The Extensible Authentication Protocol (EAP) is a framework for transporting authentication credentials. EAP offers simpler interoperability and compatibility across authentication methods. EAP supports multiple authentication methods. In this paper, we have modelled the Extensible Authentication Protocol as a finite state machine. The various entities in our model are Authenticator, EAP Server, User and User Database. The messages exchanged between various entities are modelled as transitions. The model is represented in PROMELA. The model is checked for conformance with its specifications to detect possible flaws using SPIN model checker.

Feedback reputation systems are gaining popularity as dealing with unfair ratings in reputation systems has been recognized as an important but difficult task. This problem is challenging when the number of true user ratings is relatively small and unfair ratings plays majority in rated values. In this paper, we propose a new method to find malicious users in online reputation systems using Mean Bisector Analysis and Cosine Similarity (MBACS). Here the effort is mainly concentrated on abnormals in both rating-values domain and the malicious users domain. MBACS is very efficient to detect malicious user ratings and aggregate trustful ratings. The proposed reputation system is evaluated through simulations, MBACS system can significantly reduce the impact of unfair ratings.

ABSTRACT

Online reputation system is gaining popularity as it helps a user to be sure about the quality of a product/service he wants to buy. Nonetheless online reputation system is not immune from attack. Dealing with malicious ratings in reputation systems has been recognized as an important but difficult task. This problem is challenging when the number of true user's ratings is relatively small and unfair ratings plays majority in rated values. In this paper, we have proposed a new method to find malicious users in online reputation systems using Quality Repository Approach (QRA). We mainly concentrated on anomaly detection in both rating values and the malicious users. QRA is very efficient to detect malicious user ratings and aggregate true ratings. The proposed reputation system has been evaluated through simulations and it is concluded that the QRA based system significantly reduces the impact of unfair ratings and improve trust on reputation score with lower false positive as compared to other method used for the purpose.

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