Fuzzy-Taylor-elephant herd optimization inspired Deep Belief Network for DDoS attack detection and comparison with state-of-the-arts algorithms

Velliangiri, S.; Pandey, Hari Mohan

doi:10.1016/j.future.2020.03.049

Cited by 55 publications

(29 citation statements)

References 23 publications

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“…In the paper [12], a fuzzy method called (T-EHO) based on DBN classification algorithm is presented for detecting DDoS attacks. The method of learning fuzzy rules was evaluated when the FT-EHO algorithm was used instead of the genetic algorithm.…”

Section: Related Workmentioning

confidence: 99%

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A Hybrid Mechanism to Detect DDoS Attacks in Software Defined Networks

Dehkordi

Aghaei

Boroujeni

2021

mjee

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DDoS (Distributed Denial-of-Service) attacks are among the cyberattacks that are increasing day by day and have caused problems for computer network servers. With the advent of SDN networks, they are not immune to these attacks, and due to the software-centric nature of these networks, this type of attack can be much more difficult for them, ignoring effective parameters such as port and Source IP in detecting attacks, providing costly solutions which are effective in increasing CPU load, and low accuracy in detecting attacks are of the problems of previously presented methods in detecting DDoS attacks. Given the importance of this issue,the purpose of this paper is to increase the accuracy of DDoS attack detection using the second order correlation coefficient technique based on ∅-entropy according to source IP and selection of optimal features.To select the best features, by examining the types of feature selection algorithms and search methods, the WrapperSubsetEval feature selection algorithm, the BestFirst search method, and the best effective features were selected. This study was performed on CTU-13 and ISOT datasets and the results were compared with other methods. The accuracy of the detection in this work indicates the high efficiency of the proposed approach compared to other similar methods.

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Section: Related Workmentioning

confidence: 99%

“…Accuracy, Precision, F-Measure, TPR, and FPR from Equations ( 8) to (12) are used to evaluate the proposed approach. In these equations  TP: the number of samples of attack flows that have been correctly detected.…”

Section: Performance Measurementioning

confidence: 99%

A Hybrid Mechanism to Detect DDoS Attacks in Software Defined Networks

Dehkordi

Aghaei

Boroujeni

2021

mjee

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“…Gopal Singh Kushwah and Virender Ranga [17] presented DDoS attacks detection model in the cloud. This built with the Voting Extreme Learning Machine.…”

Section: Literature Surveymentioning

confidence: 99%

An IPSO-KELM based malicious behaviour detection and SHA256-RSA based secure data transmission in the cloud paradigm

2021

KSII TIIS

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Cloud Computing has emerged as an extensively used technology not only in the IT sector but almost in all sectors. As the nature of the cloud is distributed and dynamic, the jeopardies present in the current implementations of virtualization, numerous security threats and attacks have been reported. Considering the potent architecture and the system complexity, it is indispensable to adopt fundamentals. This paper proposes a secure authentication and data sharing scheme for providing security to the cloud data. An efficient IPSO-KELM is proposed for detecting the malicious behaviour of the user. Initially, the proposed method starts with the authentication phase of the data sender. After authentication, the sender sends the data to the cloud, and the IPSO-KELM identifies if the received data from the sender is an attacked one or normal data i.e. the algorithm identifies if the data is received from a malicious sender or authenticated sender. If the data received from the sender is identified to be normal data, then the data is securely shared with the data receiver using SHA256-RSA algorithm. The upshot of the proposed method are scrutinized by identifying the dissimilarities with the other existing techniques to confirm that the proposed IPSO-KELM and SHA256-RSA works well for malicious user detection and secure data sharing in the cloud.

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“…Recently, DBN is most valued for its versatile ability and has exposed great success in unsupervised feature dimensionality reduction and supervised pattern classification [41] , [42] . But only limited studies in literature have used DBN in the field of intrusion detection [43] , [44] . Also, the authors in these studies have not focused to investigate the influence of feature selection on DBN.…”

Section: The Classifier Scheme For Intrusion Detectionmentioning

confidence: 99%

Comprehensive analysis and recommendation of feature evaluation measures for intrusion detection

Binbusayyis

Vaiyapuri

2020

Heliyon

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The revolutionary advances in network technologies have spearheaded the design of advanced cyberattacks to surpass traditional security defense with dreadful consequences. Recently, Intrusion Detection System (IDS) is considered as a pivotal element in network security infrastructures to achieve solid line of protection against cyberattacks. The prime challenges presented to IDS are curse of high dimensionality and class imbalance that tends to increase the detection time and degrade the efficiency of IDS. As a result, feature selection plays an important role in enabling to identify the most significant features for intrusion detection. Although, several feature evaluation measures are being proposed for feature selection in literature, there is no consensus on which measures are best for intrusion detection. Therein, this work aims at recommending the most appropriate feature evaluation measure for building an efficient IDS. In this direction, four filter-based feature evaluation measures that stem from different theories such as Consistency, Correlation, Information and Distance are investigated for their potential implications in enhancing the detection ability of IDS model for different classes of attacks. Along with this, the influence of the selected features on classification accuracy of an IDS model is analyzed using four different categories of classifiers namely, K-nearest neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM) and Deep Belief Network (DBN). Finally, a two-step statistical significance test is conducted on the experimental results to determine which feature evaluation measure contributes statistically significant difference in IDS performance. All the experimental comparisons are performed on two benchmark intrusion detection datasets, NSL-KDD and UNSW-NB15. In these experiments, consistency measure has best influenced the IDS model in improving the detection ability with regard to detection rate (DR), false alarm rate (FAR), kappa statistics (KS) and identifying the most significant features for intrusion detection. Also, from the analysis results, it is revealed that RF is the ideal classifier to be used in conjunction with any of these four feature evaluation measures to achieve better detection accuracy than others. From the statistical results, we recommend the use of consistency measure for designing an efficient IDS in terms of DR and FAR.

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Fuzzy-Taylor-elephant herd optimization inspired Deep Belief Network for DDoS attack detection and comparison with state-of-the-arts algorithms

Cited by 55 publications

References 23 publications

A Hybrid Mechanism to Detect DDoS Attacks in Software Defined Networks

A Hybrid Mechanism to Detect DDoS Attacks in Software Defined Networks

An IPSO-KELM based malicious behaviour detection and SHA256-RSA based secure data transmission in the cloud paradigm

Comprehensive analysis and recommendation of feature evaluation measures for intrusion detection

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