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2020-08-13
Fan, Chun-I, Tseng, Yi-Fan, Cheng, Chen-Hsi, Kuo, Hsin-Nan, Huang, Jheng-Jia, Shih, Yu-Tse.  2019.  Anonymous Authentication and Key Agreement Protocol for LTE Networks. 2019 2nd International Conference on Communication Engineering and Technology (ICCET). :68—71.
In 2008, 3GPP proposed the Long Term Evolution (LTE) in version 8. The standard is used in high-speed wireless communication standard for mobile terminal in telecommunication. It supports subscribers to access internet via specific base station after authentication. These authentication processes were defined in standard TS33.401 and TS33.102 by 3GPP. Authenticated processing standard inherits the authentication and key agreement protocol in RFC3310 and has been changed into authenticated scheme suitable for LTE. In the origin LTE authenticated scheme, subscribers need to transfer its International Mobile Subscriber Identity (IMSI) with plaintext. The IMSI might be intercepted and traced by fake stations. In this work, we propose a new scheme with a pseudo IMSI so that fake stations cannot get the real IMSI and trace the subscriber. The subscriber can keep anonymous and be confirmed by the base station for the legality. The pseudo identity is unlinkable to the subscriber. Not only does the proposed scheme enhance the security but also it just has some extra costs for signature generation and verification as compared to the original scheme.
2020-07-20
Tanksale, Vinayak.  2019.  Intrusion Detection For Controller Area Network Using Support Vector Machines. 2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems Workshops (MASSW). :121–126.
Controller Area Network is the most widely adopted communication standard in automobiles. The CAN protocol is robust and is designed to minimize overhead. The light-weight nature of this protocol implies that it can't efficiently process secure communication. With the exponential increase in automobile communications, there is an urgent need for efficient and effective security countermeasures. We propose a support vector machine based intrusion detection system that is able to detect anomalous behavior with high accuracy. We outline a process for parameter selection and feature vector selection. We identify strengths and weaknesses of our system and propose to extend our work for time-series based data.