{"title":"Machine learning-based detection of the man-in-the-middle attack in the physical layer of 5G networks","authors":"Abdullah Qasem , Ashraf Tahat","doi":"10.1016/j.simpat.2024.102998","DOIUrl":null,"url":null,"abstract":"<div><p>Fifth generation communication networks (5G) has received a great deal of attention from academia and industry alike, which will enable a wide variety of vertical applications by connecting heterogeneous devices and machines. Assessing availability and reliability in many circumstances and environments is critical. Researchers have recently focused on investigating and analyzing new multimedia networks with artificial intelligence (AI) technologies to achieve higher data rates and secure communication traffic between parties. User information privacy and security are of vital importance and of growing concerns that present evolving challenges to overcome in preventing attacks. Man-in-the-middle (MITM) attack is considered one of the most common attacks, where an attacker can impersonate one of the parties in a communication system to steal user data or forge the malicious data. Due to the limitation of using conventional cryptographic techniques for mobile networks and similar systems, new methods have been introduced to validate and authenticate transmitted signals dynamically, depending on the physical layer. In this paper, we present the distance-time directional delay (DTDD) model to detect the MITM attack in a variety of contexts and scenario. Indoor hotspots (InH) and urban micro-cellular (UMi) propagation environments were investigated to verify the reliability of the proposed approaches using realistic 5G millimeter-wave configurations and system setups. Simulations have been constructed based on the mmWave 5G channel simulator tool NYUSIM, in conjunction with a collection of machine learning algorithms (ML) including the extreme gradient boosting (XGBoost) and light gradient boosting machine (LGBM) as the core of the presented models and methodologies. Numerical simulations results produced a detection accuracy approaching 100% in the InH environment scenario, whereas for UMi environment scenario, a detection accuracy approaching 99% was attained.</p></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"136 ","pages":"Article 102998"},"PeriodicalIF":3.5000,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Simulation Modelling Practice and Theory","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1569190X24001126","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Fifth generation communication networks (5G) has received a great deal of attention from academia and industry alike, which will enable a wide variety of vertical applications by connecting heterogeneous devices and machines. Assessing availability and reliability in many circumstances and environments is critical. Researchers have recently focused on investigating and analyzing new multimedia networks with artificial intelligence (AI) technologies to achieve higher data rates and secure communication traffic between parties. User information privacy and security are of vital importance and of growing concerns that present evolving challenges to overcome in preventing attacks. Man-in-the-middle (MITM) attack is considered one of the most common attacks, where an attacker can impersonate one of the parties in a communication system to steal user data or forge the malicious data. Due to the limitation of using conventional cryptographic techniques for mobile networks and similar systems, new methods have been introduced to validate and authenticate transmitted signals dynamically, depending on the physical layer. In this paper, we present the distance-time directional delay (DTDD) model to detect the MITM attack in a variety of contexts and scenario. Indoor hotspots (InH) and urban micro-cellular (UMi) propagation environments were investigated to verify the reliability of the proposed approaches using realistic 5G millimeter-wave configurations and system setups. Simulations have been constructed based on the mmWave 5G channel simulator tool NYUSIM, in conjunction with a collection of machine learning algorithms (ML) including the extreme gradient boosting (XGBoost) and light gradient boosting machine (LGBM) as the core of the presented models and methodologies. Numerical simulations results produced a detection accuracy approaching 100% in the InH environment scenario, whereas for UMi environment scenario, a detection accuracy approaching 99% was attained.
期刊介绍:
The journal Simulation Modelling Practice and Theory provides a forum for original, high-quality papers dealing with any aspect of systems simulation and modelling.
The journal aims at being a reference and a powerful tool to all those professionally active and/or interested in the methods and applications of simulation. Submitted papers will be peer reviewed and must significantly contribute to modelling and simulation in general or use modelling and simulation in application areas.
Paper submission is solicited on:
• theoretical aspects of modelling and simulation including formal modelling, model-checking, random number generators, sensitivity analysis, variance reduction techniques, experimental design, meta-modelling, methods and algorithms for validation and verification, selection and comparison procedures etc.;
• methodology and application of modelling and simulation in any area, including computer systems, networks, real-time and embedded systems, mobile and intelligent agents, manufacturing and transportation systems, management, engineering, biomedical engineering, economics, ecology and environment, education, transaction handling, etc.;
• simulation languages and environments including those, specific to distributed computing, grid computing, high performance computers or computer networks, etc.;
• distributed and real-time simulation, simulation interoperability;
• tools for high performance computing simulation, including dedicated architectures and parallel computing.