CIDD: A Cloud Intrusion Detection Dataset for Cloud Computing and Masquerade Attacks

Kholidy, Hisham A.; Baiardi, Fabrizio

doi:10.1109/itng.2012.97

Cited by 34 publications

(22 citation statements)

References 12 publications

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“…25 Experiments are conducted using CIDD. 21,22 Our implementation consists of two phases labelled, training phase and testing phase. In the training phase, Cloud is loaded with the two-third of intrusion detection dataset (named as training dataset).…”

Section: Resultsmentioning

confidence: 99%

“…Current dataset becomes unsuitable here due to the diverse operating systems of the virtual machines, the different range of consumers' requirements, and the size of data in cloud systems. So, a Log Analyzer and Correlator System (LACS) 21,22 has been employed to the logs that are obtained from the DARPA Intrusion Detection Evaluation Group of MIT Lincoln Laboratory to generate CIDD. This CIDD consists of both knowledge-based and behavior-based audit data that are gathered from UNIX and Windows users.…”

Section: Fig 1 Fuzzy Neural Networkmentioning

confidence: 99%

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An Efficient Fuzzy Self-Classifying Clustering based Framework for Cloud Security

Raja¹,

Jaiganesh²,

Ramaiah³

2017

IJCIS

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Though cloud computing has become an attractive technology due to its openness and services, it brings several security hazards towards cloud storage. Since the distributed nature of clouds is achieved through internetworking technologies, clouds suffer from all the vulnerabilities by which networking also suffers. In essence, data stored in clouds are vulnerable to attacks from intruders. But, no single technique can provide efficient intrusion detection. In this paper, we propose fuzzy self-classifying clustering based cloud intrusion detection system which is intelligent to gain knowledge of fuzzy sets and fuzzy rules from data to detect intrusions in a cloud environment. Its efficiency is explained by comparing with other three cloud intrusion detection systems. Using a standard benchmark data from a CIDD (Cloud Intrusion Detection Dataset), experiments are conducted and tested. The results are presented in terms of success rate accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Fig 1 Fuzzy Neural Networkmentioning

confidence: 99%

An Efficient Fuzzy Self-Classifying Clustering based Framework for Cloud Security

Raja¹,

Jaiganesh²,

Ramaiah³

2017

IJCIS

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show abstract

“…Service Level Agreement improves the quality of service being delivered in the cloud. Masquerade attack is one where an attacker assumes the detect this we need to move the data to different environments to increase its usability and keep safe from attackers [14].…”

Section: Literature Surveymentioning

confidence: 99%

Comparison of Intrusion Detection Techniques in Cloud Computing

2018

IJRTER

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Abstract-Cloud computing is a kind of computing, which is highly scalable and use virtualized resources that can be shared by the users. Cloud computing provides scalable services to end users with greater flexibility and less infrastructure. Cloud services include software as a service, platform as a service, and infrastructure as a service. These services are provided by using different protocols and standards. Economic benefits are the main driver for the cloud, since it promises the reduction of capital expenditure and operational expenditure. One of the most significant search done on cloud computing is to provide security to the cloud environment. Cloud computing can be threatened by various cyber attacks, because most of the cloud computing systems provide services to many people who are not proven as trustworthy. In this paper, we are going to compare the techniques to detect the intrusion in cloud computing. The techniques used are Signature Based detection (SD), Anomaly-based Detection (AD) and Stateful Protocol Analysis.

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“…al., in [26] discussed cloud intrusion detection dataset and masquerade attacks on cloud computing. Masquerades attack is one of the most serious attacks on cloud system because it has huge amount of resources.…”

Section: Literature Reviewmentioning

confidence: 99%

Appraisal of the Most Prominent Attacks due to Vulnerabilities in Cloud Computing

Shah¹,

Shah²

2016

IJGDC

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CIDD: A Cloud Intrusion Detection Dataset for Cloud Computing and Masquerade Attacks

Cited by 34 publications

References 12 publications

An Efficient Fuzzy Self-Classifying Clustering based Framework for Cloud Security

An Efficient Fuzzy Self-Classifying Clustering based Framework for Cloud Security

Comparison of Intrusion Detection Techniques in Cloud Computing

Appraisal of the Most Prominent Attacks due to Vulnerabilities in Cloud Computing

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