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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/12973

Title: Encrypted Traffic Analytic using Identity Based Encryption with Equality Test for Cloud Computing
Authors: Alornyo, Seth
Asante, Michael
Hu, Xiong
Mireku, Kingsford Kissi
Issue Date: Aug-2018
Publisher: Institute of Electrical and Electronics Engineers
Citation: Institute of Electrical and Electronics Engineers, pp. 1-4
Abstract: Abstract—In this paper, the concept of identity based encryption with equality test (IBEET) was used to propose a new scheme for malware detection and verifiability of encrypted data. The use of encrypted data by malware poses new challenges to network threat detection because adversaries are able to encrypt malicious data to the cloud which pose a threat to receivers of the encrypted data and the cloud service provider. The flow metadata which includes the number of inbound bytes, outbound bytes, inbound packet, outbound packets, source and destination ports and the total duration of the flow in seconds of an encrypted data is used to compute a trapdoor for detecting a malware of an encrypted data.The flow metadata computed is sent to a private agent known as malware analytic provider (MAP) to check whether the trapdoor generated from a 3-way handshake between a client and a server matches to a normal handshake scheme of a specific traffic. If there is a mismatch, the ciphertext is rejected otherwise, ciphertext sent to an outsourced cloud service provider for storage.We refer to our scheme as encrypted traffic analytic using identity based encryption with equality test (ETA-IBEET).Our approach was instantiated using Bilinear map and Bilinear Diffie-Hellman (BDH) assumption to define one-way chosen-ciphertext security against a chosen identity attack (OW-ID-CCA)
Description: This article is published in Institute of Electrical and Electronics Engineers and also available at doi: 10.1109/ICASTECH.2018.8507063
URI: 10.1109/ICASTECH.2018.8507063.
http://hdl.handle.net/123456789/12973
Appears in Collections:College of Science

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