Mitigating Economic Denial of Sustainability Attacks to Secure Cloud Computing Environments

Abstract

In cloud computing environment where the infrastructure is shared by millions of users, attackers have the opportunity to ensure more damage to the compromised resources. The main aim of such attacks is to saturate and overload the system network through a massive data packets size flooding toward a victim server and to block the service to customers. The Distributed Denialofservice (DDoS) attack is considered one of the largest threats to the Quality of Service (QoS) of cloud services which is used to deny access for legitimate users of an online service. However, Economic Denial of Sustainability (EDoS) attack is a special breed of DDoS attack that attack exploits auto scaling feature of cloud. The Cloud Service Provider (CSP) scales the architecture automatically to serve those requests for which cloud consumer is charged. A consumer expects a sustainable profit after hosting his application on cloud. The attacker purpose is to guarantee the service unavailability and maximize the financial loss costs by increasing the cost and decreasing the profit. Hence, in this paper we propose a novel mitigation system against the EDoS attacks. Our system consists of source Checking, Counting, and Turing Test. The obtained simulation results show that our system works efficiently to mitigate the EDoS attack in cloud environment.

An efficient EDoS-DOME system in cloud computing using obfuscated IP spoofing technique and RCDH-ENN detection technique

“…Furthermore, the cloud service model's adopters are charged centered on a pay-per-use basis of the cloud's server along with network resources that are extensively identified as utility computing. Most organizations avoid CC because of the security issue, albeit this technology offers disparate services to its users (Aborujilah and Musa 2017;Zekri et al 2017). On account of the intruding behavior of attackers, accessing the internet through the cloud brings about vulnerable attacks on the stored data.…”

A Machine Learning Approach for DDoS (Distributed Denial of Service) Attack Detection Using Multiple Linear Regression

“…Over the years, the most widely used methods to detect Intrusion detection with DoS and DDoS attacks are based on machine learning algorithms. One such algorithm where the results were accurate and efficient in detecting the DDoS attack was using Decision Tree C4.5 algorithm [10]. Liao et al [11] proposed a scheme based on SVM to detect the DDoS attacks using detection scheme base algorithm.…”