With the rapid evolution of mobile connectivity, security threats such as distributed denial of service attacks have become a significant concern for network stability. This research proposes a defense framework that integrates zero trust architecture with a real-time monitoring system to enhance network protection. The system employs continuous identity verification and dynamic permission management to ensure that only authenticated devices can transmit data. By evaluating different machine learning approaches, the study demonstrates that a deep learning model focusing on sequence dependencies provides superior accuracy in identifying malicious traffic compared to traditional classification methods. Practical testing indicates that the proposed architecture effectively mitigates the impact of attacks and prevents the internal spread of threats, offering a more robust security solution than conventional network defenses.
With the rapid development of electronic technology, navigation and control technology, low-altitude flying targets have been increasingly widely used. Due to the low flight altitude, small size and high movement speed of low-altitude targets, these characteristics pose significant challenges to the tracking of low-altitude targets. How to achieve accurate and efficient tracking of low-altitude targets has become an urgent problem to be solved. To address this issue, this paper designs a low-altitude target detection and tracking method mainly from two aspects: the accuracy and real-time performance of low-altitude target tracking. Aiming at the requirements of real-time detection and tracking of low-altitude targets, two detection schemes based on YOLOv8 and YOLOv8-Ghost are discussed and analyzed. Through comparative experiments, the YOLOv8 algorithm exhibits excellent performance in low-altitude target detection due to its high accuracy, but it suffers from the problems of high power consumption and slow inference speed. Therefore, on the basis of YOLOv8, this paper integrates Ghost Convolution, which reduces power consumption and improves inference speed. Then, combined with the ByteTrack multi-object tracking algorithm, which consists of a target detection module, a trajectory prediction module and a hierarchical data association module, the detection and tracking of multiple low-altitude targets are realized. The datasets adopted in the experiments include low-altitude target images and video datasets (covering birds and unmanned aerial vehicles) [11]. The YOLOv8-Ghost algorithm is applied in the detection stage, achieving favorable detection results, with reduced model parameters, lower power consumption and significantly improved inference speed. The ByteTrack algorithm is used in the tracking stage, outputting visualized results with good tracking performance.
Mission-critical services, such as emergency communications, active safe driving (ASD), eV2X, and LEO satellite networking, require low-latency and reliable transmission in 5G-A TN) and 6G NTN LEO systems. In mixed-numerology operation, static BWP configuration and conventional scheduling may lead to vRB MX, RB fragmentation, and inefficient resource usage. To address these issues, this paper adopts an extended Sigmoid-based Cost-based Flow Scheduling (eSig) framework for queue-level flow scheduling. eSig evaluates each HoL packet by using a net-profit metric that combines QoS demand, vRB state, carrying cost, and service reward. The carrying cost is modeled by a dynamic exponential function, while the service reward is modeled by an adaptive extended Sigmoid function with MCS-aware and resource-aware factors. Simulation results show that eSig enhances vRB utilization and total net-profit while keeping delay within the required bound.
Telemetry generated by honeypots has a wealth of attack information but lacks in actionable semantics to inform triage decisions. While security experts usually get to deal with shell commands, protocol fields, partial payloads, and unsuccessful authentication attempts, triage calls for more abstract decision-making regarding the crucial attack mechanism used. This semantic mismatch poses a challenge for CAPEC inference and slows down decision-making. To solve the issue, we propose a decoupled pipeline that builds a behavioral profile out of raw data, uses an LLM in the organization’s premises only to determine the attacker’s intent, and finally retrieves potential CAPEC entries by running two simultaneous searches, semantic and lexical, and combining results with Reciprocal Rank Fusion. Evaluating our approach on 25 behavioral profiles, generated from data collected by Cowrie, Honeytrap, Dionaea, SentryPeer, and CiscoASA honeypots, yields the following metrics: P@1 = 0.760, MRR = 0.973, nDCG@5 = 0.741, Precision@5 = 0.632, Recall@5 = 0.511. An analysis of the ablative variants suggests that neither branch alone is capable of inferring attack intents accurately. In turn, compared to a monolithic frontier LLM, our system shows an acceptable performance trade-off and can be considered auditable, compact, and feasible for on-premises SOC deployment.
In conventional three-dimensional (3D) content production, multiple processes, such as modeling, texture settings, material adjustment, spatial arrangement, and rendering, must be performed separately using different tools. These operations require specialized knowledge of 3D computer graphics and virtual reality development, making it extremely difficult for nonexperts to create and experience 3D content. In addition, file conversion between processes, scale matching, and lighting settings must be performed manually, making production a complicated and time-consuming process. In this study, we developed a technical platform to automatically generate a 3D model from a single still image and visualize and experience the generated model in a virtual space. The proposed system was evaluated experimentally using the system usability scale with 50 paricipants, mainly students. The results were generally favorable.
