DocumentCode :
3585211
Title :
Advance Artificial Intelligence Based Mutual Authentication Technique with Four Entities in 4-G Mobile Communications
Author :
Bhattacharjee, Pijush Kanti ; Roy, Sudipta ; Pal, Rajat Kumar
Author_Institution :
Dept. of Inf. Technol., Assam Univ., Silchar, India
fYear :
2014
Firstpage :
139
Lastpage :
145
Abstract :
4-G mobile communications system is offering high speed data communications technology having connectivity to all sorts of the networks including 2-G and 3-G mobile networks. Authentication of a mobile subscriber (MS) or a subnetwork and a main network are an important issue to check and minimize security threats or attacks. An advanced artificial intelligence based mutual authentication system applying fuzzy neural network with four entities is proposed. Voice frequency of the salutation or the selective words used by a subscriber like Hello, Good Morning, etc. is taken as first entity. Second entity is chosen as thumb fingerprint matching of the calling subscriber with his/her stored thumb fingerprint. 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 thumb fingerprint 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 the sub-network and the network (the switch or the server) are mutually authenticated in- 4-G mobile communications.
Keywords :
3G mobile communication; 4G mobile communication; artificial intelligence; face recognition; fingerprint identification; fuzzy neural nets; fuzzy set theory; image matching; message authentication; telecommunication computing; telecommunication security; 2G mobile network; 3G mobile network; 4G mobile communication; artificial intelligence; composite maxima method; face image matching; fuzzy neural network; high speed data communications technology; membership function; mutual authentication technique; security threat; thumb fingerprint matching; voice frequency; Face; Fingerprint recognition; Mobile communication; Mobile computing; Servers; Switches; Thumb; Biometric scheme; Face image matching; Fuzzy neural network; Fuzzy operation; Identifier; Mutual authentication; Packet switching; Salutation word; Thumb fingerprint matching;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Soft Computing and Machine Intelligence (ISCMI), 2014 International Conference on
Type :
conf
DOI :
10.1109/ISCMI.2014.11
Filename :
7079371
Link To Document :
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