The Experts below are selected from a list of 243 Experts worldwide ranked by ideXlab platform
Cem Gurkok - One of the best experts on this subject based on the ideXlab platform.
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Chapter 63 – Securing Cloud Computing Systems
Computer and Information Security Handbook, 2017Co-Authors: Cem GurkokAbstract:Cloud computing is a method of delivering computing resources. Cloud computing services, ranging from data storage and processing to software such as customer relationship management systems, are now available instantly and on demand. In times of financial and economic hardship, this new low-cost ownership model for computing has received lots of attention and is seeing increasing global investment. Generally speaking, cloud computing provides implementation agility, lower capital expenditure, location independence, resource pooling, Broad Network Access, reliability, scalability, elasticity, and ease of maintenance. While in most cases cloud computing can improve security due to ease of management, the lack of knowledge and experience of the provider can jeopardize customer environments. This chapter discusses various cloud computing environments and methods to make them more secure for hosting companies and their customers.
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Chapter 4 – Securing Cloud Computing Systems
Network and System Security, 2014Co-Authors: Cem GurkokAbstract:Cloud computing is a method of delivering computing resources. Cloud computing services ranging from data storage and processing to software, such as customer relationship management systems, are now available instantly and on demand. In times of financial and economic hardship, this new low cost of ownership model for computing has gotten lots of attention and is seeing increasing global investment. Generally speaking, cloud computing provides implementation agility, lower capital expenditure, location independence, resource pooling, Broad Network Access, reliability, scalability, elasticity, and ease of maintenance. While in most cases cloud computing can improve security due to ease of management, the provider’s lack of knowledge and experience can jeopardize customer environments. This chapter aims to discuss various cloud computing environments and methods to make them more secure for hosting companies and their customers.
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Securing Cloud Computing Systems
Computer and Information Security Handbook, 2013Co-Authors: Cem GurkokAbstract:Cloud computing is a method of delivering computing resources. Cloud computing services ranging from data storage and processing to software, such as customer relationship management systems, are now available instantly and on demand. In times of financial and economic hardship, this new low cost of ownership model for computing has gotten lots of attention and is seeing increasing global investment. Generally speaking, cloud computing provides implementation agility, lower capital expenditure, location independence, resource pooling, Broad Network Access, reliability, scalability, elasticity, and ease of maintenance. While in most cases cloud computing can improve security due to ease of management, the provider’s lack of knowledge and experience can jeopardize customer environments. This chapter aims to discuss various cloud computing environments and methods to make them more secure for hosting companies and their customers.
Ioannis M. Stephanakis - One of the best experts on this subject based on the ideXlab platform.
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EANN Workshops - Anomaly Detection In Secure Cloud Environments Using a Self-Organizing Feature Map (SOFM) Model For Clustering Sets of R-Ordered Vector-Structured Features
Proceedings of the 16th International Conference on Engineering Applications of Neural Networks (INNS) - EANN '15, 2015Co-Authors: Ioannis M. Stephanakis, Ioannis P. Chochliouros, Evangelos Sfakianakis, Noorulhassan ShiraziAbstract:Cloud computing delivers services over virtualized Networks to many end-users. Cloud services are characterized by such attributes as on-demand self-service, Broad Network Access, resource pooling, rapid and elastic resource provisioning and metered services of various qualities. Cloud Networks provide data as well as multimedia and video services. Cloud computing for critical structure IT is a relative new area of potential applications. Cloud Networks are classified into private cloud Networks, public cloud Networks and hybrid cloud Networks. Anomaly detection systems are defined as a branch of intrusion detection systems that deal with identifying anomalous events with respect to normal system behavior. A novel application of a Self-Organizing-Feature Map (SOFM) of reduced/aggregate sets of ordered vector structured features that are used for detecting anomalies in the context of secure cloud environments is herein proposed. Multivalue inputs consist of reduced/aggregate ordered sets of vector and binary features. The nodes of the SOFM - after training - are indicative of local distributions of feature measurements during normal cloud operation. Anomalies are detected as outliers of the trained SOFM. Each structured vector consists of binary as well as histogram data. The aggregated Canberra distance is used to order histogram data whereas the Jaccard distance is used for multivalue binary data. The so-called Cross-Order Distance Matrix is defined for both cases. The distance depends upon the selection of a similarity/distance measure and a method for operating upon the elements of the Cross-Order Distance Matrix. Several methods of estimating the distance between two ordered sets of features are investigated in the course of this paper.
