Applying machine learning enabled myriad fragment empirical modes in 5G communications to detect profile injection attacks

被引:7
作者
Alzaidi, Mohammed S. S. [1 ]
Shukla, Piyush Kumar [2 ]
Sangeetha, V. [3 ]
Pandagre, Karuna Nidhi [4 ]
Minchula, Vinodh Kumar [5 ]
Sharma, Sachin [6 ]
Khan, Arfat Ahmad [7 ]
Prashanth, V. [8 ]
机构
[1] Taif Univ, Coll Engn, Dept Elect Engn, Taif 21944, Saudi Arabia
[2] Rajiv Gandhi Proudyogiki Vishwavidyalaya, Univ Inst Technol, Technol Univ Madhya Pradesh, Dept Comp Sci & Engn, Bhopal 462033, Madhya Pradesh, India
[3] Ramaiah Inst Technol, Dept Comp Sci & Engn, Bangalore, Karnataka, India
[4] Bansal Inst Sci & Technol, Dept MCA, Bhopal, Madhya Pradesh, India
[5] Chaitanya Bharathi Inst Technol A, Dept ECE, Hyderabad, Telangana, India
[6] Manav Rachna Int Inst Res & Studies, Faridabad, India
[7] Khon Kaen Univ, Coll Comp, Dept Comp Sci, Khon Kaen 40002, Thailand
[8] NITTE Meenakshi Inst Technol, Dept Elect & Elect, Bangalore, Karnataka, India
关键词
5G communication; Feature extraction; Improved support vector classification; Profile injection attack detection; INDUSTRIAL INTERNET; CLOUDLET PLACEMENT; EDGE; MIGRATION; ALGORITHM; STRATEGY;
D O I
10.1007/s11276-023-03301-z
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In order to facilitate communication, wireless networks are built from a collection of nodes that may be either static or dynamic. They are acquiring a lot of popularity in the area of research due to the fact that they are ad hoc in nature, and the number of users of mobile devices is rising day by day. Because of the ease with which these networks may be deployed in challenging and unsupervised rural places, the exchange of information has been a reality since the invention of these networks. Mobile ad hoc networks are simple to set up because of the properties that allow for self-organization and the fact that the medium is wireless. A lack of centralized fixed infrastructure, flexibility to frequent change in topologies, and other features like these are some of the other things that draw people's attention to wireless networks. Wireless networks are vulnerable to a wide range of assaults since their nodes are able to move around and their topologies are constantly changing. In addition, MANET operates in an environment that is both open and dynamic, which leaves it subject to a variety of threats from other types of network assaults. Routing protocols are almost always the target of one form or another of the same general category of attacks. Eavesdropping, causing damage, changing routing information, deleting routing information, manipulating information, advertising phoney routes, and misrouting information are all potential components of these assaults. The circumstances may make it difficult to maintain confidentiality in any communications. There are many different kinds of assaults, and each one may damage wireless networks on a different tier of the communication stack and bring the performance of the network down. Eavesdropping, jamming, traffic analysis and monitoring, denial of service attacks, grey hole attacks, black hole attacks, and wormhole assaults are a few examples of the many sorts of attacks that fall under this category. Ad-hoc networks are more susceptible to security breaches than traditional wired and wireless networks due to the usage of open wireless medium, dynamic topology, and dispersed and cooperative channel sharing. The wormhole attack on dispersed wireless networks is being described here by the person who conducted this study. Because this assault is so potent, it is very difficult to identify it before it has ever been launched. The invader may simply initiate it without having knowledge of the network or compromising any authorized nodes, which is a need for launching it. During a wormhole attack, a malicious node in one part of the network takes control of the packets and tunnels them to another hostile node in a different part of the network, which then repeats the packets locally. The thesis aims to do two things at the same time: (a) To simulate a variety of possible wormhole assaults on the MANET network (b) To investigate the functionality and efficiency of the proposed secure routing protocol within the context of these simulated attacks on the network.
引用
收藏
页码:5533 / 5546
页数:14
相关论文
共 22 条
[1]   Robust Model-Based Reliability Approach to Tackle Shilling Attacks in Collaborative Filtering Recommender Systems [J].
Alonso, Santiago ;
Bobadilla, Jesus ;
Ortega, Fernando ;
Moya, Ricardo .
IEEE ACCESS, 2019, 7 :41782-41798
[2]   5G-Telecommunication Allocation Network Using IoT Enabled Improved Machine Learning Technique [J].
Alzaidi, Mohammed S. ;
Subbalakshmi, Chatti ;
Roshini, T. V. ;
Shukla, Piyush Kumar ;
Shukla, Surendra Kumar ;
Dutta, Papiya ;
Alhassan, Musah .
WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2022, 2022
[3]  
[Anonymous], Thesis |
[4]   Shilling attack detection in binary data: a classification approach [J].
Batmaz, Zeynep ;
Yilmazel, Burcu ;
Kaleli, Cihan .
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2020, 11 (06) :2601-2611
[5]   BS-SC: An Unsupervised Approach for Detecting Shilling Profiles in Collaborative Recommender Systems [J].
Cai, Hongyun ;
Zhang, Fuzhi .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2021, 33 (04) :1375-1388
[6]   Detecting shilling attacks in recommender systems based on analysis of user rating behavior [J].
Cai, Hongyun ;
Zhang, Fuzhi .
KNOWLEDGE-BASED SYSTEMS, 2019, 177 :22-43
[7]   Trustworthy and profit: A new value-based neighbor selection method in recommender systems under shilling attacks [J].
Cai, Yuanfeng ;
Zhu, Dan .
DECISION SUPPORT SYSTEMS, 2019, 124
[8]   Dynamic clustering collaborative filtering recommendation algorithm based on double-layer network [J].
Chen, Jianrui ;
Wang, Bo ;
Ouyang, Zhiping ;
Wang, Zhihui .
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2021, 12 (04) :1097-1113
[9]   RETRACTED: An Improved Secure Key Generation Using Enhanced Identity-Based Encryption for Cloud Computing in Large-Scale 5G (Retracted Article) [J].
Gupta, Rajeev Kumar ;
Almuzaini, Khalid K. ;
Pateriya, R. K. ;
Shah, Kaushal ;
Shukla, Piyush Kumar ;
Akwafo, Reynah .
WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2022, 2022
[10]   An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment [J].
Lei, Lifeng ;
Kou, Liang ;
Zhan, Xianghao ;
Zhang, Jilin ;
Ren, Yongjian .
SENSORS, 2022, 22 (19)