Many studies utilized machine learning schemes to improve network intrusion detection systems recently. Most of the research is based on manually extracted features, but this approach not only requires a lot of labor costs but also loses a lot of information in the original data, resulting in low judgment accuracy and cannot be deployed in actual situations. This paper develops a DL-IDS (deep learning-based intrusion detection system), which uses the hybrid network of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) to extract the spatial and temporal features of network traffic data and to provide a better intrusion detection system. To reduce the influence of an unbalanced number of samples of different attack types in model training samples on model performance, DL-IDS used a category weight optimization method to improve the robustness. Finally, DL-IDS is tested on CICIDS2017, a reliable intrusion detection dataset that covers all the common, updated intrusions and cyberattacks. In the multiclassification test, DL-IDS reached 98.67% in overall accuracy, and the accuracy of each attack type was above 99.50%.

Sparse mobile crowdsensing (SMCS) has emerged as a promising sensing paradigm for urban sensing, leveraging the spatial and temporal correlation among data sensed in distinct sub-areas to cut sensing expenses dramatically. It intelligently selects only a tiny portion of the target regions for sensing and accurately infers the data for the remaining unsensed areas. SMCS confronts numerous challenges, such as sensing cell selection and missing data inference, when compared to mobile crowdsensing. Researchers in recent years have proposed plenty of strategies to solve these challenges. From the perspective of comparing MCS, we aim to provide a comprehensive literature review of recent advances in SMCS in this paper. We begin by going over the preliminary of SMCS and MCS, including their evolution, characteristics, and life-cycle stages. We then go through their common key techniques and recent developments. Furthermore, we give a review of the unique key techniques as well as the most recent advancements. We finally identify existing applications and highlight potential research opportunities for SMCS. Our objective is to provide researchers with a comprehensive understanding of SMCS.

Path planning is critical to realizing a high-performance unmanned aerial vehicle (UAV) crowdsensing system, which can be deployed to carry out large-scale tasks in the physical world, especially in emergency scenarios, such as earthquakes and mudslides. Deep reinforcement learning (DRL) has recently proven its superiority in path design. However, it is often applied under the assumption that the entire status of the target region is available, which is hard to achieve in practice. Instead, efforts should be made to ensure the efficient flight of several UAVs in order to collect data with incomplete observations in specified places. In this work, we set out to create a high-performance UAV crowdsensing system by combining DRL with partial observations. We present a novel DRL-based path-planning algorithm called DRL-PP. Specifically, we integrate an attention mechanism into the actor–critic technique to assist UAV swarm collaboration to collect data. We also design an incentive mechanism to ease the problem of sparse reward. Furthermore, we provide a dilemma detection system to prevent the generation of overlapping flight paths. Experimental results from extensive simulations prove that compared with the state-of-the-art approaches, the proposed DRL-PP can significantly improve the efficiency of data collection.