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Cloud computing offers scalable on-demand services to consumers with greater flexibility and lesser infrastructure investment. Since Cloud services are delivered using classical network protocols and formats over the Internet, implicit vulnerabilities existing in these protocols as well as threats introduced by newer architectures raise many security and(More)
Cloud computing is used extensively to deliver utility computing over the Internet. Defending network accessible Cloud resources and services from various threats and attacks is of great concern. Intrusion Detection System (IDS) has become popular as an important network security technology to detect cyber-attacks. In this paper, we propose a novel(More)
In this paper, we survey different intrusions affecting availability, confidentiality and integrity of Cloud resources and services. Proposals incorporating Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS) in Cloud are examined. We recommend IDS/IPS positioning in Cloud environment to achieve desired security in the next generation(More)
One of the major security challenges in cloud computing is the detection and prevention of denial-of-service (DoS) attacks. In order to detect and prevent DoS attacks as well as other malicious activities at the network layer, we propose a framework which integrates a network intrusion detection system (NIDS) in the Cloud infrastructure. We use snort and(More)
This paper presents results from a case study in predictive maintenance at a distribution warehouse. A simulation model was built with ARENA 5.0 for integrating predic-tive maintenance strategies with production planning strategies, for a conveyor system. Equipment health was monitored using condition-based parameters such as temperature and vibration for(More)
BACKGROUND Children born with congenital anomalies present a very high rate of perinatal death and neonatal mortality. Cytogenetic analysis is a convincing investigation along with clinical suspicion and biochemical screening tests. The current study was designed to characterize the prevalence and types of chromosomal abnormalities in high risk prenatal(More)
Privacy preserving data mining (PPDM) is a novel research area to preserve privacy for sensitive knowledge from disclosure. Many of the researchers in this area have recently made effort to preserve privacy for sensitive knowledge in statistical database. In this paper, we present a detailed overview and classification of approaches which have been applied(More)