A Novel Cloud Security Enhancement Scheme to Defend against DDoS Attacks by using Deep Learning Strategy

Cloud computing is a recent technology that allows users to create services on-demand. Cloud computing has achieved benefits as a result of its self-service capability as well as on demand services. This offers significant adaptability to its users, as they simply pay for the services they require, rather than worrying about the expense of equipment or software support. The major benefit of utilizing the cloud -based environment in organization is to enhance the data maintenance scheme in an easy way as well as improve the integrity of service to avoid manual flaws over maintenance. However, the remote cloud based data maintenance and evaluation leads certain security related threats, especially with Distributed Denial of Service (DDoS) Attacks. These attacks are caused by attempts of intruders or hackers to hack the data present in the server end or traverse between client and server end. The attacker obtains the data and modifies it according to their convenience without the knowledge of the data owner. These kinds of attacks are most dangerous, and the confidentiality of the data is totally disturbed due to such threats. This paper is intended to design a novel deep learning strategy called Modified Learning based Cloud Attack Detection (MLCAD), in which it adapts the features from the conventional security handling scheme called Intelligent Attack Identification Strategy (IAIS). This proposed MLCAD approach identifies the DDoS attacks over cloud environment by means of analyzing the authorization and authentication logics of the respective user, examining the Internet Protocol (IP) Address mentioned in the relevant request as well as the metadata acquired from the user end. These provisions have made the proposed approach MLCAD to act better to identify the DDoS attack in an efficient manner with full significance. The paper provides the proper graphical proofs to prove the integrity and performance of the proposed approach in a clear manner.