Automated Approach for DDOS Attacks Detection Based on Naive Bayes Multinomial Classifier

Ajagekar, Shital K.; Jadhav, Vaishali

doi:10.1109/icoei.2018.8553848

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…The first approach consists in the separate use of some machine learning algorithms to detect the RoQ attacks, namely Multi-layer Perceptron (MLP) neural network with backpropagation, K-Nearest Neighbors (K-NN), Support Vector Machine (SVM) and Multinomial Naive Bayes (MNB). These four algorithms have been widely investigated for detecting high-rate DDoS attacks, e, g. MLP used in [57][58][59], K-NN used in [57,59,60], SVM used in [58][59][60][61] and MNB used in [62][63][64]. The second approach consists in the joint use of a combination of three distinct methods: Fuzzy Loggic (FL), MLP and the Euclidean Distance (ED).…”

Section: Introductionmentioning

confidence: 99%

Detection of reduction-of-quality DDoS attacks using Fuzzy Logic and machine learning algorithms

et al. 2021

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Distributed Denial of Service (DDoS) attacks are still among the most dangerous attacks on the Internet. With the advance of methods for detecting and mitigating these attacks, crackers have improved their skills in creating new DDoS attack types with the aim of mimicking normal traffic behaviour therefore becoming silently powerful. Among these advanced DDoS attack types, the so-called lowrate DoS attacks aim at keeping a low level of network traffic. In this paper, we study one of these techniques, called Reduction of Quality (RoQ) attack. To investigate the detection of this type of attack, we evaluate and compare the use of four machine learning algorithms: Multi-Layer Perceptron (MLP) neural network with backpropagation, K-Nearest Neighbors (K-NN), Support Vector Machine (SVM) and Multinomial Naive Bayes (MNB). We also propose an approach for detecting this kind of attack based on three methods: Fuzzy Logic (FL), MLP and Euclidean Distance (ED). We evaluate and compare the approach based on FL, MLP and ED to the above machine learning algorithms using both emulated and real traffic traces. We show that among the four Machine Learning algorithms, the best classification results are obtained with MLP, which, for emulated traffic, leads to a F1-score of 98.04% for attack traffic and 99.30% for legitimate traffic, while, for real traffic, it leads to a F1-score of 99.87% for attack traffic and 99.95% for legitimate traffic. Regarding the approach using FL, MLP and EC, for classification of emulated traffic, we obtained a F1-score of 98.80% for attack traffic and 99.60% for legitimate traffic, while, for real traffic, we obtained a F1-score of 100% for attack traffic and 100% for legitimate traffic. However, the better performance of the approach based on FL, MLP and ED is obtained at the cost of larger execution time, since MLP required 0.74 ms and 0.87ms for classification of the emulated and real traffic datasets, respectively, where as the approach using FL, MLP and ED required 11'46" and 46'48" to classify the emulated and real traffic datasets, respectively.

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Section: Introductionmentioning

confidence: 99%

Detection of reduction-of-quality DDoS attacks using Fuzzy Logic and machine learning algorithms

et al. 2021

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“…is method improves the efficiency of bug classification by reducing the dimensions of the terms [17]. Some researchers have proposed automatic bug-dispatching techniques, such as support-vector machine (SVM) [18], k-nearest neighbor algorithm (KNN) [19], and naive Bayes multinomial (NBM) [20], to ensure that bug reports are assigned to the appropriate developers to improve the accuracy of bug allocation. Xia et al used different combinations of imbalanced learning strategies and text classification algorithms to identify high-impact bug reports.…”

Section: Introductionmentioning

confidence: 99%

Combining Imbalance Learning Strategy and Multiclassifier Estimator for Bug Report Classification

Guo

Wang

Wei

et al. 2020

Mathematical Problems in Engineering

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Since a large number of bug reports are submitted to the bug repository every day, efficiently assigning bug reports to the correct developer is a considerable challenge. Because of the large differences between the different components of different projects, the current bug classification mainly relies on the components of the bug report to dispatch bug reports to the designated developer or developer community. Unfortunately, the component information of the bug report is filled in by default according to the bug submitter and the result is often incorrect. us, an automatic technology that can identify high-impact bug reports can help developers to be aware of them early, rectify them quickly, and minimize the damages they cause. In this paper, we propose a method based on the combination of imbalanced learning strategies such as random undersampling (RUS), random oversampling (ROS), synthetic minority oversampling technique (SMOTE), and AdaCost algorithms with multiclass classification methods, OVO and OVA, to solve bug reports component classification problem. We investigate the effectiveness of different combinations, i.e., variants, each of which includes a specific imbalance learning strategy and a specific classification algorithm. We mainly perform an analytical study on five open bug repositories (Eclipse, Mozilla, GCC, OpenOffice, and NetBeans). e results show that different variants have different performance for bug reports component identification and the best performance variants are combined with the imbalanced learning strategy RUS and the OVA method based on the SVM classifier.

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Automated Approach for DDOS Attacks Detection Based on Naive Bayes Multinomial Classifier

Cited by 2 publications

References 7 publications

Detection of reduction-of-quality DDoS attacks using Fuzzy Logic and machine learning algorithms

Detection of reduction-of-quality DDoS attacks using Fuzzy Logic and machine learning algorithms

Combining Imbalance Learning Strategy and Multiclassifier Estimator for Bug Report Classification

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