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anomaly detection in secure cloud environments using a self organizing feature map sofm model for clustering sets of r ordered vector structured features
International Conference on Engineering Applications of Neural Networks, 2015Co-Authors: Ioannis M. Stephanakis, Ioannis P. Chochliouros, Evangelos Sfakianakis, Noorulhassan ShiraziAbstract:Cloud computing delivers services over virtualized Networks to many end-users. Cloud services are characterized by such attributes as on-demand self-service, Broad Network Access, resource pooling, rapid and elastic resource provisioning and metered services of various qualities. Cloud Networks provide data as well as multimedia and video services. Cloud computing for critical structure IT is a relative new area of potential applications. Cloud Networks are classified into private cloud Networks, public cloud Networks and hybrid cloud Networks. Anomaly detection systems are defined as a branch of intrusion detection systems that deal with identifying anomalous events with respect to normal system behavior. A novel application of a Self-Organizing-Feature Map (SOFM) of reduced/aggregate sets of ordered vector structured features that are used for detecting anomalies in the context of secure cloud environments is herein proposed. Multivalue inputs consist of reduced/aggregate ordered sets of vector and binary features. The nodes of the SOFM - after training - are indicative of local distributions of feature measurements during normal cloud operation. Anomalies are detected as outliers of the trained SOFM. Each structured vector consists of binary as well as histogram data. The aggregated Canberra distance is used to order histogram data whereas the Jaccard distance is used for multivalue binary data. The so-called Cross-Order Distance Matrix is defined for both cases. The distance depends upon the selection of a similarity/distance measure and a method for operating upon the elements of the Cross-Order Distance Matrix. Several methods of estimating the distance between two ordered sets of features are investigated in the course of this paper.
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EANN (2) - A Particle Swarm Optimization (PSO) Model for Scheduling Nonlinear Multimedia Services in Multicommodity Fat-Tree Cloud Networks
Engineering Applications of Neural Networks, 2013Co-Authors: Ioannis M. Stephanakis, Ioannis P. Chochliouros, George Caridakis, Stefanos KolliasAbstract:Cloud computing delivers computing services over virtualized Networks to many end-users. Virtualized Networks are characterized by such attributes as on-demand self-service, Broad Network Access, resource pooling, rapid and elastic resource provisioning and metered services at various qualities. Cloud Networks provide data as well as multimedia and video services. They are classified into private cloud Networks, public cloud Networks and hybrid cloud Networks. Linear video services include Broadcasting and in-stream video that may be viewed in a video player whereas non-linear video services include a combination of in-stream video with on-demand services, which are originated from distributed servers in the Network and deliver interactive and pay-per view content. Furthermore heterogeneous delivery Networks that include fixed and mobile internet infrastructures require that adaptive video streaming should be carried out at Network boundaries based on such protocols as HTTP Live Streaming (HLS). Distributed processing of nonlinear video services in cloud environments is addressed in the present work by defining Distributed Acyclic Graphs (DAG) models for multimedia processes executed by a set of non-locally confined virtual machines. A novel discrete multivalue Particle Swarm Optimization (PSO) algorithm is proposed in order to optimize task scheduling and workflow. Numerical simulations regarding such measures as Schedule-Length-Ratio (SLR) and Speedup are given for novel fat-tree cloud architectures.
Noorulhassan Shirazi - One of the best experts on this subject based on the ideXlab platform.
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anomaly detection in secure cloud environments using a self organizing feature map sofm model for clustering sets of r ordered vector structured features
International Conference on Engineering Applications of Neural Networks, 2015Co-Authors: Ioannis M. Stephanakis, Ioannis P. Chochliouros, Evangelos Sfakianakis, Noorulhassan ShiraziAbstract:Cloud computing delivers services over virtualized Networks to many end-users. Cloud services are characterized by such attributes as on-demand self-service, Broad Network Access, resource pooling, rapid and elastic resource provisioning and metered services of various qualities. Cloud Networks provide data as well as multimedia and video services. Cloud computing for critical structure IT is a relative new area of potential applications. Cloud Networks are classified into private cloud Networks, public cloud Networks and hybrid cloud Networks. Anomaly detection systems are defined as a branch of intrusion detection systems that deal with identifying anomalous events with respect to normal system behavior. A novel application of a Self-Organizing-Feature Map (SOFM) of reduced/aggregate sets of ordered vector structured features that are used for detecting anomalies in the context of secure cloud environments is herein proposed. Multivalue inputs consist of reduced/aggregate ordered sets of vector and binary features. The nodes of the SOFM - after training - are indicative of local distributions of feature measurements during normal cloud operation. Anomalies are detected as outliers of the trained SOFM. Each structured vector consists of binary as well as histogram data. The aggregated Canberra distance is used to order histogram data whereas the Jaccard distance is used for multivalue binary data. The so-called Cross-Order Distance Matrix is defined for both cases. The distance depends upon the selection of a similarity/distance measure and a method for operating upon the elements of the Cross-Order Distance Matrix. Several methods of estimating the distance between two ordered sets of features are investigated in the course of this paper.
