An Adaptive Security Data Collection and Composition Recognition method for security measurement over LTE/LTE-A networks

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An Adaptive Security Data Collection and Composition Recognition method for security measurement over LTE/LTE-A networks. / Fu, Yulong; Chen, Hanlu; Zheng, Qinghua; Yan, Zheng; Kantola, Raimo; Jing, Xuyang; Cao, Jin; Li, Hui.

In: Journal of Network and Computer Applications, Vol. 155, 102549, 01.04.2020.

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@article{908f4ddf6f044650ba67de0e3e3f5c58,
title = "An Adaptive Security Data Collection and Composition Recognition method for security measurement over LTE/LTE-A networks",
abstract = "With the development of wireless communications, Mobile Networks have become an important part of our daily life and fueled the growth of many attractive technologies such as 5G, Internet of Things (IoT) and even Smart City. As a main bearer of current Mobile Networks, LTE/LTE-A carries massive and important business data but is facing more and more serious attack situations, which makes the Security Measurement over it become necessary and important. However, current methods are usually designed from specific malicious detections, which cannot provide the user with a synthetic view of security evaluation. Meanwhile, as the massive amount and poor quality of networking data are considered, the efficiency and accuracy of the current security measurement methods are usually not good. In this paper, we focus on the evaluation basis (the collecting data) of security measurement over LTE/LTE-A networks, and propose an Adaptive Security Data Collection and Composition Recognition (ASDCCR) method for it. We design heuristic algorithms and processing framework in ASDCCR to make the data collection adaptive and synthetic attack recognition become possible. We also verified the proposed method in simulated LTE environment of NS3 to verify the usability and accuracy of the proposed methods.",
keywords = "Data collection algorithm, Data composition algorithm, Data processing, LTE network security, NS3 simulation, Security measurement",
author = "Yulong Fu and Hanlu Chen and Qinghua Zheng and Zheng Yan and Raimo Kantola and Xuyang Jing and Jin Cao and Hui Li",
year = "2020",
month = "4",
day = "1",
doi = "10.1016/j.jnca.2020.102549",
language = "English",
volume = "155",
journal = "Journal of Network and Computer Applications",
issn = "1084-8045",
publisher = "Academic Press Inc.",

}

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TY - JOUR

T1 - An Adaptive Security Data Collection and Composition Recognition method for security measurement over LTE/LTE-A networks

AU - Fu, Yulong

AU - Chen, Hanlu

AU - Zheng, Qinghua

AU - Yan, Zheng

AU - Kantola, Raimo

AU - Jing, Xuyang

AU - Cao, Jin

AU - Li, Hui

PY - 2020/4/1

Y1 - 2020/4/1

N2 - With the development of wireless communications, Mobile Networks have become an important part of our daily life and fueled the growth of many attractive technologies such as 5G, Internet of Things (IoT) and even Smart City. As a main bearer of current Mobile Networks, LTE/LTE-A carries massive and important business data but is facing more and more serious attack situations, which makes the Security Measurement over it become necessary and important. However, current methods are usually designed from specific malicious detections, which cannot provide the user with a synthetic view of security evaluation. Meanwhile, as the massive amount and poor quality of networking data are considered, the efficiency and accuracy of the current security measurement methods are usually not good. In this paper, we focus on the evaluation basis (the collecting data) of security measurement over LTE/LTE-A networks, and propose an Adaptive Security Data Collection and Composition Recognition (ASDCCR) method for it. We design heuristic algorithms and processing framework in ASDCCR to make the data collection adaptive and synthetic attack recognition become possible. We also verified the proposed method in simulated LTE environment of NS3 to verify the usability and accuracy of the proposed methods.

AB - With the development of wireless communications, Mobile Networks have become an important part of our daily life and fueled the growth of many attractive technologies such as 5G, Internet of Things (IoT) and even Smart City. As a main bearer of current Mobile Networks, LTE/LTE-A carries massive and important business data but is facing more and more serious attack situations, which makes the Security Measurement over it become necessary and important. However, current methods are usually designed from specific malicious detections, which cannot provide the user with a synthetic view of security evaluation. Meanwhile, as the massive amount and poor quality of networking data are considered, the efficiency and accuracy of the current security measurement methods are usually not good. In this paper, we focus on the evaluation basis (the collecting data) of security measurement over LTE/LTE-A networks, and propose an Adaptive Security Data Collection and Composition Recognition (ASDCCR) method for it. We design heuristic algorithms and processing framework in ASDCCR to make the data collection adaptive and synthetic attack recognition become possible. We also verified the proposed method in simulated LTE environment of NS3 to verify the usability and accuracy of the proposed methods.

KW - Data collection algorithm

KW - Data composition algorithm

KW - Data processing

KW - LTE network security

KW - NS3 simulation

KW - Security measurement

UR - http://www.scopus.com/inward/record.url?scp=85079237087&partnerID=8YFLogxK

U2 - 10.1016/j.jnca.2020.102549

DO - 10.1016/j.jnca.2020.102549

M3 - Article

AN - SCOPUS:85079237087

VL - 155

JO - Journal of Network and Computer Applications

JF - Journal of Network and Computer Applications

SN - 1084-8045

M1 - 102549

ER -

ID: 41058562