Flow Correlation Degree Optimization Driven Random Forest for Detecting DDoS Attacks in Cloud Computing
TL;DR: The proposed DDoS attack-detection method with enhanced random forest (RF) optimized by genetic algorithm based on flow correlation degree (FCD) feature can effectively detect DDoS attacks in cloud environment with a higher accuracy rate and lower false- and missing-alarm rates compared to existing DDoSAttackDetection methods.
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Abstract: Distributed denial-of-service (DDoS) has caused major damage to cloud computing, and the false- and missing-alarm rates of existing DDoS attack-detection methods are relatively high in cloud environment. In this paper, we propose a DDoS attack-detection method with enhanced random forest (RF) optimized by genetic algorithm based on flow correlation degree (FCD) feature. We define the FCD feature according to the asymmetric and semidirectivity interaction characteristics and use the two-tuples FCD feature consisting of packet-statistical degree (PSD) and semidirectivity interaction abnormality (SDIA) to describe the features of attack flow and normal flow. Then we use a genetic algorithm based on the FCD feature sequences to optimize two key parameters of the decision tree in the RF: the maximum number of decision trees and the maximum depth of every single decision tree. We apply the trained RF model with optimized parameters to generate the classifier to be used for DDoS attack-detection. The experiment shows that the proposed method can effectively detect DDoS attacks in cloud environment with a higher accuracy rate and lower false- and missing-alarm rates compared to existing DDoS attack-detection methods.
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Citations
Smart Detection: An Online Approach for DoS/DDoS Attack Detection Using Machine Learning
Francisco Sales de Lima Filho,Frederico Augusto Fernandes Silveira,Agostinho de Medeiros Brito Junior,Genoveva Vargas-Solar,Luiz F. Q. Silveira +4 more
TL;DR: A machine learning- (ML-) based DoS detection system that makes inferences based on signatures previously extracted from samples of network traffic shows an online detection rate (DR) of attacks above 96%, with high precision (PREC) and low false alarm rate (FAR) using a sampling rate of 20% of network Traffic.
Semi-Supervised K-Means DDoS Detection Method Using Hybrid Feature Selection Algorithm
TL;DR: A semi-supervised weighted k-means detection method using hybrid feature selection (SKM-HFS) and an improved density-based initial cluster centers selection algorithm to solve the problem of outliers and local optimal.
Detection of DDOS Attack using Deep Learning Model in Cloud Storage Application
TL;DR: A novel a feature selection-whale optimization algorithm-deep neural network (FS-WOA–DNN) method is proposed in this research article to mitigate DDoS attack in effective manner.
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An Innovative Perceptual Pigeon Galvanized Optimization (PPGO) Based Likelihood Naïve Bayes (LNB) Classification Approach for Network Intrusion Detection System
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TL;DR: In this paper , the authors proposed an innovative clustering-based classification methodology to precisely detect intrusions from the different types of IDS datasets, where the most recent and extensively used IDS dataset such as NSL-KDD, CICIDS and Bot-IoT have been employed for detecting intrusions.
An efficient SVM based DEHO classifier to detect DDoS attack in cloud computing environment
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