Caiqiu Zhou

Bio: Caiqiu Zhou is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Computer science & Wireless network. The author has an hindex of 1, co-authored 1 publications receiving 51 citations.

Physical-Layer Security in Space Information Networks: A Survey

Abstract: Research and processing development on satellite communications has strongly re-emerged in recent years. Following the prosperity of various wireless services provided by satellite communications, the security issue has raised growing concerns since the space information network is susceptible to be eavesdropped by illegal adversaries in such a large-scale wireless network. Recently, the physical-layer security (PLS) has emerged as an alternative security paradigm that explores the randomness of the wireless channel to achieve confidentiality and authentication. The success story of the PLS technique now spans a decade and thrives to provide a layer of defense in satellite communications. With this position, a comprehensive survey of satellite communications is conducted in this article with an emphasis on PLS. We first briefly introduce essential background and the view of the satellite Internet of Things (IoT), as well as discuss related research challenges faced by the emerging integrated network architecture. Then, we revisit the most popular satellite channel model influenced by many factors and list the commonly used secrecy performance metrics. Also, we provide an exhaustive review of state-of-the-art research activity on PLS in satellite communications, which we categorize by different architectures including land mobile satellite communication networks, hybrid satellite-terrestrial relay networks, and satellite-terrestrial integrated networks. In addition, a number of open research problems are identified as possible future research directions.

Malicious Traffic Detection with Class Imbalanced Data Based on Coarse-grained Labels

Abstract: In order to resist complex cyber-attacks, a Security Operations Center (SOC) named IHEPSOC has been developed and deployed in the Institute of High Energy Physics (IHEP) of the Chinese Academy of Sciences, which contributed to the reliability and security of the network for IHEP. It has become a major task to integrate state-of-the-art cyber-attack detection methods for IHEPSOC to improve the ability of threat detection. Malicious traffic detection based on machine learning is an emerging security paradigm, which can effectively detect both known and unknown cyber-attacks. However, the existing studies usually adopt traditional supervised learning, which often encounter issueswhen appliedto real-worldproduction environmentdue toits implicitassumptions on the operating dependence. For example, most studies are based on datasets that already have accurate data labels, but labeling these datasets accurately requires significant manual effort. In addition, in the real-world service, the volume of benign traffic data is larger than that of the malicious traffic data, and the imbalance between benign and malicious categories also makes many machine learning detection models difficult to apply to a production environment. Based on these, we propose a detection method for class imbalanced malicious traffic based on coarse-grained data labels, which achieves comparable performance compare to other supervised learning methods. We conducted three experiments, using the Android Malware 2017 dataset, and verified the practicability and effectiveness of the proposed method.

A Weakly-supervised Method for Encrypted Malicious Traffic Detection

Abstract: and addresses the insufficiency of labeled samples. In addition, a multi-instance detector with an attention mechanism is used to identify encrypted malicious traffic from coarse-grained labeled data. We validate the proposed approach on two public datasets. Compared with other malicious traffic detection methods, the experimental results show that our proposed framework can effectively perform fine-grained detection of encrypted malicious traffic.

A Fusion Model Based on Dynamic Web Browsing Behavior Analysis for IoT Insider Threat Detection

Abstract: With the wide application of Internet of things (IoT) devices in enterprises, the traditional boundary defense mechanisms are difficult to satisfy the demands of the insider threats detection. IoT insider threat detection can be more challenging, since internal employees are born with the ability to escape the deployed information security mechanism, such as firewalls and endpoint protection. In order to detect internal attacks more accurately, we can analyze users’ web browsing behaviors to identify abnormal users. The existing web browsing behavior anomaly detection methods ignore the dynamic change of the web browsing behavior of the target user and the behavior consistency of the target user in its peer group, which results in a complex modeling process, low system efficiency and low detection accuracy. Therefore, the paper respectively proposes the individual user behavior model and the peer-group behavior model to characterize the abnormal dynamic change of user browsing behavior and compare the mutual behavioral inconsistency among one peer-group. Furthermore, the fusion model is presented for insider threat detection which simultaneously considers individual behavioral abnormal dynamic changes and mutual behavioral dynamic inconsistency from peers. The experimental results show that the proposed fusion model can accurately detect insider threat based on the abnormal user web browsing behaviors in the enterprise networks.

UAV Communications for 5G and Beyond: Recent Advances and Future Trends

Abstract: Providing ubiquitous connectivity to diverse device types is the key challenge for 5G and beyond 5G (B5G). Unmanned aerial vehicles (UAVs) are expected to be an important component of the upcoming wireless networks that can potentially facilitate wireless broadcast and support high rate transmissions. Compared to the communications with fixed infrastructure, UAV has salient attributes, such as flexible deployment, strong line-of-sight (LoS) connection links, and additional design degrees of freedom with the controlled mobility. In this paper, a comprehensive survey on UAV communication towards 5G/B5G wireless networks is presented. We first briefly introduce essential background and the space-air-ground integrated networks, as well as discuss related research challenges faced by the emerging integrated network architecture. We then provide an exhaustive review of various 5G techniques based on UAV platforms, which we categorize by different domains including physical layer, network layer, and joint communication, computing and caching. In addition, a great number of open research problems are outlined and identified as possible future research directions.

