The increased requirement of data science in recent times has given rise to the concept of data security, which has become a major issue; thus, the amalgamation of data science methodology with intrusion detection systems as a field of research has acquired a lot of prominence. The level of access to the information system and its visibility to user pursuit was required to operate securely. Intrusion detection has been gaining popularity in the area of data science to incorporate the overall information security infrastructure, where regular operations depend upon shared use of information. The problems are to build an intrusion detection system efficient enough for detecting attacks and to reduce the false positives with a high detection rate. In this paper, the authors analyse various techniques of intrusion detection combined with data science, which will help in understanding the best fit technique under different circumstances.
The movement towards digital era introduces centralization of information, web services, applications, and devices. The fraudster keeps an eye over ongoing transaction and forges data by using different techniques as traffic monitoring, session hijacking, phishing, and network bottleneck. In this study, the authors design a framework using deep learning algorithm to suspect the fraudulence transaction and evaluate the performance of the proposed system by updating data repositories. The neural network-based sequence classification technique is used for fraud detection of credit card transactions by including threshold value to measure the deviation of transaction. The reconstruction error (MSE) and predefined threshold value of 4.9 is used for determination of fraudulent transactions.
Big data is the high-volume, high-variety data which involves data storage, data management, and data analysis that presents a wide view of business possibility for real-time data, sensor data, and streaming data over the web. Big data relies on technology, analysis, and mythology where technology deals with computation power, accuracy, linking, and large datasets; analysis is to find patterns by analyzing large datasets to discover hidden information; and mythology is the wrong beliefs that large datasets give insight knowledge of data that is not obtained by small datasets. In this paper, the authors analyzed the major benefits the organization see from employing contract workers using map reduce programming framework.
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