Low-Rate DoS Attack Detection Based on Improved Logistic Regression

Yan, Yudong; Tang, Dan; Zhan, Sijia; Dai, Rui; Chen, Jingwen; Zhu, Ningbo

doi:10.1109/hpcc/smartcity/dss.2019.00076

Cited by 15 publications

(7 citation statements)

References 17 publications

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“…These effects directly impact the availability of a system or service. An AI detection model based on logistic regression (LR) and NB has been proposed as a method for detecting attacks as well as normal scenarios [47], [48]. The authors in [49] presented an intelligent agent system that incorporates the K-nearest neighbors (KNN) algorithm to detect distributed denial-of-service (DDoS) attacks.…”

Section: Ai-based Detection Roadmap and Threat Model Analysismentioning

confidence: 99%

Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems

Abdullahi,

Alhussian,

Aziz

et al. 2024

IEEE Access

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Section: Ai-based Detection Roadmap and Threat Model Analysismentioning

confidence: 99%

Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems

Abdullahi,

Alhussian,

Aziz

et al. 2024

IEEE Access

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“…Currently, the detection of LDoS attacks can be divided into feature-based detection and time–frequency domain detection. Yan et al [ 22 ] extracted the mean, variance, and entropy features of TCP traffic and employed them as features to train an enhanced logistic regression model for the purpose of detecting LDoS attacks. However, the feature extraction method used in this approach was relatively weak.…”

Section: Related Workmentioning

confidence: 99%

A Method for Detecting LDoS Attacks in SDWSN Based on Compressed Hilbert–Huang Transform and Convolutional Neural Networks

Liu

Sun

Zhang

et al. 2023

Sensors

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Currently, Low-Rate Denial of Service (LDoS) attacks are one of the main threats faced by Software-Defined Wireless Sensor Networks (SDWSNs). This type of attack uses a lot of low-rate requests to occupy network resources and hard to detect. An efficient detection method has been proposed for LDoS attacks with the features of small signals. The non-smooth small signals generated by LDoS attacks are analyzed employing the time–frequency analysis method based on Hilbert–Huang Transform (HHT). In this paper, redundant and similar Intrinsic Mode Functions (IMFs) are removed from standard HHT to save computational resources and to eliminate modal mixing. The compressed HHT transformed one-dimensional dataflow features into two-dimensional temporal–spectral features, which are further input into a Convolutional Neural Network (CNN) to detect LDoS attacks. To evaluate the detection performance of the method, various LDoS attacks are simulated in the Network Simulator-3 (NS-3) experimental environment. The experimental results show that the method has 99.8% detection accuracy for complex and diverse LDoS attacks.

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“…Logistic regression [14] is a classification algorithm for predicting binary classes. The value of the outcome or target variable is categorical.…”

Section: Logistic Regressionmentioning

confidence: 99%

Detection of Different DDoS Attacks Using Machine Learning Classification Algorithms

Dasari¹,

Devarakonda²

2021

ISI

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Cyber attacks are one of the world's most serious challenges nowadays. A Distributed Denial of Service (DDoS) attack is one of the most common cyberattacks that has affected availability, which is one of the most important principles of information security. It leads to so many negative consequences in terms of business, production, reputation, data theft, etc. It shows the importance of effective DDoS detection mechanisms to reduce losses. In order to detect DDoS attacks, statistical and data mining methods have not been given good accuracy values. Researchers get good accuracy values while detecting DDoS attacks by using classification algorithms. But researchers, use individual classification algorithms on generalized DDoS attacks. This study used six machine learning classification algorithms to detect eleven different DDoS attacks on different DDoS attack datasets. We used the CICDDoS2019 dataset which is collected from the Canadian Institute of Cyber security in this study. It contains eleven different DDoS attack datasets in CSV file format. On each DDoS attack, we evaluated the effectiveness of the classification methods Logistic regression, Decision tree, Random Forest, Ada boost, KNN, and Naive Bayes, and determined the best classification algorithms for detection.

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Low-Rate DoS Attack Detection Based on Improved Logistic Regression

Cited by 15 publications

References 17 publications

Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems

Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems

A Method for Detecting LDoS Attacks in SDWSN Based on Compressed Hilbert–Huang Transform and Convolutional Neural Networks

Detection of Different DDoS Attacks Using Machine Learning Classification Algorithms

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