Mutual Authentication Technique with Four Biometric Entities Applying Fuzzy Neural Network in 5G Mobile Communications
By: Bhattacharjee, Pijush Kanti
.
Publisher: Haryana IOSR - International Organization of Scientific Research 2022Edition: Vol.12(2), Mar-Apr.Description: 44-52p.Subject(s): EXTC EngineeringOnline resources: Click here
In:
IOSR journal of VLSI and signal processing (IOSR-JVSP)Summary: 5G mobile communications system is offering very high speed data communications technology
having connectivity to all sorts of the networks like 2G, 3G, 4G, WiMAX, MANET, VANET and other adhoc
mobile networks. Authentication of a mobile subscriber (MS) or a sub-network and a main network are an
important issue to check and minimize security threats or attacks. An artificial intelligence based mutual
authentication system applying fuzzy neural network with four biometric entities is proposed. Voice frequency
matching of the salutation or the selective words used by a subscriber like Hello, Good Morning, etc. with
his/her stored voice frequency of that particular word is taken as first entity. Second entity is chosen as
matching the flipping or clapping sound frequency of the calling subscriber with his/her stored flipping or
clapping sound frequency. Then third entity is taken as face image matching of the calling subscriber. Fourth
entity is granted as probability of the salutation word from subscriber’s talking habit while initializing a call.
These four entities such as probability of particular range of frequencies for the salutation word, the flipping or
clapping sound frequency matching, the face image matching of the subscriber, using particular salutation or
greeting word at the time of starting a call are used with the most frequently, more frequently, and less
frequently by the calling subscriber like uncertainty in artificial intelligence. Now different relative grades are
assigned to the most frequently, more frequently, and less frequently used parameters. Fuzzy operations such as
intersection and union are computed taking three membership functions at a time out of four membership
functions to adopt fuzzy neural network. Thereafter, the optimum or the final fuzzy operations are computed
according to the assumed weightages. Lastly, the optimized fuzzy operations are defuzzified by the Composite
Maxima method and the results are tested according to the invented fuzzy neural rule. If the results are
satisfactory, the subscriber or sub-network and the network are mutually authenticated in 5G mobile network.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2022-2031 |
5G mobile communications system is offering very high speed data communications technology
having connectivity to all sorts of the networks like 2G, 3G, 4G, WiMAX, MANET, VANET and other adhoc
mobile networks. Authentication of a mobile subscriber (MS) or a sub-network and a main network are an
important issue to check and minimize security threats or attacks. An artificial intelligence based mutual
authentication system applying fuzzy neural network with four biometric entities is proposed. Voice frequency
matching of the salutation or the selective words used by a subscriber like Hello, Good Morning, etc. with
his/her stored voice frequency of that particular word is taken as first entity. Second entity is chosen as
matching the flipping or clapping sound frequency of the calling subscriber with his/her stored flipping or
clapping sound frequency. Then third entity is taken as face image matching of the calling subscriber. Fourth
entity is granted as probability of the salutation word from subscriber’s talking habit while initializing a call.
These four entities such as probability of particular range of frequencies for the salutation word, the flipping or
clapping sound frequency matching, the face image matching of the subscriber, using particular salutation or
greeting word at the time of starting a call are used with the most frequently, more frequently, and less
frequently by the calling subscriber like uncertainty in artificial intelligence. Now different relative grades are
assigned to the most frequently, more frequently, and less frequently used parameters. Fuzzy operations such as
intersection and union are computed taking three membership functions at a time out of four membership
functions to adopt fuzzy neural network. Thereafter, the optimum or the final fuzzy operations are computed
according to the assumed weightages. Lastly, the optimized fuzzy operations are defuzzified by the Composite
Maxima method and the results are tested according to the invented fuzzy neural rule. If the results are
satisfactory, the subscriber or sub-network and the network are mutually authenticated in 5G mobile network.
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