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Arul, V.
- Detection and Localization of Multiple Spoofing Attackers and Revoking them in Wireless Networks
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Authors
Affiliations
1 Department of Computer Science & Engineering, K. S. Rangasamy College of Technology, Tamilnadu, IN
1 Department of Computer Science & Engineering, K. S. Rangasamy College of Technology, Tamilnadu, IN
Source
Wireless Communication, Vol 6, No 1 (2014), Pagination: 1-6Abstract
Wireless spoofing attacks are easy to launch and can significantly impact the performance of networks. Although the identity of a node can be verified through cryptographic authentication, conventional security approaches are not always desirable because of their overhead requirements. The project is proposed to use spatial information, a physical property associated with each node, hard to falsify, and not reliant on cryptography, as the basis for 1) detecting spoofing attacks; 2) determining the number of attackers when multiple adversaries masquerading as the same node identity; and 3) localizing multiple adversaries. It is proposed to use the spatial correlation of received signal strength (RSS) inherited from wireless nodes to detect the spoofing attacks. It formulates the problem of determining the number of attackers as a multi-class detection problem. Cluster-based mechanisms are developed to determine the number of attackers. When the training data are available, the project explores using the Support Vector Machines (SVM) method to further improve the accuracy of determining the number of attackers. The localization results use a representative set of algorithms that provide strong evidence of high accuracy of localizing multiple adversaries. In addition, a fast and effective mobile replica node detection scheme is proposed using the Sequential Probability Ratio Test. evaluated our techniques through two testbedsusing both an 802.11 (WiFi) network and an 802.15.4 (ZigBee) network in two real officebuildings.Keywords
Wireless Network Security, Spoofing Attack, Attack Detection, Localization.- Privacy Preservation of Micro Data Publishing using Fragmentation
Abstract Views :181 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Anna University, Chennai, IN
2 Department of Computer Science and Engineering, SRM Institute of Science and Technology, IN
3 Department of Computing Science and Engineering, Galgotias University, IN
1 Department of Computer Science and Engineering, Anna University, Chennai, IN
2 Department of Computer Science and Engineering, SRM Institute of Science and Technology, IN
3 Department of Computing Science and Engineering, Galgotias University, IN
Source
ICTACT Journal on Soft Computing, Vol 9, No 3 (2019), Pagination: 1945-1949Abstract
Organization such as hospitals, publish detailed data or micro data about individuals for research or statistical purposes. Many applications that employ data mining techniques involve mining data that include private and sensitive information about the subjects. When releasing the micro data, it is necessary to prevent the sensitive information of the individuals from being disclosed. Several existing privacy-preserving approaches focus on anonymization techniques such as generalization and bucketization. Recent work has shown that generalization loses considerable amount of information for high dimensional data, the bucketization does not prevent membership disclosure and does not make clear separation between quasi-identifying attributes and sensitive attributes. In this work a novel technique called Fragmentation is proposed for publishing sensitive data with preventing the sensitive information of the individual. Here first the vertical Fragmentation is applied to attributes. In vertical Fragmentation, attributes are segmented into columns. Each column contains a subset of attributes. Secondly, the horizontal Fragmentation is applied to tuples. In this, tuples are segmented into buckets. Each bucket contains a subset of tuples. Finally the real dataset is used for experiments and the results show that this Fragmentation technique preserves better utility while protecting privacy threats and prevents the membership disclosure.Keywords
Privacy, Privacy Preservation, Data Anonymization, Data Publishing, Data Security.References
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