A range of video contents and technology have provided convenience to humans, with real-time video applications-such as surveillance applications-able to contribute to increasing public safety by reducing physical crimes. The development of video technology has made it possible to achieve an improved quality of life. However, this technology can also be exploited and lead to security issues such as physical and digital crimes. Unfortunately, security breaches are increasing in complexity and frequency, making current countermeasures insufficient to prevent them. Given recent trends, we recognize the need for security technology to respond to advanced video crimes. Intelligent security is one of the methods that can be used to respond to these issues. Although research on video data security has been actively conducted, not enough studies have been published on video data security that also addresses intelligent security. Specifically, a classification system for research on video data security has not been provided, and no systematic analysis has been conducted for advanced research. Thus, the purpose of this is to fill in these gaps in existing research. This study offers a classification of research on video data security based on the collection and analysis of related works. Moreover, this study presents an analysis of research on video data security technologies combined with intelligent technologies based on SLR methodology.

The commercialization and advancement of unmanned aerial vehicles (UAVs) have increased in the past decades for surveillance. UAVs use gimbal cameras and LIDAR technology for monitoring as they are resource-constrained devices that are composed of limited storage, battery power, and computing capacity. Thus, the UAV’s surveillance camera and LIDAR data must be analyzed, extracted, and stored efficiently. Video synopsis is an efficient methodology that deals with shifting foreground objects in time and domain space, thus creating a condensed video for analysis and storage. However, traditional video synopsis methodologies are not applicable for making an abnormal behavior synopsis (e.g., creating a synopsis only of the abnormal person carrying a revolver). To mitigate this problem, we proposed an early fusion-based video synopsis. There is a drastic difference between the proposed and the existing synopsis methods as it has several pressing characteristics. Initially, we fused the 2D camera and 3D LIDAR point cloud data; Secondly, we performed abnormal object detection using a customized detector on the merged data and finally extracted only the meaningful data for creating a synopsis. We demonstrated satisfactory results while fusing, constructing the synopsis, and detecting the abnormal object; we achieved an mAP of 85.97%.

Malicious insiders who possess system access and security expertise are notoriously difficult to detect and can inflict severe financial damage. While recent advances in deep learning have demonstrated impressive accuracy in detecting insider threats, these models often assume the presence of well-defined or previously known anomalies. In practical organizational environments, however, threats may manifest as novel, subtle, or context-dependent behaviors that are not captured by existing patterns. Detecting such anomalies necessitates the extraction and analysis of rich behavioral features from large-scale insider activity data—an approach that, while effective, often leads to increased model complexity and computational burden. This, in turn, impedes real-time responsiveness and operational viability, potentially resulting in delayed threat mitigation and financial losses. Therefore, there is a pressing need for lightweight yet robust insider threat detection frameworks that can ensure timely and efficient deployment without compromising detection performance. To address this challenge, this paper proposes the Insider-Specific Feature Learning Autoencoder (ISFL-AE), a model designed to achieve high detection accuracy and fast processing speed. Unlike traditional reconstruction-based anomaly detection models—which use a single set of model parameters to reconstruct normal behavior for all insiders regardless of their role, authority level, or other attributes—ISFL-AE tailors its feature learning to insider-specific characteristics. ISFL-AE operates with the same number of parameters as a conventional autoencoder (AE), maintaining comparable processing speed while significantly improving detection performance. We evaluated ISFL-AE using the CERT r4.2 and r6.2 datasets. The results show that, while processing data at the same speed as a standard AE, ISFL-AE delivered markedly higher detection accuracy. Furthermore, it outperformed other machine learning models in detection accuracy and processing speed. Furthermore, our empirical results demonstrate that integrating insider-specific feature learning into autoencoder-based deep learning architectures significantly enhances anomaly detection performance, all while preserving real-time processing efficiency.