Currently, Distributed Denial of Service Attacks are the most dangerous cyber danger. By inhibiting the server's ability to provide resources to genuine customers, the affected server's resources, such as bandwidth and buffer size, are slowed down. A mathematical model for distributed denial-of-service attacks is proposed in this study. Machine learning algorithms such as Logistic Regression and Naive Bayes, are used to detect attacks and normal scenarios. The CAIDA 2007 Dataset is used for experimental study. The machine learning algorithms are trained and tested using this dataset and the trained algorithms are validated. Weka data mining platform are used in this study for implementation and results of the same are analysed and compared. Other machine learning algorithms used with respect to denial of service attacks are compared with the existing work.
Data quality (DQ) is gaining traction as a new area to focus on for increasing organisational effectiveness. Despite the fact that the implications of poor data quality are often felt in the day-to-day operations of businesses, only a small percentage of companies use particular approaches for measuring and monitoring data quality. In this paper, the focus is on the efficiency and incompleteness of IOT big data and since security is the major concern in large clusters, map reduce technique is proposed in order to overcome the issues and challenges faced on regular basis while dealing with huge volume of information. Dealing with veracity is need of an hour and therefore, the work in this paper can be categorised into analysis, observation, proposing model and testing its accuracy and performance.
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