DL-HIDS: deep learning-based host intrusion detection system using system calls-to-image for containerized cloud environment

Joraviya, Nidhi; Gohil, Bhavesh N.; Rao, Udai Pratap

doi:10.1007/s11227-024-05895-3

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…Joraviya et al [102] investigated several key issues, including addressing security challenges arising from containerization in cloud settings and improving the efficacy of intrusion detection systems for monitoring and identifying attacks within containerized environments. The proposed solutions involve employing DL methods, specifically Convolutional Neural Networks (CNNs), for anomaly detection in system call sequences and images derived from these calls.…”

Section: B Ids Anomaly Based On DL -Existing Research Workmentioning

confidence: 99%

Cloud Network Anomaly Detection Using Machine and Deep Learning Techniques— Recent Research Advancements

Abdallah,

Saif Rashed Obaid Alkaabi,

Bark Nasser Douman Alameri

et al. 2024

IEEE Access

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In the rapidly evolving landscape of computing and networking, the concepts of cloud networks have gained significant prominence. Although the cloud network offers on-demand access to shared resources, anomalies pose potential risks to the integrity and security of cloud networks. However, protecting the cloud network against anomalies remains a challenge. Unlike traditional detection techniques, machine learning (ML) and deep learning (DL) offer new and adaptable methods for detecting anomalies in cloud networks. The objective of this study is to comprehensively explore existing ML /DL methods for detecting different anomalies based on distributed denial of service anomaly (DDoS) and intrusion detection systems (IDS) in cloud networks. The study seeks to address the gaps in anomaly detection for cloud networks, proposing potential solutions for anomaly detection in these cloud environments. The ultimate goal is to contribute valuable insights and practical solutions to enhance the security and reliability of cloud networks through effective anomaly detection by ML/ DL techniques. Methodologies for ML/DL are explained, along with their advantages, disadvantages, and respective approaches. In addition, a summary of the comparison between different ML/ DL models is also included.

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Section: B Ids Anomaly Based On DL -Existing Research Workmentioning

confidence: 99%

Cloud Network Anomaly Detection Using Machine and Deep Learning Techniques— Recent Research Advancements

Abdallah,

Saif Rashed Obaid Alkaabi,

Bark Nasser Douman Alameri

et al. 2024

IEEE Access

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A bizarre synthesized cascaded optimized predictor (BizSCOP) model for enhancing security in cloud systems

Menezes,

Jayarin,

Sekar

2024

J Cloud Comp

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Due to growing network data dissemination in cloud, the elasticity, pay as you go options, globally accessible facilities, and security of networks have become increasingly important in today's world. Cloud service providers, including AWS, Azure, GCP, and others, facilitate worldwide expansion within minutes by offering decentralized communication network functions, hence providing security to cloud is still remains a challenging task. This paper aims to introduce and evaluate the Biz-SCOP model, a novel intrusion detection system developed for cloud security. The research addresses the pressing need for effective intrusion detection in cloud environments by combining hybrid optimization techniques and advanced deep learning methodologies. The study employs prominent intrusion datasets, including CSE-CIC-IDS 2018, CIC-IDS 2017, and a cloud intrusion dataset, to assess the proposed model's performance. The study's design involves implementing the Biz-SCOP model using Matlab 2019 software on a Windows 10 OS platform, utilizing 8 GB RAM and an Intel core i3 processor. The hybrid optimization approach, termed HyPSM, is employed for feature selection, enhancing the model's efficiency. Additionally, an intelligent deep learning model, C2AE, is introduced to discern friendly and hostile communication, contributing to accurate intrusion detection. Key findings indicate that the Biz-SCOP model outperforms existing intrusion detection systems, achieving notable accuracy (99.8%), precision (99.7%), F1-score (99.8%), and GEO (99.9%). The model excels in identifying various attack types, as demonstrated by robust ROC analysis. Interpretations and conclusions emphasize the significance of hybrid optimization and advanced deep learning techniques in enhancing intrusion detection system performance. The proposed model exhibits lower computational load, reduced false positives, ease of implementation, and improved accuracy, positioning it as a promising solution for cloud security.

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DL-HIDS: deep learning-based host intrusion detection system using system calls-to-image for containerized cloud environment

Cited by 2 publications

References 35 publications

Cloud Network Anomaly Detection Using Machine and Deep Learning Techniques— Recent Research Advancements

Cloud Network Anomaly Detection Using Machine and Deep Learning Techniques— Recent Research Advancements

A bizarre synthesized cascaded optimized predictor (BizSCOP) model for enhancing security in cloud systems

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