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Intrusion detection system for cloud computing

TLDR
This work proposes a new multi-threaded distributed cloud IDS model that handles large flow of data packets, analyze them and generate reports efficiently by integrating knowledge and behavior analysis to detect intrusions.
Abstract
Providing security in a distributed system requires more than user authentication with passwords or digital certificates and confidentiality in data transmission. Distributed model of cloud makes it vulnerable and prone to sophisticated distributed intrusion attacks like Distributed Denial of Service (DDOS) and Cross Site Scripting (XSS). To handle large scale network access traffic and administrative control of data and application in cloud, a new multi-threaded distributed cloud IDS model has been proposed. Our proposed cloud IDS handles large flow of data packets, analyze them and generate reports efficiently by integrating knowledge and behavior analysis to detect intrusions. ————————————————————

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Citations
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Anomaly Detection System in Cloud Environment Using Fuzzy Clustering Based ANN

TL;DR: This work proposes an anomaly detection system at the hypervisor layer named Hypervisor Detector that uses a hybrid algorithm which is a mixture of Fuzzy C-Means clustering algorithm and Artificial Neural Network to improve the accuracy of the detection system.
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A New Threat Intelligence Scheme for Safeguarding Industry 4.0 Systems

TL;DR: The proposed threat intelligence technique is designed based on beta mixture-hidden Markov models (MHMMs) for discovering anomalous activities against both physical and network systems, and is evaluated on two well-known datasets.
Journal ArticleDOI

Computational intelligence intrusion detection techniques in mobile cloud computing environments: Review, taxonomy, and open research issues

TL;DR: This paper aims to present a comprehensive survey of intrusion detection systems that use computational intelligence (CI) methods in a (mobile) cloud environment and defines a taxonomy for IDS and classify CI-based techniques into single and hybrid methods.
Journal ArticleDOI

Outlier Dirichlet Mixture Mechanism: Adversarial Statistical Learning for Anomaly Detection in the Fog

TL;DR: An adversarial statistical learning mechanism for anomaly detection, outlier Dirichlet mixture-based ADS (ODM-ADS), which has three new capabilities, which can self-adapt against data poisoning attacks that inject malicious instances in the training phase for disrupting the learning process.
Proceedings ArticleDOI

Intrusion Detection System in Cloud Computing: Challenges and opportunities

TL;DR: This paper provides an overview of different intrusions in cloud and analyzes some existing cloud based intrusion detection systems with respect to their type, positioning, detection time, detection technique, data source and attacks they can detect.
References
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Proceedings ArticleDOI

Controlling data in the cloud: outsourcing computation without outsourcing control

TL;DR: It is argued that with continued research advances in trusted computing and computation-supporting encryption, life in the cloud can be advantageous from a business intelligence standpoint over the isolated alternative that is more common today.
Journal ArticleDOI

Intrusion Detection for Grid and Cloud Computing

TL;DR: The Grid and Cloud Computing Intrusion Detection System integrates knowledge and behavior analysis to detect intrusions.
Proceedings ArticleDOI

Intrusion Detection in the Cloud

TL;DR: Several requirements for deploying IDS in the Cloud are summarized and an extensible IDS architecture for being easily used in a distributed cloud infrastructure is proposed.
Proceedings ArticleDOI

A Cooperative Intrusion Detection System Framework for Cloud Computing Networks

TL;DR: The implementation results indicate that the proposed cooperative IDS system could resist DoS attack and only increases little computation effort compared with pure Snort based IDS but prevents the system from single point of failure attack.

Distributed Cloud Intrusion Detection Model

Irfan Gul, +1 more
TL;DR: This proposed cloud IDS handles large flow of data packets, analyze them and generate reports efficiently and instantly send for information of cloud user and expert advice for cloud service provider’s network misconfigurations through a third party IDS monitoring and advisory service.
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