Modern rescue operations rely on wireless communications for safety reporting, area monitoring, and rescue coordination. However, natural disasters severely damage ground infrastructure, creating significant challenges for emergency rescue and recovery efforts. This paper establishes a disaster response network using Movable and Deployable Resource Units (MDRUs) in disaster-affected areas, to provide timely and reliable message transmission services. Firstly, to ensure a timely and efficient disaster response, we design a post-disaster emergency vehicle network architecture. Secondly, we propose a three-phase emergency relief model to dynamically deploy MDRUs, aiming to maximize their service coverage. Finally, we propose a Trajectory-Based Anycast Routing (TBAR) protocol, which enhances message transmission efficiency by optimizing route selection. Specifically, by facilitating the flexibility of any cast in delivering messages to anyone of the reachable MDRUs, TBAR utilizes multiple copies of messages to reduce end-to-end latency and increase the delivery ratio. Moreover, TBAR adaptively evaluates the message delivery capability of candidate vehicles using a multi-attribute decision-making algorithm, considering link quality, trajectory similarity, and distance cost. Extensive simulation results show that TBAR significantly outperforms other baseline algorithms in multiple aspects.

Reconfigurable Intelligent Surface (RIS) is envisioned to be an enabling technique in 6G wireless communications. By configuring the reflection beamforming codebook, RIS focuses signals on target receivers to enhance signal strength. In this paper, we investigate the codebook configuration for RIS-aided communication systems. We formulate an implicit relationship between user's coordinates information and the codebook from the perspective of signal radiation mechanisms, and introduce a novel learning-based method, implicit neural representations (INRs), to solve this implicit coordinates-to-codebook mapping problem. Our approach requires only user's coordinates, avoiding reliance on channel models. Additionally, given the significant practical applications of the 1-bit RIS, we formulate the 1-bit codebook configuration as a multi-label classification problem, and propose an encoding strategy for 1-bit RIS to reduce the codebook dimension, thereby improving learning efficiency. Experimental results from simulations and measured data demonstrate significant advantages of our method.

Dual-domain terminal with two built-in independent operating systems - Life Domain and Work Domain, provides convenience for daily use and mobile office. However, the security isolation between the two domains also causes that message reminders cannot be delivered and viewed across domains, which restricts the improvement of work efficiency and the expansion of mobile services. This paper conducts an in-depth study on this pain point and proposes the concept and implementation method of a cross-domain instant messaging reminder service system for mobile office, focusing on solving the problems of: cross-domain isolated boundary exchange of message reminders, timeliness and delivery rate guarantee of message reminders, and security check filtering of message contents. Technically, on the side of mobile office platform, based on AMQP technical framework and protocol, the cross-domain isolated border message queue push and synchronization services are built, which are real-time, reliable and high-throughput.

This paper studies the collection of large-scale log data of information system and puts forward a dynamic adaptive mechanism for large-scale unified log data collection. Furthermore, we design and implement our method for pan-government industry safety operation management platform. The data flow processing architecture based on message queue is adopted to realize the decoupling of log collection, log processing and log reporting. The traffic peak clipping technology of message queue is adopted to ensure the safety and reliability of log transmission. According to the characteristics of log traffic, a design mode supporting dynamic adjustment of consumption group is proposed to meet the high-performance requirements of the system. The whole system can meet the centralized analysis, security threat perception and intelligent analysis of various security data. Meanwhile, we analyzed and compared with the traditional open-source log collection technology, our proposed method and system has clear advantages.

With the development of smart cities, video surveillance networks have become an important infrastructure for urban governance. However, by replacing or tampering with surveillance cameras, an important front-end device, attackers are able to access the internal network. In order to identify illegal or suspicious camera identities in advance, a camera identity identification method that incorporates multidimensional identification features is proposed. By extracting the static information of cameras and dynamic traffic information, a camera identity system that incorporates explicit, implicit, and dynamic identifiers is constructed. The experimental results show that the explicit identifiers have the highest contribution, but they are easy to forge; the dynamic identifiers rank second, but the traffic preprocessing is complex; the static identifiers rank last but are indispensable. Experiments on 40 cameras verified the effectiveness and feasibility of the proposed identifier system for camera identification, and the accuracy of identification reached 92.5%.

In order to improve the ability to detect network attacks, traditional intrusion detection models often used convolutional neural networks to encode spatial information or recurrent neural networks to obtain temporal features of the data. Some models combined the two methods to extract spatio-temporal features. However, these approaches used separate models and learned features insufficiently. This paper presented an improved model based on temporal convolutional networks (TCN) and attention mechanism. The causal and dilation convolution can capture the spatio-temporal dependencies of the data. The residual blocks allow the network to transfer information in a cross-layered manner, enabling in-depth network learning. Meanwhile, attention mechanism can enhance the model's attention to the relevant anomalous features of different attacks. Finally, this paper compared models results on the KDD CUP99 and UNSW-NB15 datasets. Besides, the authors apply the model to video surveillance network attack detection scenarios. The result shows that the model has advantages in evaluation metrics.

Joint extraction of entities and relations is a task that extracts the entity mentions and semantic relations between entities from the unstructured texts with one single model. Existing entity and relation extraction datasets usually rely on distant supervision methods which cannot identify the corresponding relations between a relation and the sentence, thus suffers from noisy labeling problem. We propose a hybrid deep neural network model to jointly extract the entities and relations, and the model is also capable of filtering noisy data. The hybrid model contains a transformer-based encoding layer, an LSTM entity detection module and a reinforcement learning-based relation classification module. The output of the transformer encoder and the entity embedding generated from the entity detection module are combined as the input state of the reinforcement learning module to improve the relation classification and noisy data filtering. We conduct experiments on the public dataset produced by the distant supervision method to verify the effectiveness of our proposed model. Different experimental results show that our model gains better performance on entity and relation extraction than the compared methods and also has the ability to filter noisy sentences.

The security threats to software-defined networks (SDNs) have become a significant problem, generally because of the open framework of SDNs. Among all the threats, distributed denial-of-service (DDoS) attacks can have a devastating impact on the network. We propose a method to discover DDoS attack behaviors in SDNs using a feature-pattern graph model. The feature-pattern graph model presented employs network patterns as nodes and similarity as weighted links; it can demonstrate not only the traffic header information but also the relationships among all the network patterns. The similarity between nodes is modeled by metric learning and the Mahalanobis distance. The proposed method can discover DDoS attacks using a graph-based neighborhood classification method; it is capable of automatically finding unknown attacks and is scalable by inserting new nodes to the graph model via local or global updates. Experiments on two datasets prove the feasibility of the proposed method for attack behavior discovery and graph update tasks, and demonstrate that the graph-based method to discover DDoS attack behaviors substantially outperforms the methods compared herein.