DoS attack detection technique using back propagation neural network

Khandelwal, Monika; Gupta, Deepak; Bhale, Pradeep

doi:10.1109/icacci.2016.7732185

Cited by 16 publications

(4 citation statements)

References 3 publications

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“…On top of that, Information gain algorithm was applied to decrease the number of parameters considered by the algorithm. Additionally, the authors in [10] proposed the use of a back propagation neural network that was trained with data containing the CPU usage, Frame length and packet rate. Additionally, solutions for DoS attack detection in cloud were reviewed.…”

Section: Related Workmentioning

confidence: 99%

“…Among the reviewed solutions, researches such as [6], [7], [8], [15], [19], and [14], are solutions that implicate high complexity and it is not possible to include simple mechanisms that would prevent the analysis of all the data in real time in order to detect a DoS attack, meaning that it is difficult to reduce the DoS attack detection time. In case of the solutions in [9], [10], [11], [20], and [22], they applied machine learning algorithms that were trained with normal datasets and attack datasets. In those cases, instead of analyzing all real time data with the trained models, those solutions can apply methods to only consider suspicious data in the analysis with the trained models, reducing detection time without affecting the efficiency.…”

Section: Introductionmentioning

confidence: 99%

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An Attack-based Filtering Scheme for Slow Rate Denial-of-Service Attack Detection in Cloud Environment

Gutierrez¹,

Lee²

2020

J Multimed Inf Syst

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Nowadays, cloud computing is becoming more popular among companies. However, the characteristics of cloud computing such as a virtualized environment, constantly changing, possible to modify easily and multi-tenancy with a distributed nature, it is difficult to perform attack detection with traditional tools. This work proposes a solution which aims to collect traffic packets data by using Flume and filter them with Spark Streaming so it is possible to only consider suspicious data related to HTTP Slow Rate Denial-of-Service attacks and reduce the data that will be stored in Hadoop Distributed File System for analysis with the FP-Growth algorithm. With the proposed system, we also aim to address the difficulties in attack detection in cloud environment, facilitating the data collection, reducing detection time and enabling an almost real-time attack detection.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Attack-based Filtering Scheme for Slow Rate Denial-of-Service Attack Detection in Cloud Environment

Gutierrez¹,

Lee²

2020

J Multimed Inf Syst

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“…In these conditions, numerous techniques depend on distance or similarity in feature sets, for example, discriminant analysis and clustering [8][9][10]. In various problems, machine learning methods such as neural network [11], k-nearest neighbour algorithm [12], support vector machines [13], and convolutional neural network [14,15] is used for classification purpose. Various fuzzy classifiers for different problems have been developed.…”

Section: Introductionmentioning

confidence: 99%

A Pattern Classification Model for Vowel Data Using Fuzzy Nearest Neighbor

Khandelwal¹,

Rout²,

Umer³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Classification of the patterns is a crucial structure of research and applications. Using fuzzy set theory, classifying the patterns has become of great interest because of its ability to understand the parameters. One of the problems observed in the fuzzification of an unknown pattern is that importance is given only to the known patterns but not to their features. In contrast, features of the patterns play an essential role when their respective patterns overlap. In this paper, an optimal fuzzy nearest neighbor model has been introduced in which a fuzzification process has been carried out for the unknown pattern using k nearest neighbor. With the help of the fuzzification process, the membership matrix has been formed. In this membership matrix, fuzzification has been carried out of the features of the unknown pattern. Classification results are verified on a completely llabelled Telugu vowel data set, and the accuracy is compared with the different models and the fuzzy k nearest neighbor algorithm. The proposed model gives 84.86% accuracy on 50% training data set and 89.35% accuracy on 80% training data set. The proposed classifier learns well enough with a small amount of training data, resulting in an efficient and faster approach.

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“…The correct rate of the detecting model for DoS attacks can reach 99.66%. The protocol proposed in [10] trained BP neural network according to CPU state, data length and request rate. The advantage is that the input attributes are small and the model is simple.…”

Section: Introductionmentioning

confidence: 99%

Research on Detection and Defense Mechanisms of DoS Attacks Based on BP Neural Network and Game Theory

Gao

Zhang

et al. 2019

IEEE Access

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DoS (Denial of Service) attacks are becoming one of the most serious security threats to global networks. We analyze the existing DoS detection methods and defense mechanisms in depth. In this paper, BP (back propagation) neural networks and game theory are introduced to design detection methods and defense mechanisms for the DoS attacks. The BP neural network DoS attacks detection model uses KDDCUP99 as the dataset and selects multiple feature vectors from the dataset that can efficiently identify DoS attacks by large-scale training, which improves the accuracy of detecting DoS attacks to 99.977%. Furthermore, we use game theory to perform secondary analysis on DoS attacks that are not recognized by the neural network model, so that the detection rate of Dos attacks increases from 99.97% to 99.998%. Finally, we propose a DoS attacks defense strategy based on game theory. The simulation results show that the proposed detection method and defense strategy are effective for DoS attacks.

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DoS attack detection technique using back propagation neural network

Cited by 16 publications

References 3 publications

An Attack-based Filtering Scheme for Slow Rate Denial-of-Service Attack Detection in Cloud Environment

An Attack-based Filtering Scheme for Slow Rate Denial-of-Service Attack Detection in Cloud Environment

A Pattern Classification Model for Vowel Data Using Fuzzy Nearest Neighbor

Research on Detection and Defense Mechanisms of DoS Attacks Based on BP Neural Network and Game Theory

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