Research And Implementation Of P2P Reputation Sybil Attack Prevention Based On Workload Proof

With the rapid development of the Internet,P2 P system with its fast and reliable performance has become an important part of the era of sharing economy that cannot be ignored.The Sybil attack against the P2 P reputation system causes serious damage to the system by registering a large number of nodes to interact with the target nodes disguised as entity nodes and manipulating the reputation of the target nodes by unfair evaluation.Therefore,how to effectively resist the Sybil attack has become an important problem to ensure the stable operation of P2 P systems.However,the existing research on Sybil attack prevention mainly focuses on detecting the nodes(Sybil nodes)controlled by Sybil attacker,and using deep learning or Bayesian network and other technologies to identify and isolate Sybil nodes so as to prevent them from harming the P2 P reputation system.However,detection method belongs to passive defense,which generally acts after the attacker infringes on the system,and it is difficult to produce substantial threat to the attacker before the attack takes effect.Another type of research advocates the use of identity authentication,binding user identity and personal information,and introducing trusted third parties to manage user information.But this kind of scheme is not conducive to the privacy protection and scalability of P2 P systems.In view of the above problems,this paper considers the motivation of the attackers from the perspective of the economy of the attacks.By referring to the idea of preventing Sybil attacks in the P2P blockchain and the idea of multi-round puzzle verification,the paper introduces the proof-of-work and proposes a multi-round verification mechanism to prevent Sybil attack.Firstly,the attacker's effectiveness function is defined,which is used to evaluate the effectiveness of the attacker's utility and prevention model.Secondly,the proof-of-work is used to construct multiple rounds of validation at fixed intervals,and the puzzle validation is applied to the nodes indiscriminately,thus increasing the cost of Sybil attack.In addition,a dynamic difficulty adjustment strategy was introduced.By considering the motivation of the attacker,the node reputation was dynamically correlated with the verification difficulty to further limit Sybil attack.Finally,whitewashing attacks in Sybil attacks are also included in the evaluation of the prevention effect of the method,and the comprehensive effectiveness evaluation function is designed to evaluate the overall prevention effect of the method.The rationality and effectiveness of the proposed prevention method are verified by theoretical analysis and simulation experiments.Theoretical analysis shows that with the increase of the scale of Sybil attack,this method can make the cost of attack increase exponentially,and fully restrain Sybil attack.At the same time,the experimental results also show that the undifferentiated verification applied to all nodes has little effect on normal nodes.In addition,this paper carries out theoretical analysis and experimental verification on the prevention effect of the prevention method against whitewashing attacks in Sybil attack,and the results show that the method is also effective in preventing whitewashing attacks.Finally,using the analysis and design ideas of software engineering,the prototype system of Sybil attack prevention is designed and implemented based on the prevention model.