Anomaly Detection of Network Traffic Based on Prediction and Self-Adaptive Threshold

Wang, Haiyan

doi:10.14257/ijfgcn.2015.8.6.20

Cited by 9 publications

(5 citation statements)

References 20 publications

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“…Moreover, existing efforts on network traffic anomaly detection include statistics-based methods and machine learning based methods. The proposed hybrid method was based on wavelet analysis and RVM (Relevance Vector Machine) 61 . Malicious attacks and network failures are the security problems with network.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, to ensure network security, detecting anomalies of network traffic is the effective manner. For network traffic prediction, simple statistical models are not good enough 61 . For that, authors 61 proposed a hybrid method that was a combination between statistical and ML techniques to solve the network traffic prediction problem and anomaly detection.…”

Section: Resultsmentioning

confidence: 99%

“…For network traffic prediction, simple statistical models are not good enough 61 . For that, authors 61 proposed a hybrid method that was a combination between statistical and ML techniques to solve the network traffic prediction problem and anomaly detection. The proposed model was composed of (1) network traffic data decomposition into low-frequency components and high-frequency components using wavelet decomposition, then, (2) non-linear model RVM (Relevance Vector Machine) and Auto Regressive Moving Average (ARMA) are employed for prediction.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

A survey on performance evaluation of artificial intelligence algorithms for improving IoT security systems

Meziane,

Ouerdi

2023

Sci Rep

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Security is an important field in the Internet of Things (IoT) systems. The IoT and security are topical domains. Because it was obtained 35,077 document results from the Scopus database. Hence, the AI (Artificial Intelligence) has proven its efficiency in several domains including security, digital marketing, healthcare, big data, industry, education, robotic, and entertainment. Thus, the contribution of AI to the security of IoT systems has become a huge breakthrough. This contribution adopts the artificial intelligence (AI) as a base solution for the IoT security systems. Two different subsets of AI algorithms were considered: Machine Learning (ML) and Deep Learning (DL) methods. Nevertheless, it is difficult to determine which AI method and IoT dataset are best (more suitable) for classifying and/or detecting intrusions and attacks in the IoT domain. The large number of existing publications on this phenomenon explains the need for the current state of research that covers publications on IoT security using AI methods. Thus, this study compares the results regarding AI algorithms that have been mentioned in the related works. The goal of this paper is to compare the performance assessment of the existing AI algorithms in order to choose the best algorithm as well as whether the chosen algorithm can be used for classifying or/and detecting intrusions and attacks in order to improve security in the IoT domain. This study compares these methods in term of accuracy rate. Evaluating the current state of IoT security, AI and IoT datasets is the main aim for considering our future work. After that, this paper proposes, as result, a new and general taxonomy of AI techniques for IoT security (classification and detection techniques). Finally, the obtained results from this assessment survey that was dedicated to research conducted between 2018 and 2023 were satisfactory. This paper provides a good reference for researchers and readers in the IoT domain.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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A survey on performance evaluation of artificial intelligence algorithms for improving IoT security systems

Meziane,

Ouerdi

2023

Sci Rep

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“…The threshold is computed according to a global factor that ascertains the confidence level of the detection. On the other hand, H. Wang [9] defines a threshold for incoming observations by conducting a wavelet analysis and the resulting confidence interval obtained by using the Central Limit Theorem over a sequence of data on a sliding window of fixed size. On their part, Clark et al [10] present a method for detecting concept drifts through a sliding window based approach that relies on a statistical test, with the threshold being adjusted accordingly.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Threshold for Anomaly Detection in ATM Radar Data Streams

Rahaoui

Riberolles

Song

2022

Lecture Notes in Computer Science

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Intrusion Detection Systems (IDS) are capital instruments for protecting ATM networks against intrusion, and subsequently ensuring the integrity of air traffic. An anomaly detection approach in such systems enables the detection of multiple types of attacks with the aid of a threshold as a criterion for differentiating between normal activity and unusual events in the network. IDS with fixed threshold fail to detect the presence of patterns in the data, thus hampering proper detection ability, and requiring regular human intervention. Detection ability of IDS can be improved by establishing an automated system that recognises pattern shifts in evolving data streams and adjusts the threshold accordingly. Our work focuses on designing an algorithm to recognize the occurrence of new patterns and adjust the threshold consequently for enhanced anomaly detection, whilst offering flexibility for different frameworks and scalability to cope with large data streams. In this article, we present an adaptive threshold approach based on extreme value theory, which aims to automatically detect concept drifts in radar data streams. We evaluate our method in a practical scenario of anomaly detection on time series data collected by air traffic radars across France and show that we can achieve a threefold performance improvement over a standard approach using a fixed threshold.

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“…What is more, outliers cannot be within a predetermined range. Adaptive thresholding provides a solution to this problem (Breier and Branišová, 2015;Wang, 2015). On the basis of adaptive thresholding, we develop a method combining the autoregressive model (referred to as AR) to identify candidate breakpoints in the deterioration process.…”

Section: Candidate Breakpoints Identified By Adaptive Thresholding Mementioning

confidence: 99%

BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework

Yang

et al. 2021

Front. Mater.

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The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration process driven by historical measurement data from the comprehensive inspection train (referred to as CIT) is proposed. The identification of when maintenance activities occurred is reformulated as a model selection optimization problem based on Bayesian Information Criterion. An efficient solution algorithm utilizing adaptive thresholding and dynamic programming is proposed for solving this optimization problem. This framework’s validity and practicability are illustrated by the measurement data from the CIT inspection of the mileage section of K21 + 184 to K220 + 308 on the Nanchang-Fuzhou railway track from 2014 to 2019. The results indicate that this framework can overcome the disturbance of contaminated measurement data and accurately estimate when maintenance activities were undertaken without any historical maintenance records. What is more, the adaptive piecewise fitting model provided by this framework can describe the irregular deterioration process of corresponding rail track sections.

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Anomaly Detection of Network Traffic Based on Prediction and Self-Adaptive Threshold

Abstract: Abstract

Cited by 9 publications

References 20 publications

A survey on performance evaluation of artificial intelligence algorithms for improving IoT security systems

A survey on performance evaluation of artificial intelligence algorithms for improving IoT security systems

Adaptive Threshold for Anomaly Detection in ATM Radar Data Streams

BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework

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