Detecting Suspicious Behavior from Positional Information
نویسنده
چکیده
Suspiciousness is not the same an anomalousness. Suspicion requires evidence of deception in observed attempts at concealment. We propose metrics for measuring suspiciousness of agents moving in a sensor field based on only periodic knowledge of their positions (as with large numbers of "small and cheap" sensors). This has applications to electronic sentries and counterterrorism. This theory requires assessment of the behavior, visibility, and noticeability of the average agent as well as the anomalousness of the position, velocity, and acceleration vectors of a particular agent. We conclude with a report on experiments with an implementation of our theory on a simulated sensor network.
منابع مشابه
Detecting Suspicious Card Transactions in unlabeled data of bank Using Outlier Detection Techniqes
With the advancement of technology, the use of ATM and credit cards are increased. Cyber fraud and theft are the kinds of threat which result in using these Technologies. It is therefore inevitable to use fraud detection algorithms to prevent fraudulent use of bank cards. Credit card fraud can be thought of as a form of identity theft that consists of an unauthorized access to another person's ...
متن کاملTowards Detection of Suspicious Behavior from Multiple Observations
This paper addresses the problem of detecting suspicious behavior from a collection of individuals events, where no single event is enough to decide whether his/her behavior is suspicious, but the combination of multiple events enables reasoning. We establish a Bayesian framework for evaluating multiple events and show that the current approaches lack modeling behavior history included in the e...
متن کاملSuspicious Behavior Detection in Debit Card Transactions using Data Mining: A Comparative Study using Hybrid Models
The approach used in this paper is an implementation of a data mining process against real-life transactions of debit cards with the aim of detecting suspicious behavior. The framework designed for this purpose has been obtained through merging supervised and unsupervised models. First, due to unlabeled data, Twostep and Self-Organizing Map algorithms have been used in clustering the transactio...
متن کاملCan We Identify Manipulative Behavior and the Corresponding Suspects on Review Websites Using Supervised Learning?
Identification of manipulative behavior and the corresponding suspects is an essential task for maintaining robustness of reputation systems integrated by review websites. However, this task constitutes a great challenge. In this paper, we present an approach based on supervised learning to automatically detect suspicious behavior on travel websites. We distinguish between two types of manipula...
متن کاملWarningBird: Detecting Suspicious URLs in Twitter Stream
Twitter can suffer from malicious tweets containing suspicious URLs for spam, phishing, and malware distribution. Previous Twitter spam detection schemes have used account features such as the ratio of tweets containing URLs and the account creation date, or relation features in the Twitter graph. Malicious users, however, can easily fabricate account features. Moreover, extracting relation fea...
متن کامل