2,150 research outputs found
Anomaly Detection in Streaming Sensor Data
In this chapter we consider a cell phone network as a set of automatically
deployed sensors that records movement and interaction patterns of the
population. We discuss methods for detecting anomalies in the streaming data
produced by the cell phone network. We motivate this discussion by describing
the Wireless Phone Based Emergency Response (WIPER) system, a proof-of-concept
decision support system for emergency response managers. We also discuss some
of the scientific work enabled by this type of sensor data and the related
privacy issues. We describe scientific studies that use the cell phone data set
and steps we have taken to ensure the security of the data. We describe the
overall decision support system and discuss three methods of anomaly detection
that we have applied to the data.Comment: 35 pages. Book chapter to appear in "Intelligent Techniques for
Warehousing and Mining Sensor Network Data" (IGI Global), edited by A.
Cuzzocre
Security in Data Mining- A Comprehensive Survey
Data mining techniques, while allowing the individuals to extract hidden knowledge on one hand, introduce a number of privacy threats on the other hand. In this paper, we study some of these issues along with a detailed discussion on the applications of various data mining techniques for providing security. An efficient classification technique when used properly, would allow an user to differentiate between a phishing website and a normal website, to classify the users as normal users and criminals based on their activities on Social networks (Crime Profiling) and to prevent users from executing malicious codes by labelling them as malicious. The most important applications of Data mining is the detection of intrusions, where different Data mining techniques can be applied to effectively detect an intrusion and report in real time so that necessary actions are taken to thwart the attempts of the intruder. Privacy Preservation, Outlier Detection, Anomaly Detection and PhishingWebsite Classification are discussed in this paper
Data Mining in Internet of Things Systems: A Literature Review
The Internet of Things (IoT) and cloud technologies have been the main focus of recent research, allowing for the accumulation of a vast amount of data generated from this diverse environment. These data include without any doubt priceless knowledge if could correctly discovered and correlated in an efficient manner. Data mining algorithms can be applied to the Internet of Things (IoT) to extract hidden information from the massive amounts of data that are generated by IoT and are thought to have high business value. In this paper, the most important data mining approaches covering classification, clustering, association analysis, time series analysis, and outlier analysis from the knowledge will be covered. Additionally, a survey of recent work in in this direction is included. Another significant challenges in the field are collecting, storing, and managing the large number of devices along with their associated features. In this paper, a deep look on the data mining for the IoT platforms will be given concentrating on real applications found in the literatur
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