Internet-based computing has lead to an emergence of a large number of threats. One of the major threat is DDoS (Distributed Denial of Service) attack. Recent incidents have shown that DDoS attacks have the capability of shutting a business not for a day but weeks. DDoS attacks have a greater impact on multi-tenant clouds than traditional infrastructure. DDoS attacks in the cloud, take the shape of EDoS (Eco- nomic denial of sustainability) attacks. In EDoS, instead of “Service Denial”, economic harms occur due to fake resource usage and subse- quent addition or buying of resources using on-demand provisioning. To detect and mitigate DDoS attacks in the cloud, we argue that on-demand resource allocation (known as auto-scaling) should also be looked, in addition to network or application layer mitigation. We have proposed a novel mitigation strategy, DARAC, which makes auto-scaling decisions by accurately differentiating between legitimate requests and attacker traffic. Attacker traffic is detected and dropped based on human behav- ior analysis based detection. We also argue that most of the solutions in the literature, do not pay much attention to the service quality to legitimate requests during an attack. We calculate the share of legiti- mate clients in resource addition/buying and make subsequent accurate auto-scaling decisions. Experimental results show that DARAC mitigates various DDoS attack sets and take accurate and quick auto-scaling deci- sions for various legitimate and attacker traffic combinations saving from EDoS. We also show how proposed mechanism could make “arms-race” very difficult for the attackers as the resource need to defeat DARAC mechanism on a very small capacity server is huge. Results also show significant improvements in the average response time of the web-service under attack, in addition to infrastructure cost savings up to 50% in heavy attack cases.