Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
C4.5: Programs for Machine Learning
C4.5: Programs for Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Future Generation Computer Systems
A Survey of Cloud Platforms and Their Future
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part I
Cloud Computing and SOA Convergence in Your Enterprise: A Step-by-Step Guide
Cloud Computing and SOA Convergence in Your Enterprise: A Step-by-Step Guide
CloudCom '09 Proceedings of the 1st International Conference on Cloud Computing
Review: A survey on security issues in service delivery models of cloud computing
Journal of Network and Computer Applications
IEEE Security and Privacy
Understanding Cloud Computing Vulnerabilities
IEEE Security and Privacy
Monitoring Cloud Computing by Layer, Part 1
IEEE Security and Privacy
Cloud security defence to protect cloud computing against HTTP-DoS and XML-DoS attacks
Journal of Network and Computer Applications
Secure virtualization for cloud computing
Journal of Network and Computer Applications
Development of Grid e-Infrastructure in South-Eastern Europe
Journal of Grid Computing
Resources and Services of the EGEE Production Infrastructure
Journal of Grid Computing
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Trust Issues that Create Threats for Cyber Attacks in Cloud Computing
ICPADS '11 Proceedings of the 2011 IEEE 17th International Conference on Parallel and Distributed Systems
Monitoring Insiders Activities in Cloud Computing Using Rule Based Learning
TRUSTCOM '11 Proceedings of the 2011IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications
A variational formulation for the multilayer perceptron
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Classifying different denial-of-service attacks in cloud computing using rule-based learning
Security and Communication Networks
Review: An intrusion detection and prevention system in cloud computing: A systematic review
Journal of Network and Computer Applications
Survey Cloud monitoring: A survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Management Issues with Cloud Computing
Proceedings of the Second International Conference on Innovative Computing and Cloud Computing
On the security of auditing mechanisms for secure cloud storage
Future Generation Computer Systems
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The long-term potential benefits through reduction of cost of services and improvement of business outcomes make Cloud Computing an attractive proposition these days. To make it more marketable in the wider IT user community one needs to address a variety of information security risks. In this paper, we present an extensive review on cloud computing with the main focus on gaps and security concerns. We identify the top security threats and their existing solutions. We also investigate the challenges/obstacles in implementing threat remediation. To address these issues, we propose a proactive threat detection model by adopting three main goals: (i) detect an attack when it happens, (ii) alert related parties (system admin, data owner) about the attack type and take combating action, and (iii) generate information on the type of attack by analyzing the pattern (even if the cloud provider attempts subreption). To emphasize the importance of monitoring cyber attacks we provide a brief overview of existing literature on cloud computing security. Then we generate some real cyber attacks that can be detected from performance data in a hypervisor and its guest operating systems. We employ modern machine learning techniques as the core of our model and accumulate a large database by considering the top threats. A variety of model performance measurement tools are applied to verify the model attack prediction capability. We observed that the Support Vector Machine technique from statistical machine learning theory is able to identify the top attacks with an accuracy of 97.13%. We have detected the activities using performance data (CPU, disk, network and memory performance) from the hypervisor and its guest operating systems, which can be generated by any cloud customer using built-in or third party software. Thus, one does not have to depend on cloud providers' security logs and data. We believe our line of thoughts comprising a series of experiments will give researchers, cloud providers and their customers a useful guide to proactively protect themselves from known or even unknown security issues that follow the same patterns.