Traffic Monitoring and Abnormality Detection Methods for Decentralized Distributed Networks

Ageyev, Dmytro; Радівілова, Тамара

doi:10.1007/978-3-030-95161-0_13

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…The chapter that is authored by Ageyev et al and is published in [68] proposes a taxonomy of attacks and intrusions, as well as an analysis of anomalies and intrusions relative to decentralised distributed networks. The main focus pertains to intrusion detection systems, and to the monitoring of network traffic anomalies.…”

Section: Network Monitoringmentioning

confidence: 99%

Real-Time Monitoring and Management of Hardware and Software Resources in Heterogeneous Computer Networks through an Integrated System Architecture

Aldea,

Bocu,

Solca

2023

Symmetry

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The theoretical and practical progress that has occurred in the field of computer networks during the past fifteen years has enhanced the economical efficiency and social relevance of related real-world use cases. Nevertheless, this ubiquitous usage has also introduced numerous security risks. Therefore, monitoring hardware and software resources represents one of the main instruments used in order to prevent potential attacks and to ensure the security and reliability of a network. Various solutions have been reported in the related scientific literature. In essence, most of the existing approaches are not suitable to implement a real-time hardware monitoring and management solution, particularly in heterogeneous networks. Therefore, the main contribution of this paper is represented by an architectural and implementational model, which is effective in order to build an interconnected system that can help system and network administrators to secure a network. This requirement is met by considering symmetrical design and implementation features related to various operating systems. Thus, the existing symmetrical relationships among identified parameters allow for the data to be wrapped into the same custom network packages, which are transported over the communication medium or are stored using the same data structures or tables. The system has been thoroughly assessed considering several real-world use case scenarios, and the results demonstrate that the proposed model can be applied to software-defined networks, which can be protected by relevant intrusion detection systems (IDS).

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Section: Network Monitoringmentioning

confidence: 99%

Real-Time Monitoring and Management of Hardware and Software Resources in Heterogeneous Computer Networks through an Integrated System Architecture

Aldea,

Bocu,

Solca

2023

Symmetry

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“…The authors of [9] suggest a method for formalizing the normal and abnormal behavior of the system in a set of useful features, and criteria are developed that enable the detection and identification of various forms of network anomalies in order to address the issue of looking for anomalies in networks. The study explores statistically based techniques for anomaly detection, including fractal traffic analysis.…”

Section: Related Workmentioning

confidence: 99%

A Blockchained AutoML Network Traffic Analyzer to Industrial Cyber Defense and Protection

et al. 2023

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Network traffic analysis can raise privacy concerns due to its ability to reveal sensitive information about individuals and organizations. This paper proposes a privacy-preserving Block-chained AutoML Network Traffic Analyzer (BANTA). The system securely stores network traffic logs in a decentralized manner, providing transparency and security. Differential privacy algorithms protect sensitive information in the network flow logs while allowing administrators to analyze network traffic without the risk of leakages. The BANTA uses blockchain technology, where smart contracts automate the process of network traffic analysis, and a multi-signature system ensures the system’s security, safety, and reliability. The proposed approach was evaluated using a real-world network traffic dataset. The results demonstrate the system’s high accuracy and real-time anomaly detection capabilities, which makes it well-suited for scalable cybersecurity operations. The system’s privacy protection, decentralized storage, automation, multi-signature system, and real-world effectiveness ensure that the organization’s data is private, secure, and effectively protected from cyber threats, which are the most vexing issue of modern cyber-physical systems.

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“…Especially, network anomalies consume additional network resources, and pose significant influence on performance of the communication networks. According to [2], network anomalies can be roughly classified into the following categories,…”

Section: Introductionmentioning

confidence: 99%

Network Traffic Anomaly Detection: A Revisiting to Gaussian Process and Sparse Representation

Wang

Nakachi

2024

IEICE Trans. Fundamentals

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Seen from the Internet Service Provider (ISP) side, network traffic monitoring is an indispensable part during network service provisioning, which facilitates maintaining the security and reliability of the communication networks. Among the numerous traffic conditions, we should pay extra attention to traffic anomaly, which significantly affects the network performance. With the advancement of Machine Learning (ML), data-driven traffic anomaly detection algorithms have established high reputation due to the high accuracy and generality. However, they are faced with challenges on inefficient traffic feature extraction and high computational complexity, especially when taking the evolving property of traffic process into consideration. In this paper, we proposed an online learning framework for traffic anomaly detection by embracing Gaussian Process (GP) and Sparse Representation (SR) in two steps: 1). To extract traffic features from past records, and better understand these features, we adopt GP with a special kernel, i.e., mixture of Gaussian in the spectral domain, which makes it possible to more accurately model the network traffic for improving the performance of traffic anomaly detection. 2). To combat noise and modeling error, observing the inherent self-similarity and periodicity properties of network traffic, we manually design a feature vector, based on which SR is adopted to perform robust binary classification. Finally, we demonstrate the superiority of the proposed framework in terms of detection accuracy through simulation.

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Traffic Monitoring and Abnormality Detection Methods for Decentralized Distributed Networks

Cited by 9 publications

References 16 publications

Real-Time Monitoring and Management of Hardware and Software Resources in Heterogeneous Computer Networks through an Integrated System Architecture

Real-Time Monitoring and Management of Hardware and Software Resources in Heterogeneous Computer Networks through an Integrated System Architecture

A Blockchained AutoML Network Traffic Analyzer to Industrial Cyber Defense and Protection

Network Traffic Anomaly Detection: A Revisiting to Gaussian Process and Sparse Representation

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