Game theoretic modeling of economic denial of sustainability (EDoS) attack in cloud computing

In this paper, we develop a novel game theoretic model of the interactions between an EDoS attacker and the defender based on a signaling game that is a dynamic game of incomplete information. We then derive the best defense strategies for the network defender to respond to the EDoS attacks. That is, we compute the perfect Bayesian Nash Equilibrium (PBE) of the proposed game model such as the pooling PBE, separating PBE and mixed strategy PBE. In the pooling equilibrium, each type of the attacker takes the same action and the attacker's type is not revealed to the defender, whereas in the separating equilibrium, each type of the attacker uses different actions and hence the attacker's type is completely revealed to the defender. On the other hand, in the mixed strategy PBE, both the attacker and the defender randomize their strategies to optimize their payoffs. Numerical illustration is also presented to show the efficacy of the proposed model.

Cloud based EDoS Attack Prevention System Using Dual Access Control Mechanism

Cloud based data storage is becoming very popular nowadays as one can easily download any document from anytime and anywhere. However documents security is very important in cloud storage as cloud is a third party server which can be accessed by administrators. There are many literatures available to improve document security on cloud and most of the literatures proposed various data encryption techniques. However, simply encrypting data (e.g., via AES) cannot fully address the practical need of data management. Besides, an effective access control over download request also needs to be considered so that Economic Denial of Sustainability (EDoS) attacks cannot be launched to hinder users from enjoying service. In this project, we consider the dual access control, in the context of cloud-based storage, in the sense that we design a control mechanism over both data access and download request without loss of security and efficiency. Along with dual access control we also focus on document security by using modified AES algorithm.

Accounting and Privacy Preserving of Data Owner in Cloud Storage

People use the support of distributed computing however can't completely believe the cloud suppliers to have protection and confidential information. To guarantee secrecy, data owners relocate encoded information rather than plain texts. To divide the encoded documents with different clients, Ciphertext-Policy Attribute-based Encryption (CP-ABE) can be utilized. But this cannot become secure against some other assaults. Many other schemes did not gave guarantee that the cloud provider has the power to check whether a downloader can unscramble or not. Consequently, these files are accessible to everybody who is approachable to the cloud storage. An intentionally harmful assailant can download a great many records to start Economic Denial of Sustainability (EDoS) attacks, it will to a great extent expend the cloud asset. The owner will bear all the expenses for the cloud storage but the cloud provider doesn’t provide the whole information about the access or usage. There is no transparency for the owner. We have to solve these concerns. In order to this we are going to propose a solution for securing the encrypted data from EDoS attacks and providing the owner whole usage information about the cloud storage. We are implementing by using the arbitrary access policy of CP-ABE.

A Hybrid Secure Storage Scheme to Avoid EDOS Attacks in Cloud Computing

In advent of cloud environment, cloud operator is not a completely trusted to put on private information, because of lack of consumer to cloud control. To assurance privacy, documents sharer deploy their encipher documents. Encipher documents dispense to among consumers using CP-ABE scheme. But it is not completely safe in opposition to different assaults. The prior knowledge cannot offer any verification ability to cloud operator whether the user can decipher or not. Various invaders may obtain lot of document by initiate EDoS assaults. The consumer of cloud abides cost. To handle above issues, this article suggests a problem solving plan to safe encipher cloud repository from EDoS assaults and maintain supply utilization. It utilizes CP-ABE tactics in a black-box method furthermore accomplish impulsive entryway contract epithetical CP-ABE. We tend to present 2 mechanisms for various styles, observed via achievement and shield research.

Mitigating Economic Denial of Sustainability (EDoS) in Cloud Environment using Genetic Algorithm and Artificial Neural Network

Economic Denial of Sustainability (EDoS) is a latest threat in the cloud environment in which EDoS attackers continually request huge number of resources that includes virtual machines, virtual security devices, virtual networking devices, databases and so on to slowly exploit illegal traffic to trigger cloud-based scaling capabilities. As a result, the targeted cloud ends with a consumer bill that could lead to bankruptcy. This paper proposes an intelligent reactive approach that utilizes Genetic Algorithm and Artificial Neural Network (GANN) for classification of cloud server consumer to minimize the effect of EDoS attacks and will be beneficial to small and medium size organizations. EDoS attack encounters the illegal traffic so the work is progressed into two phases: Artificial Neural Network (ANN) is used to determine affected path and to detect suspected service provider out of the detected affected route which further consist of training and testing phase. The properties of every server are optimized by using an appropriate fitness function of Genetic Algorithm (GA) based on energy consumption of server. ANN considered these properties to train the system to distinguish between the genuine overwhelmed server and EDoS attack affected server. The experimental results show that the proposed Genetic and Artificial Neural Network (GANN) algorithm performs better compared to existing Fuzzy Entropy and Lion Neural Learner (FLNL) technique with values of precision, recall and f-measure are increased by 3.37%, 10.26% and 6.93% respectively.