Denial of service (DoS) attacks detection in MANETs using Bayesian classifiers

Rmayti, Mohammad; Begriche, Youcef; Khatoun, Rida; Khoukhi, Lyes; Gaïti, Dominique

doi:10.1109/scvt.2014.7046699

Cited by 16 publications

(10 citation statements)

References 7 publications

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“…We use these two items in a complementary manner to successfully become aware of the packet shedding attacks in MANETs. Considering reproduction comes about, our channels demonstrate that these attacks can be detected with a high rate of accuracy [10].…”

Section: Literature Surveymentioning

confidence: 84%

A Novel Approach to detect Denial Of Service Attack in MANET

Dhakar¹,

Sonker²

2017

IJRIA

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Section: Literature Surveymentioning

confidence: 84%

A Novel Approach to detect Denial Of Service Attack in MANET

Dhakar¹,

Sonker²

2017

IJRIA

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“…Thus, the credibility of each single node is key to ensuring accurate and reliable network service delivery. Current trust management schemes such as those proposed in [74,78] only provide verification of data consistency and validity, but cannot guarantee objects' authentication. Furthermore, these previously proposed schemes are not completely adaptable to the IoT context.…”

Section: Trust Managementmentioning

confidence: 99%

IoT technologiesfor smart cities

et al. 2018

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The large deployment of Internet of things (IoT) is actually enabling smart city projects and initiatives all over the world. Objects used in daily life are being equipped with electronic devices and protocol suites in order to make them interconnected and connected to the Internet. According to a recent Gartner study, 50 billion connected objects will be deployed in smart cities by 2020. These connected objects will make the authors' cities smart. However, they will also open up risks and privacy issues. As various smart city initiatives and projects have been launched in recent years, they have witnessed not only the expected benefits, but the risks introduced. They describe the current and future trends of smart city and IoT. They also discuss the interaction between smart cities and IoT and explain some of the drivers behind the evolution and development of IoT and smart city. Finally, they discuss some of the IoT weaknesses and how they can be addressed when used for smart cities.

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“…Hema & Shyni (2015) proposed a Bayesian classification model for Denial of Service attacks to improve the detection rate and reduce the occurrence of false positive alarms in the system. An approach based on two Bernoulli and Multinomial Bayesian models which efficiently detects packet dropping attacks in Mobile Ad hoc Networks was described in Rmaythi et al (2014). The research aimed at discovering secure paths between source and a destination by avoiding DoS attacks.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Analysis of Decision Tree and Bayesian Model for Network Intrusion Detection System

2019

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Denial of Service Attacks (DoS) is a major threat to computer networks. This paper presents two approaches (Decision tree and Bayesian network) to the building of classifiers for DoS attack. Important attributes selection increases the classification accuracy of intrusion detection systems; as decision tree which has the advantage of generating explainable rules was used for the selection of relevant attributes in this research. A C4.5 decision tree dimensional reduction algorithm was used in reducing the 41 attributes of the KDD´99 dataset to 29. Thereafter, a rule based classification system (decision tree) was built as well as Bayesian network classification system for denial of service attack (DoS) based on the selected attributes. The classifiers were evaluated and compared using performance on the test dataset. Experimental results show that Decision Tree is robust and gives the highest percentage of successful classification than Bayesian Network which was found to be sensitive to the discritization techniques. It has been successfully tested that significant attribute selection is important in designing a real world intrusion detection system (IDS). Keywords— Intrusion Detection System, Machine Learning, Decision Tree, and Bayesian Network.

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Denial of service (DoS) attacks detection in MANETs using Bayesian classifiers

Cited by 16 publications

References 7 publications

A Novel Approach to detect Denial Of Service Attack in MANET

A Novel Approach to detect Denial Of Service Attack in MANET

IoT technologiesfor smart cities

A Comparative Analysis of Decision Tree and Bayesian Model for Network Intrusion Detection System

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