Enhancing security of MME handover via fractional programming and Firefly algorithm

Vien, Quoc-Tuan ORCID logoORCID: https://orcid.org/0000-0001-5490-904X, Le, Tuan Anh ORCID logoORCID: https://orcid.org/0000-0003-0612-3717, Yang, Xin-She ORCID logoORCID: https://orcid.org/0000-0001-8231-5556 and Duong, Trung Q. (2019) Enhancing security of MME handover via fractional programming and Firefly algorithm. IEEE Transactions on Communications . ISSN 0090-6778 [Article] (Published online first) (doi:10.1109/TCOMM.2019.2920353)

PDF - Final accepted version (with author's formatting)
Download (3MB) | Preview


Key update and residence management have been investigated as an effective solution to cope with desynchronisation attacks in Mobility Management Entity (MME) handovers. In this paper, we first analyse the impacts of the Key Update Interval (KUI) and MME Residence Interval (MRI) on handover processes and their secrecy performance in terms of the Number of Exposed Packets (NEP), Signaling Overhead Rate (SOR) and Outage Probability of Vulnerability (OPV). Specifically, the bounds of the derived NEP and SOR not only capture their behaviours at the boundary of the KUI and MRI, but also show the trade-off between the NEP and SOR. Additionally, through the analysis of the OPV, it is shown that the handover security can be enhanced by shortening the KUI and the desynchonisation attacks can be avoided with high-mobility users. The above facts accordingly motivate us to propose a Multi- objective Optimisation (MO) problem to find the optimal KUI and MRI that minimise both the NEP and SOR subject to the constraint on the OPV. To this end, two scalarisation techniques are adopted to transform the proposed MO problem into single- objective optimisation problems, i.e., an achievement-function method via Fractional Programming (FP) and a weighted-sum method. Based on the derived bounds on NEP and SOR, the FP approach can be optimally solved via a simple numerical method. For the weighted-sum method, the Firefly Algorithm (FA) is utilised to find the optimal solution. The results show that both techniques can solve the proposed MO problem with a significantly reduced searching complexity compared to the conventional heuristic iterative search technique.

Item Type: Article
Research Areas: A. > School of Science and Technology > Computer and Communications Engineering
Item ID: 26661
Notes on copyright: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Useful Links:
Depositing User: Quoc-Tuan Vien
Date Deposited: 28 May 2019 09:19
Last Modified: 29 Nov 2022 19:05
URI: https://eprints.mdx.ac.uk/id/eprint/26661

Actions (login required)

View Item View Item


Activity Overview
6 month trend
6 month trend

Additional statistics are available via IRStats2.