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S. V. Narwane

Bio: S. V. Narwane is an academic researcher. The author has contributed to research in topics: Machine learning & Anomaly-based intrusion detection system. The author has an hindex of 1, co-authored 1 publications receiving 41 citations.

Papers
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Proceedings Article
08 Apr 2012
TL;DR: This paper proposed a system is to detect intrusions in the cloud computing using Behavior-based approach and knowledge- based approach, and definitely there will have very low false positive alarm.
Abstract: The Cloud computing system can be easily threatened by various attacks, because most of the cloud computing systems provide service to so many people who are not proven to be trustworthy. Due to their distributed nature, cloud computing environment are easy targets for intruders[1]. There are various Intrusion Detection Systems having various specifications to each. Cloud computing have two approaches i.e. Knowledge-based IDS and Behavior-Based IDS to detect intrusions in cloud computing. Behavior-Based IDS assumes that an intrusion can be detected by observing a deviation from normal to expected behavior of the system or user[2]s. Knowledgebased IDS techniques apply knowledge accumulated about specific attack . Knowledge-based IDS can’t detect unknown attacks, but it uses rules and monitors a stream of event s to find malicious characteristics and set the new rules for unknown attacks. In this paper we proposed a system is to detect intrusions in the cloud computing using Behavior-based approach and knowledge-based approach. If first approach unable to detect the data, second approach again verifies the data and compare it with the signatures within the database. In the proposed system definitely we will have very low false positive alarm.

47 citations

Journal ArticleDOI
TL;DR: In this article , the authors proposed a systematic approach of Closest Distance Ranking and Principal Component Analysis to deal with the unbalanced dataset, which significantly increased the performance of the machine learning-based system.
Abstract: Healthcare is a sensitive sector, and addressing the class imbalance in the healthcare domain is a time-consuming task for machine learning-based systems due to the vast amount of data. This study looks into the impact of socioeconomic disparities on the healthcare data of diabetic patients to make accurate disease predictions.This study proposed a systematic approach of Closest Distance Ranking and Principal Component Analysis to deal with the unbalanced dataset. A typical machine learning technique was used to analyze the proposed approach. The data set of pregnant diabetic women is analysed for accurate detection.The results of the case are analysed using sensitivity, which demonstrates that the minority class's lack of information makes it impossible to forecast the results. On the other hand, the unbalanced dataset was treated using the proposed technique and evaluated with the machine learning algorithm which significantly increased the performance of the system.The performance of the machine learning-based system was significantly enhanced by the unbalanced dataset which was processed with the proposed technique and evaluated with the machine learning algorithm. For the first time, an unbalanced dataset was treated with a combination of Closest Distance Ranking and Principal Component Analysis.

1 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper surveys the works on cloud security issues, making a comprehensive review of the literature on the subject and proposes a taxonomy for their classification, addressing several key topics, namely vulnerabilities, threats, and attacks.
Abstract: In the last few years, the appealing features of cloud computing have been fueling the integration of cloud environments in the industry, which has been consequently motivating the research on related technologies by both the industry and the academia. The possibility of paying-as-you-go mixed with an on-demand elastic operation is changing the enterprise computing model, shifting on-premises infrastructures to off-premises data centers, accessed over the Internet and managed by cloud hosting providers. Regardless of its advantages, the transition to this computing paradigm raises security concerns, which are the subject of several studies. Besides of the issues derived from Web technologies and the Internet, clouds introduce new issues that should be cleared out first in order to further allow the number of cloud deployments to increase. This paper surveys the works on cloud security issues, making a comprehensive review of the literature on the subject. It addresses several key topics, namely vulnerabilities, threats, and attacks, proposing a taxonomy for their classification. It also contains a thorough review of the main concepts concerning the security state of cloud environments and discusses several open research topics.

423 citations

Journal ArticleDOI
TL;DR: This paper proposes a quantitative solution for analyzing alerts generated by the Intrusion Detection Systems, using the Dempster-Shafer theory (DST) operations in 3-valued logic and the fault-tree analysis (FTA) for the mentioned flooding attacks.
Abstract: This paper is focused on detecting and analyzing the Distributed Denial of Service (DDoS) attacks in cloud computing environments. This type of attacks is often the source of cloud services disruptions. Our solution is to combine the evidences obtained from Intrusion Detection Systems (IDSs) deployed in the virtual machines (VMs) of the cloud systems with a data fusion methodology in the front-end. Specifically, when the attacks appear, the VM-based IDS will yield alerts, which will be stored into the Mysql database placed within the Cloud Fusion Unit (CFU) of the front-end server. We propose a quantitative solution for analyzing alerts generated by the IDSs, using the Dempster-Shafer theory (DST) operations in 3-valued logic and the fault-tree analysis (FTA) for the mentioned flooding attacks. At the last step, our solution uses the Dempsters combination rule to fuse evidence from multiple independent sources.

95 citations

Journal ArticleDOI
TL;DR: Vulnerabilities in cloud computing are studied and a collaborative IDS framework is proposed to enhance the security and privacy of big data.
Abstract: Big data, often stored in cloud networks, is changing our business models and applications. Rich information residing in big data is driving business decision making to be a data-driven process. The security and privacy of this data, however, have always been a concern of the data owners. Securing cloud computing environments could strengthen data security and privacy. Doing so requires a comprehensive security solution, from attack prevention to attack detection. Intrusion detection systems (IDSs) are playing an increasingly important role in network security schemes. This article studies vulnerabilities in cloud computing and proposes a collaborative IDS framework to enhance the security and privacy of big data.

76 citations

Journal ArticleDOI
TL;DR: The proposals of cloud intrusion detection system (IDS) and intrusion detection and prevention system frameworks are examined and the cloud IDS requirements and research scope are recommended to achieve desired level of security at virtualization layer of cloud computing.
Abstract: Virtualization plays a vital role in the construction of cloud computing. However, various vulnerabilities are existing in current virtualization implementations, and thus there are various security challenges at virtualization layer. In this paper, we investigate different vulnerabilities and attacks at virtualization layer of cloud computing. We examine the proposals of cloud intrusion detection system (IDS) and intrusion detection and prevention system frameworks. We recommend the cloud IDS requirements and research scope to achieve desired level of security at virtualization layer of cloud computing.

68 citations

Proceedings ArticleDOI
01 Dec 2013
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.
Abstract: Today, Cloud Computing is the preferred choice of every IT organization since it provides flexible and pay-per-use based services to its users However, the security and privacy is a major hurdle in its success because of its open and distributed architecture that is vulnerable to intruders Intrusion Detection System (IDS) is the most commonly used mechanism to detect attacks on cloud This paper provides an overview of different intrusions in cloud Then, we analyze some existing cloud based intrusion detection systems (IDS) with respect to their type, positioning, detection time, detection technique, data source and attacks they can detect The analysis also provides limitations of each technique to evaluate whether they fulfill the security requirements of cloud computing environment or not We emphasize the deployment of IDS that uses multiple detection methods to cope with security challenges in cloud

59 citations