DeepContainer: A Deep Learning-based Framework for Real-time Anomaly Detection in Cloud-Native Container Environments
This paper presents DeepContainer, a novel deep learning-based framework for real-time anomaly detection in cloud-native container environments. The proposed framework addresses critical security challenges in containerized infrastructures through an innovative integration of neural network archite...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Scientific Publication Center
2025-01-01
|
| Series: | Journal of Advanced Computing Systems |
| Subjects: | |
| Online Access: | https://scipublication.com/index.php/JACS/article/view/79 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849222383511011328 |
|---|---|
| author | Ke Xiong Zhonghao Wu Xuzhong Jia |
| author_facet | Ke Xiong Zhonghao Wu Xuzhong Jia |
| author_sort | Ke Xiong |
| collection | DOAJ |
| description |
This paper presents DeepContainer, a novel deep learning-based framework for real-time anomaly detection in cloud-native container environments. The proposed framework addresses critical security challenges in containerized infrastructures through an innovative integration of neural network architectures and automated response mechanisms. DeepContainer implements a multi-layered detection approach, combining feature engineering techniques with optimized deep learning models to identify security anomalies across diverse container workloads. The system architecture incorporates specialized components for real-time data collection, processing, and analysis, achieving a detection accuracy of 96.8% with an average response latency of 7.3ms. Experimental evaluation in large-scale Kubernetes environments demonstrates significant performance improvements over existing solutions, including a 39.7% reduction in detection latency and a 25.5% decrease in resource utilization. The framework maintains linear scalability up to 10,000 monitored containers while achieving a false positive rate of 0.008. Comprehensive security testing validates the system's effectiveness across multiple attack vectors, including network-based attacks, resource exhaustion attempts, and access violations. Through automated response capabilities and sophisticated threat classification mechanisms, DeepContainer establishes a robust security foundation for modern containerized applications, addressing critical gaps in existing container security solutions.
|
| format | Article |
| id | doaj-art-7b729ef045a34f158dbb42e7f89cd1cb |
| institution | Kabale University |
| issn | 3066-3962 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Scientific Publication Center |
| record_format | Article |
| series | Journal of Advanced Computing Systems |
| spelling | doaj-art-7b729ef045a34f158dbb42e7f89cd1cb2025-08-26T05:58:53ZengScientific Publication CenterJournal of Advanced Computing Systems3066-39622025-01-015110.69987/JACS.2025.50101DeepContainer: A Deep Learning-based Framework for Real-time Anomaly Detection in Cloud-Native Container EnvironmentsKe Xiong0Zhonghao Wu1 Xuzhong Jia2Computer Science, University of Southern California, CA, USAComputer Engineering, New York University, NY, USAComputer Application Technology, Hunan University of Technology, HuNan, China This paper presents DeepContainer, a novel deep learning-based framework for real-time anomaly detection in cloud-native container environments. The proposed framework addresses critical security challenges in containerized infrastructures through an innovative integration of neural network architectures and automated response mechanisms. DeepContainer implements a multi-layered detection approach, combining feature engineering techniques with optimized deep learning models to identify security anomalies across diverse container workloads. The system architecture incorporates specialized components for real-time data collection, processing, and analysis, achieving a detection accuracy of 96.8% with an average response latency of 7.3ms. Experimental evaluation in large-scale Kubernetes environments demonstrates significant performance improvements over existing solutions, including a 39.7% reduction in detection latency and a 25.5% decrease in resource utilization. The framework maintains linear scalability up to 10,000 monitored containers while achieving a false positive rate of 0.008. Comprehensive security testing validates the system's effectiveness across multiple attack vectors, including network-based attacks, resource exhaustion attempts, and access violations. Through automated response capabilities and sophisticated threat classification mechanisms, DeepContainer establishes a robust security foundation for modern containerized applications, addressing critical gaps in existing container security solutions. https://scipublication.com/index.php/JACS/article/view/79Cloud-Native SecurityContainer Anomaly DetectionDeep LearningReal-time Threat Detection |
| spellingShingle | Ke Xiong Zhonghao Wu Xuzhong Jia DeepContainer: A Deep Learning-based Framework for Real-time Anomaly Detection in Cloud-Native Container Environments Journal of Advanced Computing Systems Cloud-Native Security Container Anomaly Detection Deep Learning Real-time Threat Detection |
| title | DeepContainer: A Deep Learning-based Framework for Real-time Anomaly Detection in Cloud-Native Container Environments |
| title_full | DeepContainer: A Deep Learning-based Framework for Real-time Anomaly Detection in Cloud-Native Container Environments |
| title_fullStr | DeepContainer: A Deep Learning-based Framework for Real-time Anomaly Detection in Cloud-Native Container Environments |
| title_full_unstemmed | DeepContainer: A Deep Learning-based Framework for Real-time Anomaly Detection in Cloud-Native Container Environments |
| title_short | DeepContainer: A Deep Learning-based Framework for Real-time Anomaly Detection in Cloud-Native Container Environments |
| title_sort | deepcontainer a deep learning based framework for real time anomaly detection in cloud native container environments |
| topic | Cloud-Native Security Container Anomaly Detection Deep Learning Real-time Threat Detection |
| url | https://scipublication.com/index.php/JACS/article/view/79 |
| work_keys_str_mv | AT kexiong deepcontaineradeeplearningbasedframeworkforrealtimeanomalydetectionincloudnativecontainerenvironments AT zhonghaowu deepcontaineradeeplearningbasedframeworkforrealtimeanomalydetectionincloudnativecontainerenvironments AT xuzhongjia deepcontaineradeeplearningbasedframeworkforrealtimeanomalydetectionincloudnativecontainerenvironments |