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EANN Workshops - Anomaly Detection In Secure Cloud Environments Using a Self-Organizing Feature Map (SOFM) Model For Clustering Sets of R-Ordered Vector-Structured Features
Proceedings of the 16th International Conference on Engineering Applications of Neural Networks (INNS) - EANN '15, 2015Co-Authors: Ioannis M. Stephanakis, Ioannis P. Chochliouros, Evangelos Sfakianakis, Noorulhassan ShiraziAbstract:Cloud computing delivers services over virtualized Networks to many end-users. Cloud services are characterized by such attributes as on-demand self-service, Broad Network Access, resource pooling, rapid and elastic resource provisioning and metered services of various qualities. Cloud Networks provide data as well as multimedia and video services. Cloud computing for critical structure IT is a relative new area of potential applications. Cloud Networks are classified into private cloud Networks, public cloud Networks and hybrid cloud Networks. Anomaly detection systems are defined as a branch of intrusion detection systems that deal with identifying anomalous events with respect to normal system behavior. A novel application of a Self-Organizing-Feature Map (SOFM) of reduced/aggregate sets of ordered vector structured features that are used for detecting anomalies in the context of secure cloud environments is herein proposed. Multivalue inputs consist of reduced/aggregate ordered sets of vector and binary features. The nodes of the SOFM - after training - are indicative of local distributions of feature measurements during normal cloud operation. Anomalies are detected as outliers of the trained SOFM. Each structured vector consists of binary as well as histogram data. The aggregated Canberra distance is used to order histogram data whereas the Jaccard distance is used for multivalue binary data. The so-called Cross-Order Distance Matrix is defined for both cases. The distance depends upon the selection of a similarity/distance measure and a method for operating upon the elements of the Cross-Order Distance Matrix. Several methods of estimating the distance between two ordered sets of features are investigated in the course of this paper.
Shashikala Tapaswi - One of the best experts on this subject based on the ideXlab platform.
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Defense Mechanisms Against DDoS Attacks in a Cloud Computing Environment: State-of-the-Art and Research Challenges
IEEE Communications Surveys & Tutorials, 2019Co-Authors: Neha Agrawal, Shashikala TapaswiAbstract:The salient features of cloud computing (such as on-demand self-service, resource pooling, Broad Network Access, rapid elasticity, and measured service) are being exploited by attackers to launch the severe Distributed Denial of Service (DDoS) attack. Generally, the DDoS attacks in such an environment have been implemented by flooding a huge volume (high-rate) of malicious traffic to exhaust the victim servers’ resources. Due to this huge volume of malicious traffic, such attacks can be easily detected. Thus, attackers are getting attracted towards the low-rate DDoS attacks, slowly. Low-rate DDoS attacks are difficult to detect due to their stealthy and low-rate traffic. In the recent years, many efforts have been devoted to defend against the low-rate DDoS attacks. By utilizing the salient features of cloud computing, it becomes easy for an attacker to launch sophisticated low-rate DDoS attacks. Thus, the study of various DDoS attacks and their corresponding defense approaches becomes essential to protect the cloud infrastructure from fatal effects of DDoS attacks. This paper presents a comprehensive taxonomy of all the possible variants of cloud DDoS attacks solutions with detailed insight into the characterization, prevention, detection, and mitigation mechanisms. The paper provides a detailed discussion on essential performance metrics to evaluate various defense solutions and their behavior in a cloud environment. The purpose of this survey paper is to excite the cloud security researchers to develop effective defense solutions against the various DDoS attacks. The research gaps and challenges are found, and described in the paper while future research directions are outlined.
Ioannis P. Chochliouros - One of the best experts on this subject based on the ideXlab platform.
