A novel framework for intrusion detection in cloud

Modi, Chirag; Patel, Dhiren; Borisanya, Bhavesh; Patel, Amit B.; Rajarajan, Muttukrishnan

doi:10.1145/2388576.2388585

Cited by 43 publications

(44 citation statements)

References 7 publications

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“…Figures 2 to 7 represent the results of HIDCC approach as a comparison with the results of Modi et al 16 Table 4. Figures 2 to 7 represent the results of HIDCC approach as a comparison with the results of Modi et al 16 Table 4.…”

Section: Results Evaluationmentioning

confidence: 99%

“…Figure 5 shows the quite superiority of method HIDCC in terms of accuracy over other methods, and it has higher efficiency (up to %15) over other methods. compared with the method of Modi et al 16 Table 5 represents the numerical values of these diagrams. This figure represents the superiority of method HIDCC in terms of F-Score over other methods, where the value of this criterion in method HIDCC is 0.01 to 0.14 higher than other methods.…”

Section: Results Evaluationmentioning

confidence: 99%

“…In host-based IDS, an intelligent agent is installed over the supervised host. 16 The front-end is related to both external and internal network. 15 Network-based IDS monitor network traffic and the activities of networks contracts and applications to identify suspicious and hostile activities such as service denial attacks, port scanning, or even detecting attempts to break lock and intrude into computers.…”

Section: Intrusion Detection Systems Taxonomy Based On Their Locationmentioning

confidence: 99%

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HIDCC: A hybrid intrusion detection approach in cloud computing

Hatef

Shaker

Jabbarpour

et al. 2017

Concurrency and Computation

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The rapid growth of distributed computing systems that heavily communicate and interact with each other has raised the importance of confrontation against cyber intruders, attackers, and subversives. With respect to the emergence of cloud computing and its deployment all over the world, and because of its distributed and decentralized nature, a special security requirement is needed to protect this paradigm. Intrusion detection systems could differentiate usual and unusual behaviors by means of supervising, verifying, and controlling the configurations, log files, network traffic, user activities, and even the actions of different processes by which they could add new security dimensions to the cloud computing systems. The position of the intrusion detection mechanisms in cloud computing systems as well as the applied algorithms in those mechanisms are the 2 main factors in which many researches have focused on. The goal of those researches is to uncover intrusions as much as possible and to increase the rate and accuracy of detections while reducing the false warnings. Those solutions, however, mainly have high computational loads, low accuracy, and high implementation costs. In this paper, we present a comprehensive and accurate solution to detect and prevent intrusions in cloud computing systems by using a hybrid method, called HIDCC. The implementation results of the proposed method show that the intrusion coverage, intrusion detection accuracy, reliability, and availability in cloud computing systems are considerably increased, and false warnings are significantly reduced. KEYWORDS cloud computing, intrusion detection systems, signature-based detection, Snort, unusual behavior based detection, warning management

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Section: Results Evaluationmentioning

confidence: 99%

Section: Results Evaluationmentioning

confidence: 99%

Section: Intrusion Detection Systems Taxonomy Based On Their Locationmentioning

confidence: 99%

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HIDCC: A hybrid intrusion detection approach in cloud computing

Hatef

Shaker

Jabbarpour

et al. 2017

Concurrency and Computation

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“…Here, Snort is able to detect known attacks and derivative known attacks. In [13,14], Bayesian and Decision tree classifiers are investigated for anomaly detection. However, they failed to detect distributed attack.…”

Section: Literature Surveymentioning

confidence: 99%

Network Intrusion Detection in Cloud Computing

Modi

2015

Emerging Research in Computing, Information, Communication and Applications

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“…al [3] proposed a Framework to integrate NIDS in Cloud Infrastructure. Snort and Decision Tree (DT) classifier are used to implement the proposed framework.…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimistic Approach for Intrusion Security in Cloud

Seth¹,

Chandra²

2014

IJCA

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Cloud computing is internet based technology that provides online resources on pay per use basis. This computing technology is dependent on virtualization. By registering with cloud providers customers can use services (soft and hard) on pay per use basis. Customers are getting services in the form of virtual machines, they interact with these virtual machines to do their jobs. In recent survey it is found that security is the top most concern associated with cloud services. Customers are always afraid of adopting clouds services as their data are stored out of their network boundary and administration. Researchers in cloud are trying to develop security models to protect cloud services from intrusions including internal and external. In this paper we have proposed an intrusion security model that uses the optimum cloud resources and increases the resource and service availability.

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A novel framework for intrusion detection in cloud

Cited by 43 publications

References 7 publications

HIDCC: A hybrid intrusion detection approach in cloud computing

HIDCC: A hybrid intrusion detection approach in cloud computing

Network Intrusion Detection in Cloud Computing

An Optimistic Approach for Intrusion Security in Cloud

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