The Automatic Identification System (AIS) has become a cornerstone of modern maritime navigation, traffic management, and situational awareness, enabling continuous exchange of vessel identity, position, speed, and voyage‐related information across global maritime networks. While AIS significantly enhances navigational safety and operational efficiency, its open and unauthenticated broadcast design introduces critical cybersecurity vulnerabilities that can be exploited through spoofing, message replay, data manipulation, and denial‐of‐service attacks. These threats not only undermine trust in AIS data but also pose serious risks to maritime safety, security, and environmental protection. This paper presents a comprehensive and systematic review of AIS within the broader maritime communication and cybersecurity ecosystem. It examines the architectural foundations of AIS, its integration with physical and digital maritime infrastructures, and its evolving role in maritime domain awareness (MDA). Particular emphasis is placed on identifying inherent protocol‐level weaknesses and analyzing documented cyberattack vectors enabled by software‐defined radio technologies and unencrypted VHF communications. The review further synthesizes recent advances in machine learning and artificial intelligence techniques applied to AIS data, including anomaly detection, trajectory analysis, intrusion detection systems, and federated learning–based privacy‐preserving frameworks. By critically evaluating existing methodologies, datasets, and deployment challenges, this study highlights key limitations related to scalability, real‐time applicability, explainability, and adversarial robustness. Drawing insights from parallel developments in aviation cybersecurity, the paper outlines future research directions aimed at strengthening AIS resilience through adaptive, explainable, and policy‐aligned AI‐driven defenses. Overall, this work provides a structured foundation for advancing AIS cybersecurity research and supports the development of robust, data‐driven strategies to safeguard increasingly digitalized maritime operations.
With the rising penetration of smartphones and mobile applications (apps), mobile apps provide a perspective to interpret and profile their users. Considering that privacy concerns make it increasingly difficult to obtain rich data on users’ app usage, user profiling is implemented solely based on the list of apps installed by the user in this paper. For existing methods, most of them only conduct simple feature engineering and ignore the latent relationships that exist in the installed app list. In this paper, novel multichannel features are proposed to represent users. In addition to mining the app property, the proposed multichannel features exploit the relationships between apps, users, and user attributes. Combined with a 2-dimensional convolutional neural network, the prediction of four user attributes is realized in this paper. Experimental results show that the proposed feature can effectively infer user attributes with the best accuracy of 79.85%, 52.34%, 69.76%, and 69.95% for gender, age, the device’s screen size, and the device’s price, which is 12.39%, 11.75%, 17.61%, and 29.23% higher than the guess based on maximum probability, respectively. Besides, the proposed prediction method has better prediction performance compared to the existing methods and can handle features with high dimensionality well.
As the ability to circumvent internet censorship, encrypted proxies are widely used by criminals in illegal activities (e.g., online gambling and darknet transactions). Thus, detection of encrypted proxy traffic is important. In recent years, deep learning-based approaches have become mainstream approaches. Many deep learning-based approaches transform internet traffic into images, but the transformed images are normally large, leading to huge computational and storage resource overhead. To solve this issue, a novel approach is proposed to compress the image size for reducing overhead in detecting encrypted proxy traffic while still achieving comparable performance. By analyzing the spatiotemporal features of the flow, we discovered that the sequences of sizes, directions, and interval times of the first few packets of a flow can be used to detect encrypted proxy traffic. We compare and analyze the characteristics of the size, direction, interval time of the packet, and the pixel value of the image, and design several equations to encode the sequences of sizes, directions, and interval times of only the first N packets of a flow into an image. Furthermore, a lightweight convolutional neural network (CNN) is constructed to classify the converted images. The experimental results exhibit that the proposed approach could reduce the image size by at least 90% and achieve F1 scores of 99.67% in ShadowsocksR traffic detection and 99.44% in VPN traffic detection. These results show that the proposed approach is effective and efficient. Because of its high efficiency, the proposed method can be applied to large-scale network traffic analysis tasks.
