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
On learning algorithm selection for classification
Applied Soft Computing
Hey, you, get off of my cloud: exploring information leakage in third-party compute clouds
Proceedings of the 16th ACM conference on Computer and communications security
CloudCom '09 Proceedings of the 1st International Conference on Cloud Computing
A Layered Security Approach for Cloud Computing Infrastructure
ISPAN '09 Proceedings of the 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks
IEEE Security and Privacy
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Understanding Cloud Computing Vulnerabilities
IEEE Security and Privacy
A survey of risks, threats and vulnerabilities in cloud computing
Proceedings of the 2011 International Conference on Intelligent Semantic Web-Services and Applications
Cloud security defence to protect cloud computing against HTTP-DoS and XML-DoS attacks
Journal of Network and Computer Applications
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
Future Generation Computer Systems
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From traditional networking to cloud computing, one of the essential but formidable tasks is to detect cyber attacks and their types. A cloud provider's unwillingness to share security-related data with its clients adds to the difficulty of detection by a cloud customer. The research contributions in this paper are twofold. First, an investigative survey on cloud computing is conducted with the main focus on gaps that is hindering cloud adoption, accompanied by a review of the threat remediation challenges. Second, some thoughts are constructed on novel approaches to address some of the widely discussed denial-of-service (DoS) attack types by using machine learning techniques. We evaluate the techniques' performances by using statistical ranking-based methods, and find the rule-based learning technique C4.5, from a set of popular learning algorithms, as an efficient tool to classify various DoS attacks in the cloud platform. The novelty of our rather rigorous analysis is in its ability to identify insider's activities and other DoS attacks by using performance data. The reason for using performance data rather than traditional logs and security-related data is that the performance data can be collected by the customers themselves without any help from cloud providers. To the best of our knowledge, no one has made such attempts before. Our findings and thoughts captured through a series of experiments in our constructed cloud server are expected to give researchers, cloud providers and customers additional insight and tools to proactively protect themselves from known or perhaps even unknown security issues that have similar patterns. Copyright © 2012 John Wiley & Sons, Ltd.