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EANN Workshops - Anomaly Detection In Secure Cloud Environments Using a Self-Organizing Feature Map (SOFM) Model For Clustering Sets of R-Ordered Vector-Structured Features
Proceedings of the 16th International Conference on Engineering Applications of Neural Networks (INNS) - EANN '15, 2015Co-Authors: Ioannis M. Stephanakis, Ioannis P. Chochliouros, Evangelos Sfakianakis, Noorulhassan ShiraziAbstract:Cloud computing delivers services over virtualized Networks to many end-users. Cloud services are characterized by such attributes as on-demand self-service, Broad Network Access, resource pooling, rapid and elastic resource provisioning and metered services of various qualities. Cloud Networks provide data as well as multimedia and video services. Cloud computing for critical structure IT is a relative new area of potential applications. Cloud Networks are classified into private cloud Networks, public cloud Networks and hybrid cloud Networks. Anomaly detection systems are defined as a branch of intrusion detection systems that deal with identifying anomalous events with respect to normal system behavior. A novel application of a Self-Organizing-Feature Map (SOFM) of reduced/aggregate sets of ordered vector structured features that are used for detecting anomalies in the context of secure cloud environments is herein proposed. Multivalue inputs consist of reduced/aggregate ordered sets of vector and binary features. The nodes of the SOFM - after training - are indicative of local distributions of feature measurements during normal cloud operation. Anomalies are detected as outliers of the trained SOFM. Each structured vector consists of binary as well as histogram data. The aggregated Canberra distance is used to order histogram data whereas the Jaccard distance is used for multivalue binary data. The so-called Cross-Order Distance Matrix is defined for both cases. The distance depends upon the selection of a similarity/distance measure and a method for operating upon the elements of the Cross-Order Distance Matrix. Several methods of estimating the distance between two ordered sets of features are investigated in the course of this paper.
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anomaly detection in secure cloud environments using a self organizing feature map sofm model for clustering sets of r ordered vector structured features
International Conference on Engineering Applications of Neural Networks, 2015Co-Authors: Ioannis M. Stephanakis, Ioannis P. Chochliouros, Evangelos Sfakianakis, Noorulhassan ShiraziAbstract:Cloud computing delivers services over virtualized Networks to many end-users. Cloud services are characterized by such attributes as on-demand self-service, Broad Network Access, resource pooling, rapid and elastic resource provisioning and metered services of various qualities. Cloud Networks provide data as well as multimedia and video services. Cloud computing for critical structure IT is a relative new area of potential applications. Cloud Networks are classified into private cloud Networks, public cloud Networks and hybrid cloud Networks. Anomaly detection systems are defined as a branch of intrusion detection systems that deal with identifying anomalous events with respect to normal system behavior. A novel application of a Self-Organizing-Feature Map (SOFM) of reduced/aggregate sets of ordered vector structured features that are used for detecting anomalies in the context of secure cloud environments is herein proposed. Multivalue inputs consist of reduced/aggregate ordered sets of vector and binary features. The nodes of the SOFM - after training - are indicative of local distributions of feature measurements during normal cloud operation. Anomalies are detected as outliers of the trained SOFM. Each structured vector consists of binary as well as histogram data. The aggregated Canberra distance is used to order histogram data whereas the Jaccard distance is used for multivalue binary data. The so-called Cross-Order Distance Matrix is defined for both cases. The distance depends upon the selection of a similarity/distance measure and a method for operating upon the elements of the Cross-Order Distance Matrix. Several methods of estimating the distance between two ordered sets of features are investigated in the course of this paper.
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EANN (2) - A Particle Swarm Optimization (PSO) Model for Scheduling Nonlinear Multimedia Services in Multicommodity Fat-Tree Cloud Networks
Engineering Applications of Neural Networks, 2013Co-Authors: Ioannis M. Stephanakis, Ioannis P. Chochliouros, George Caridakis, Stefanos KolliasAbstract:Cloud computing delivers computing services over virtualized Networks to many end-users. Virtualized Networks are characterized by such attributes as on-demand self-service, Broad Network Access, resource pooling, rapid and elastic resource provisioning and metered services at various qualities. Cloud Networks provide data as well as multimedia and video services. They are classified into private cloud Networks, public cloud Networks and hybrid cloud Networks. Linear video services include Broadcasting and in-stream video that may be viewed in a video player whereas non-linear video services include a combination of in-stream video with on-demand services, which are originated from distributed servers in the Network and deliver interactive and pay-per view content. Furthermore heterogeneous delivery Networks that include fixed and mobile internet infrastructures require that adaptive video streaming should be carried out at Network boundaries based on such protocols as HTTP Live Streaming (HLS). Distributed processing of nonlinear video services in cloud environments is addressed in the present work by defining Distributed Acyclic Graphs (DAG) models for multimedia processes executed by a set of non-locally confined virtual machines. A novel discrete multivalue Particle Swarm Optimization (PSO) algorithm is proposed in order to optimize task scheduling and workflow. Numerical simulations regarding such measures as Schedule-Length-Ratio (SLR) and Speedup are given for novel fat-tree cloud architectures.
