A reputation score policy and Bayesian game theory based incentivized mechanism for DDoS attacks mitigation and cyber defense

DDoS attack is one of the most powerful cyber-weapons as it does not wait for a specific server configuration or particular network state to attack or to disrupt any operation of the target machine. Further, it does not require any huge investment and can cause enormous reputational and financial loss to the organization. Additionally, the uneven distribution of resources and incentives on Internet has paved an easy path for attackers to take the repercussions of DDoS attack to a challenging level. Malicious users cannot be assumed to obey network protocols or algorithms. In fact, they tried to take advantage of their knowledge about network to disrupt other users and to gain a maximum share of resources. Therefore, in this paper, we propose a Bayesian game theory-based solution to empower service provider to maximize the social welfare by employing incentives and pricing rules on the users of a network. The service provider and legitimate users are assumed to observe the network for a long time and gain probabilistic knowledge about another user being malicious or not. This probabilistic knowledge is utilized by the service provider and legitimate users to amend their actions to counteract malicious users present in the network. Considering these assumptions and facts, we propose Bayesian pricing and auction mechanism to achieve Bayesian Nash Equilibrium points in different scenarios where probabilistic information proves beneficial for legitimate users and service provider. Further, we propose a reputation assessment and updating mechanism where payment and participation parameters are considered to quantify user's reliability. Extensive experimentation has been carried out using MatLab. We consider the rate of social welfare degradation and variation in user's utility as parameters to validate the proposed model.