Supporting IoT With Rate-Splitting Multiple Access in Satellite and Aerial-Integrated Networks

Abstract: To satisfy the explosive access demands of Internet-of-Things (IoT) devices, various kinds of multiple access techniques have received much attention. In this article, we investigate the multicast communication of a satellite and aerial-integrated network (SAIN) with rate-splitting multiple access (RSMA), where both satellite and unmanned aerial vehicle (UAV) components are controlled by network management center and operate in the same frequency band. Considering a content delivery scenario, the UAV subnetwork adopts the RSMA to support massive access of IoT devices (IoTDs) and achieve desired performances of interference suppression, spectral efficiency, and hardware complexity. We first formulate an optimization problem to maximize the sum rate of the considered system subject to the signal-interference-plus-noise-ratio requirements of IoTDs and per-antenna power constraints at the UAV and satellite. To solve this nonconvex optimization problem, we exploit the sequential convex approximation and the first-order Taylor expansion to convert the original optimization problem into a solvable one with the rank-one constraint, and then propose an iterative penalty function-based algorithm to solve it. Finally, simulation results verify that the proposed method can effectively suppress the mutual interference and improve the system sum rate compared to the benchmark schemes.

Refracting RIS-Aided Hybrid Satellite-Terrestrial Relay Networks: Joint Beamforming Design and Optimization

Abstract: Reconfigurable intelligent surface (RIS) has been viewed as a promising solution in constructing reconfigurable radio environment of the propagation channel and boosting the received signal power by smartly coordinating the passive elements’ phase shifts at the RIS. Inspired by this emerging technique, this article focuses on joint beamforming design and optimization for RIS-aided hybrid satellite-terrestrial relay networks, where the links from the satellite and base station (BS) to multiple users are blocked. Specifically, a refracting RIS cooperates with a BS, where the latter operates as a half-duplex decode-and-forward relay, in order to strengthen the desired satellite signals at the blocked users. Considering the limited onboard power resource, the design objective is to minimize the total transmit power of both the satellite and BS while guaranteeing the rate requirements of users. Since the optimized beamforming weight vectors at the satellite and BS, and phase shifters at the RIS are coupled, leading to a mathematically intractable optimization problem, we propose an alternating optimization scheme by utilizing singular value decomposition and uplink–downlink duality to optimize beamforming weight vectors, and using Taylor expansion and penalty function methods to optimize phase shifters iteratively. Finally, simulation results are provided to verify the superiority of the proposed scheme compared to the benchmark schemes.

Non-terrestrial networks in 5G & beyond: A survey

Abstract: Fifth-generation (5G) telecommunication systems are expected to meet the world market demands of accessing and delivering services anywhere and anytime. The Non-Terrestrial Network (NTN) systems are able to satisfy the requests of anywhere and anytime connections by offering wide-area coverage and ensuring service availability, continuity, and scalability. In this work, we review the 3GPP NTN features and their potential for satisfying the user expectations in 5G & beyond networks. The state of the art, current 3GPP research activities, and open issues are summarized to highlight the importance of NTN over the wireless communication landscape. Future research directions are also identified to assess the role of NTN in 5G and beyond systems.

Data-Driven Trajectory Quality Improvement for Promoting Intelligent Vessel Traffic Services in 6G-Enabled Maritime IoT Systems

Abstract: Future generation communication systems, such as 5G and 6G wireless systems, exploit the combined satellite-terrestrial communication infrastructures to extend network coverage and data throughput for data-driven applications. These ground-breaking techniques have promoted the rapid development of Internet of Things (IoT) in maritime industries. In maritime IoT applications, intelligent vessel traffic services can be guaranteed by collecting and analyzing high volume of spatial data flows from automatic identification system (AIS). This AIS system includes a highly integrated automatic equipment, including functionalities of core communication, tracking, and sensing. The increased utilization of shipboard AIS devices allows the collection of massive trajectory data. However, the received raw AIS data often suffers from undesirable outliers (i.e., poorly tracked timestamped points for vessel trajectories ) during signal acquisition and analog-to-digital conversion. The degraded AIS data will bring negative effects on vessel traffic services (e.g., maritime traffic monitoring, intelligent maritime navigation, vessel collision avoidance, etc.) in maritime IoT scenarios. To improve the quality of vessel trajectory records from AIS networks, we propose to develop a two-phase data-driven machine learning framework for vessel trajectory reconstruction. In particular, a density-based clustering method is introduced in the first phase to automatically recognize the undesirable outliers. The second phase proposes a bidirectional long short-term memory (BLSTM)-based supervised learning technique to restore the timestamped points degraded by random outliers in vessel trajectories. Comprehensive experiments on simulated and realistic data sets have verified the dominance of our two-phase vessel reconstruction framework compared to other competing methods. It thus has the capacity of promoting intelligent vessel traffic services in 6G-enabled maritime IoT systems.