To support a wide range of applications, cloud computing has a variety of services. It has a number of positive acceptance tales as well as a couple of negative ones including security breaches. The versatile usage of cloud services to store Sensitive and personal data in cloud become hesitated by many organizations because of security issues. A new model of relying on a third-party auditor (TPA) has been adopted to improve trust and entice adoption between cloud clients (CC). Hence, we require a dynamic approach to control the privacy and integrity problem that occur across the cloud computing. Decentralized Attribute based encryption techniques and FHE approach is used to overwhelmed the issues. In this proposed scheme, the integrity checking is verified and auditor by the TPA without have any knowledge of the data content and double encryption is performed on the data stored in cloud. the data owner encrypts the data using ABK-XE (Attribute Based Key generation with XOR encryption) technique and send it to tag server whose encrypt the data again using ECEA (Elliptical Curve Elgamal) algorithm and generate the signature and unique ID using SHA-1 algorithm then store the data in Cloud Environment. The proposed algorithm is an integration of auditing scheme with Symmetric key Encryption and Homomorphic Encryption.

Cloud computing would be an easy method to obtain services, resources and applications from any location on the internet. In the future of data generation, it is an unavoidable conclusion. Despite its many attractive properties, the cloud is vulnerable to a variety of attacks. One such well-known attack that emphasizes the availability of amenities is the Distributed Denial of Service (DDoS). A DDoS assault overwhelms the server with massive quantities of regular or intermittent traffic. It compromises with the cloud servers' services and makes it harder to reply to legitimate users of the cloud. A monitoring system with correct resource scaling approach should be created to regulate and monitor the DDoS assault. The network is overwhelmed with excessive traffic of significant resource usage requests during the attack, resulting in the denial of needed services to genuine users. In this research, a unique way to the analyze resources used by the cloud users, lowering of the resources consumed is done when the network is overburdened with excessive traffic, and the dynamic cloud load balancing algorithm DCLB (Dynamic Cloud Load Balancing) is used to balance the overhead towards the server. The core premise is to monitor traffic using the fuzzy logic approach, which employs different traffic parameters in conjunction with various built in measured to recognize the DDoS attack traffic in the network. Finally, the proposed method shows a 93% of average detection rate when compared to the existing model. This method is a unique attempt to comprehend the importance of DDoS mitigation techniques as well as good resource management during an attack and analysis of the.

With distributed storage accommodations, users can remotely store their information to the cloud and understand the information imparting to other people. Remote information respectability evaluating is proposed to guarantee the honesty of the information put away in the cloud. In some commonplace distributed storage frameworks, for example, the Electronic Health Records (EHRs) framework, the cloud file may contain some delicate data. The touchy data ought not be presented to others when the cloud file is shared. Scrambling the entire shared file can understand the touchy data obnubilating, however will make this mutual file incapable to be used by others. So the entelechy of data sharing with sensitive obnubilating in remote data integrity auditing still has not been explored up to now. In order to address this quandary, a remote data integrity auditing scheme has been proposed that realizes data sharing with sensitive information obnubilating. In this plot, a sanitizer is utilized to sanitize the information squares comparing to the touchy data of the record and changes these information blocks' marks into substantial ones for the sanitized record. These marks are habituated to confirm the keenness of the sanitized record within the stage of astuteness reviewing. As a result, this plot makes the record put away within the cloud able to be shared and utilized by others on the condition that the delicate data is obnubilated, whereas the farther information astuteness reviewing is still able to be productively executed. In the mean time, the proposed plot is predicated on character predicated cryptography, which rearranges the confused certificate administration. The security examination and the execution assessment appear that the proposed plot is secure and proficient.

Distributed Denial of Service (DDoS) is a major attack carried out by attackers leveraging critical cloud computing technologies. DDoS attacks are carried out by flooding the victim servers with a massive volume of malicious traffic over a short period, Because of the enormous amount of malicious traffic, such assaults are easily detected. As a result, DDoS operations are increasingly appealing to attackers due to their stealth and low traffic rates, DDoS assaults with low traffic rates are also difficult to detect. In recent years, there has been a lot of focus on defense against low-rate DDoS attacks. This paper presents a two-phase detection technique for mitigating and reducing LRDDoS threats in a cloud environment. The proposed model includes two phases: one for calculating predicted packet size and entropy, and another for calculating the covariance vector. In this model, each cloud user accesses the cloud using the virtual machine, which has a unique session ID. This model identifies all LRDDoS assaults that take place by using different protocols (TCP, UDP, ICMP). The experiment's findings demonstrate, how the suggested data packet size, IP address, and flow behavior is used to identify attacks and prevent hostile users from using cloud services. The VM instances used by different users are controlled by this dynamic mitigation mechanism, which also upholds the cloud service quality. The results of the experiments reveal that the suggested method identifies LRDDoS attacks with excellent accuracy and scalability.

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