Preemptive modelling towards classifying vulnerability of DDoS attack in SDN environment

Narender, M.; Yuvaraju, B N

doi:10.11591/ijece.v10i2.pp1599-1611

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…The effectiveness of ML techniques has led researchers to use them in topical areas such as IoT [52], [53], online social network [54], DoS detection, DDoS [55]- [60], and distributed reflexion denial of service (DRDoS) [61]. However, obtaining good results depends on the techniques used, and the way in which we create our models.…”

Section: Resultsmentioning

confidence: 99%

Machine learning-based intrusion detection system for detecting web attacks

Abdou Vadhil,

Lemine Salihi,

Farouk Nanne

2024

IJ-AI

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<p>The increasing use of smart devices results in a huge amount of data, which raises concerns about personal data, including health data and financial data. This data circulates on the network and can encounter network traffic at any time. This traffic can either be normal traffic or an intrusion created by hackers with the aim of injecting abnormal traffic into the network. Firewalls and traditional intrusion detection systems detect attacks based on signature patterns. However, this is not sufficient to detect advanced or unknown attacks. To detect different types of unknown attacks, the use of intelligent techniques is essential. In this paper, we analyse some machine learning techniques proposed in recent years. In this study, several classifications were made to detect anomalous behaviour in network traffic. The models were built and evaluated based on the Canadian Institute for Cybersecurity-intrusion detection systems dataset released in 2017 (CIC-IDS-2017), which includes both current and historical attacks. The experiments were conducted using decision tree, random forest, logistic regression, gaussian naïve bayes, adaptive boosting, and their ensemble approach. The models were evaluated using various evaluation metrics such as accuracy, precision, recall, F1-score, false positive rate, receiver operating characteristic curve, and calibration curve.</p>

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Section: Resultsmentioning

confidence: 99%

Machine learning-based intrusion detection system for detecting web attacks

Abdou Vadhil,

Lemine Salihi,

Farouk Nanne

2024

IJ-AI

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“…To recognize a fraudulent user behavior, more intelligence is needed since traditional security measures fall short of providing the requisite protection and privacy [57]. To identify real users from false ones, many supervised machine learning models were consequently suggested [58], [59]. Therefore, a proactive security approach is used to identify and mitigate these problems.…”

Section: Figure 2 Overview Of Grey Hole Attack [19]mentioning

confidence: 99%

Malicious attacks modelling: a prevention approach for ad hoc network security

Talib

Alothman

Mohammed

2023

IJEECS

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As a result of the expansions that have taken place in the field of networking and the increase in the number of users of networks, there have recently been breakthroughs made in the techniques and methods used for network security. In this paper, a virtual private network (VPN) is proposed as a means of providing the necessary level of security for particular connections that span across vast networks. After the network performance metrics such as time delay and throughput have been accomplished, the suggested VPN is recommended for the purpose of assuring network security. In addition, artificial intelligence attack predictors and virtual private networks have been implemented with the purpose of preventing harmful activity within such connections. Using a wide variety of machine learning methods like Random Forests and Nave Bays, malicious assaults of any kind can be identified and thwarted in their tracks. Another technique for anticipating attacks is the use of an artificial neural network, which is a type of system that engages in deep learning and learns the behaviors of attacks while it is being trained so that it can then predict attacks. The results of this study demonstrate that the use of machine learning and artificial intelligence techniques can significantly improve the security and performance of virtual private networks and can effectively identify and prevent malicious attacks on networks.

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Preemptive modelling towards classifying vulnerability of DDoS attack in SDN environment

Cited by 2 publications

References 30 publications

Machine learning-based intrusion detection system for detecting web attacks

Machine learning-based intrusion detection system for detecting web attacks

Malicious attacks modelling: a prevention approach for ad hoc network security

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