Narrowing the scope of failure prediction using targeted fault load injection

As society becomes more dependent upon computer systems to perform increasingly critical tasks, ensuring that those systems do not fail becomes increasingly important. Many organizations depend heavily on desktop computers for day-to-day operations. Unfortunately, the software that runs on these computers is written by humans and, as such, is still subject to human error and consequent failure. A natural solution is to use statistical machine learning to predict failure. However, since failure is still a relatively rare event, obtaining labelled training data to train these models is not a trivial task. This work presents new simulated fault-inducing loads that extend the focus of traditional fault injection techniques to predict failure in the Microsoft enterprise authentication service and Apache web server. These new fault loads were successful in creating failure conditions that were identifiable using statistical learning methods, with fewer irrelevant faults being created

Security assurance assessment methodology for hybrid clouds

The emergence of the cloud computing paradigm has altered the delivery models for ICT services. Unfortunately, the widespread use of the cloud has a cost, in terms of reduced transparency and control over a user's information and services. In addition, there are a number of well-understood security and privacy challenges that are specific to this environment. These drawbacks are particularly problematic to operators of critical information infrastructures that want to leverage the benefits of cloud. To improve transparency and provide assurances that measures are in place to ensure security, novel approaches to security evaluation are needed. To evaluate the security of services that are deployed in the cloud requires an evaluation of complex multi-layered systems and services, including their interdependencies. This is a challenging task that involves significant effort, in terms of both computational and human resources. With these challenges in mind, we propose a novel security assessment methodology for analysing the security of critical services that are deployed in cloud environments. Our methodology offers flexibility, in that tailored policy-driven security assessments can be defined based on a user's requirements, relevant standards, policies, and guidelines. We have implemented and evaluated a system that supports online assessments using our methodology, which acquires and processes large volumes of security-related data without affecting the performance of the services in a cloud environment

Botnet detection techniques: review, future trends, and issues

NoIn recent years, the Internet has enabled access to widespread remote services in the distributed computing environment; however, integrity of data transmission in the distributed computing platform is hindered by a number of security issues. For instance, the botnet phenomenon is a prominent threat to Internet security, including the threat of malicious codes. The botnet phenomenon supports a wide range of criminal activities, including distributed denial of service (DDoS) attacks, click fraud, phishing, malware distribution, spam emails, and building machines for illegitimate exchange of information/materials. Therefore, it is imperative to design and develop a robust mechanism for improving the botnet detection, analysis, and removal process. Currently, botnet detection techniques have been reviewed in different ways; however, such studies are limited in scope and lack discussions on the latest botnet detection techniques. This paper presents a comprehensive review of the latest state-of-the-art techniques for botnet detection and figures out the trends of previous and current research. It provides a thematic taxonomy for the classification of botnet detection techniques and highlights the implications and critical aspects by qualitatively analyzing such techniques. Related to our comprehensive review, we highlight future directions for improving the schemes that broadly span the entire botnet detection research field and identify the persistent and prominent research challenges that remain open.University of Malaya, Malaysia (No. FP034-2012A