicious node in the MANET. This paper presents the improvements of the existing data analysis and pattern-matching algorithm based active set, which develop a more effective BER.