Biblio

A web service is a web-based application connected via the internet connectivity. The common web-based applications are deployed using web browsers and web servers. However, the security of Web Service is a major concern issues since it is not widely studied and integrated in the design stage of Web Service standard. They are add-on modules rather a well-defined solutions in standards. So, various web services security solutions have been defined in order to protect interaction over a network. Remote attestation is an authentication technique proposed by the Trusted Computing Group (TCG) which enables the verification of the trusted environment of platforms and assuring the information is accurate. To incorporate this method in web services framework in order to guarantee the trustworthiness and security of web-based applications, a new framework called TrustWeb is proposed. The TrustWeb framework integrates the remote attestation into SSL/TLS protocol to provide integrity information of the involved endpoint platforms. The framework enhances TLS protocol with mutual attestation mechanism which can help to address the weaknesses of transferring sensitive computations, and a practical way to solve the remote trust issue at the client-server environment. In this paper, we describe the work of designing and building a framework prototype in which attestation mechanism is integrated into the Mozilla Firefox browser and Apache web server. We also present framework solution to show improvement in the efficiency level.

Today, beyond a legitimate usage, the numerous advantages of cloud computing are exploited by attackers, and Botnets supporting DDoS attacks are among the greatest beneficiaries of this malicious use. Such a phenomena is a major issue since it strongly increases the power of distributed massive attacks while involving the responsibility of cloud service providers that do not own appropriate solutions. In this paper, we present an original approach that enables a source-based de- tection of UDP-flood DDoS attacks based on a distributed system behavior analysis. Based on a principal component analysis, our contribution consists in: (1) defining the involvement of system metrics in a botcoud's behavior, (2) showing the invariability of the factorial space that defines a botcloud activity and (3) among several legitimate activities, using this factorial space to enable a botcloud detection.

Today, beyond a legitimate usage, the numerous advantages of cloud computing are exploited by attackers, and Botnets supporting DDoS attacks are among the greatest beneficiaries of this malicious use. Such a phenomena is a major issue since it strongly increases the power of distributed massive attacks while involving the responsibility of cloud service providers that do not own appropriate solutions. In this paper, we present an original approach that enables a source-based de- tection of UDP-flood DDoS attacks based on a distributed system behavior analysis. Based on a principal component analysis, our contribution consists in: (1) defining the involvement of system metrics in a botcoud's behavior, (2) showing the invariability of the factorial space that defines a botcloud activity and (3) among several legitimate activities, using this factorial space to enable a botcloud detection.

Online fault diagnosis has been a crucial task for industrial processes. Reconstruction-based fault diagnosis has been drawing special attentions as a good alternative to the traditional contribution plot. It identifies the fault cause by finding the specific fault subspace that can well eliminate alarming signals from a bunch of alternatives that have been prepared based on historical fault data. However, in practice, the abnormality may result from the joint effects of multiple faults, which thus can not be well corrected by single fault subspace archived in the historical fault library. In the present work, an aggregative reconstruction-based fault diagnosis strategy is proposed to handle the case where multiple fault causes jointly contribute to the abnormal process behaviors. First, fault subspaces are extracted based on historical fault data in two different monitoring subspaces where analysis of relative changes is taken to enclose the major fault effects that are responsible for different alarming monitoring statistics. Then, a fault subspace selection strategy is developed to analyze the combinatorial fault nature which will sort and select the informative fault subspaces that are most likely to be responsible for the concerned abnormalities. Finally, an aggregative fault subspace is calculated by combining the selected fault subspaces which represents the joint effects from multiple faults and works as the final reconstruction model for online fault diagnosis. Theoretical support is framed and the related statistical characteristics are analyzed. Its feasibility and performance are illustrated with simulated multi-faults using data from the Tennessee Eastman (TE) benchmark process.
 

Cloud computing is gaining ground and becoming one of the fast growing segments of the IT industry. However, if its numerous advantages are mainly used to support a legitimate activity, it is now exploited for a use it was not meant for: malicious users leverage its power and fast provisioning to turn it into an attack support. Botnets supporting DDoS attacks are among the greatest beneficiaries of this malicious use since they can be setup on demand and at very large scale without requiring a long dissemination phase nor an expensive deployment costs. For cloud service providers, preventing their infrastructure from being turned into an Attack as a Service delivery model is very challenging since it requires detecting threats at the source, in a highly dynamic and heterogeneous environment. In this paper, we present the result of an experiment campaign we performed in order to understand the operational behavior of a botcloud used for a DDoS attack. The originality of our work resides in the consideration of system metrics that, while never considered for state-of-the-art botnets detection, can be leveraged in the context of a cloud to enable a source based detection. Our study considers both attacks based on TCP-flood and UDP-storm and for each of them, we provide statistical results based on a principal component analysis, that highlight the recognizable behavior of a botcloud as compared to other legitimate workloads.

Cloud computing is gaining ground and becoming one of the fast growing segments of the IT industry. However, if its numerous advantages are mainly used to support a legitimate activity, it is now exploited for a use it was not meant for: malicious users leverage its power and fast provisioning to turn it into an attack support. Botnets supporting DDoS attacks are among the greatest beneficiaries of this malicious use since they can be setup on demand and at very large scale without requiring a long dissemination phase nor an expensive deployment costs. For cloud service providers, preventing their infrastructure from being turned into an Attack as a Service delivery model is very challenging since it requires detecting threats at the source, in a highly dynamic and heterogeneous environment. In this paper, we present the result of an experiment campaign we performed in order to understand the operational behavior of a botcloud used for a DDoS attack. The originality of our work resides in the consideration of system metrics that, while never considered for state-of-the-art botnets detection, can be leveraged in the context of a cloud to enable a source based detection. Our study considers both attacks based on TCP-flood and UDP-storm and for each of them, we provide statistical results based on a principal component analysis, that highlight the recognizable behavior of a botcloud as compared to other legitimate workloads.

This article presents results of the recognition process of acoustic fingerprints from a noise source using spectral characteristics of the signal. Principal Components Analysis (PCA) is applied to reduce the dimensionality of extracted features and then a classifier is implemented using the method of the k-nearest neighbors (KNN) to identify the pattern of the audio signal. This classifier is compared with an Artificial Neural Network (ANN) implementation. It is necessary to implement a filtering system to the acquired signals for 60Hz noise reduction generated by imperfections in the acquisition system. The methods described in this paper were used for vessel recognition.