Security in Big Data and Cloud Computing - Large Scale and Big Data: Processing and Management - page 580

For example, a botnet attack on Amazon's cloud infrastructure was reported in
2009 [1]. Besides attacking cloud infrastructure, adversaries can use the cloud to
launch attack on other systems. For example, an adversary can rent hundreds of vir-
tual machines (VM) to launch a distributed denial of service (DDoS) attack. After
a successful attack, they can erase all the traces of the attack by turning off the
VMs. A criminal can also keep their secret files (e.g., child pornography, terrorist
documents) in cloud storage and can destroy all evidence from their local storage to
remain clean. In light of the above, it is important to examine and understand the
critical security issues in cloud computing and Big Data. The goal of this chapter
is to motivate and educate researchers about the various security threats in cloud
computing and Big Data and provide them with a guideline toward solving the
security challenges.

19.1.2 t oPiCs C overeD
Big Data and cloud security problems may be roughly grouped into categories involv-
ing data integrity, computation integrity, data confidentiality and privacy, misuse
detection, and novel attacks. We point out that to properly address open problems
in the security of Big Data and clouds, it is vital to understand what makes cloud
security different from traditional distributed systems security.
The main security and privacy issues in Big Data and cloud computing in form of
the following research questions:

• Exploitation of Co-tenancy : How can we prevent attackers from exploit-

ing cotenancy in attacking the infrastructure and/or other clients [27]?

• Secure Architecture for the Cloud : How do we design cloud comput-

ing architectures that are semitransparent and provide clients with some
accountability and control over security [30]?

• Accountability for Outsourced Data : How can clients get assurance/proofs

that the cloud provider is actually storing data, is not tampering with data,
and can make the data available on demand [3,20]?

• Confidentiality of Data and Computation : How can we ensure confiden-

tiality of data and computations in a cloud?

• Privacy : How do we perform outsourced computation while guaranteeing

user privacy [28]?

• Verifying Outsourced Computation : How can we (efficiently) verify the

accuracy of outsourced computation [12]?

• Verifying Capability : How can a client remotely verify the capability and

resource capacity of a cloud provider [8]?

• Cloud Forensics : How can we augment cloud infrastructures to allow

forensic investigations [23]?

• Misuse Detection : How can we rapidly detect misbehavior of clients in a

cloud [18]?

• Resource Accounting and Economic Attacks : How do we ensure proper,

verifiable accounting, and prevent attackers from exploiting the pay as you
go model of clouds [32]?