Incentives are very important to be employed in any defensive mechanism against DDoS attack. Incentive is a major concept abandoned by most of the defensive mechanisms that have been proposed so far. It is a tool that can motivate users to send data wisely into the network. Therefore, in this paper, we have proposed a two layered multi-attribute auction mechanism for incentivising users by imposing payment schemes as well as by providing rewards. Apart from this, we have developed a reputation assessment procedure to identify malicious user by monitoring his credibility score calculated through his marginal utility. Identified malicious users are then mapped to different levels of suspiciousness. Identified legitimate users are forwarded towards first level of auction in which virtual users have been added by service provider to increase the competition among users. Critical values are computed for every user and the users satisfying the criteria are moved towards the second level. In second level, greedy method is utilized for resource allocation. Extensive simulations have been conducted on MatLab to check the validity of the proposed model. Rate of social welfare degradation and user’s satisfaction are utilized to check the appropriateness and validity of the model. Results from experimentation have shown that proposed model is able to generate enough revenue for the service provider and is able to provide acceptable QoS to identified legitimate users when there is an increase in number of malicious users.

在任何防御DDoS攻击的防御机制中采用激励措施都非常重要。激励是迄今为止提出的大多数防御机制放弃的一个主要概念。它是一种可以激励用户明智地将数据发送到网络的工具。因此，在本文中，我们提出了一种两层的多属性拍卖机制，通过实施支付方案以及提供奖励来激励用户。除此之外，我们还开发了一种信誉评估程序，可以通过监视通过边际效用计算出的信誉分数来识别恶意用户。然后，将识别出的恶意用户映射到不同级别的可疑程度。识别出的合法用户将被转发到一级拍卖，其中服务提供商已添加了虚拟用户以增加用户之间的竞争。为每个用户计算临界值，并将满足条件的用户移至第二级。在第二级，贪婪方法用于资源分配。在MatLab上进行了广泛的仿真，以检验所提出模型的有效性。利用社会福利退化率和用户满意度来检验模型的适当性和有效性。实验结果表明，提出的模型能够为服务提供商产生足够的收入，并且能够在恶意用户数量增加时为已识别的合法用户提供可接受的QoS。为每个用户计算临界值，并将满足条件的用户移至第二级。在第二级，贪婪方法用于资源分配。在MatLab上进行了广泛的仿真，以检验所提出模型的有效性。利用社会福利退化率和用户满意度来检验模型的适当性和有效性。实验结果表明，提出的模型能够为服务提供商产生足够的收入，并且能够在恶意用户数量增加时为已识别的合法用户提供可接受的QoS。为每个用户计算临界值，并将满足条件的用户移至第二级。在第二级，贪婪方法用于资源分配。在MatLab上进行了广泛的仿真，以检验所提出模型的有效性。利用社会福利退化率和用户满意度来检验模型的适当性和有效性。实验结果表明，提出的模型能够为服务提供商产生足够的收入，并且能够在恶意用户数量增加时为已识别的合法用户提供可接受的QoS。在MatLab上进行了广泛的仿真，以检验所提出模型的有效性。利用社会福利退化率和用户满意度来检验模型的适当性和有效性。实验结果表明，提出的模型能够为服务提供商产生足够的收入，并且能够在恶意用户数量增加时为已识别的合法用户提供可接受的QoS。在MatLab上进行了广泛的仿真，以检验所提出模型的有效性。利用社会福利退化率和用户满意度来检验模型的适当性和有效性。实验结果表明，提出的模型能够为服务提供商产生足够的收入，并且能够在恶意用户数量增加时为已识别的合法用户提供可接受的QoS。