Biblio

Due to a rapid revaluation in a virtualization environment, Virtual Machines (VMs) are target point for an attacker to gain privileged access of the virtual infrastructure. The Advanced Persistent Threats (APTs) such as malware, rootkit, spyware, etc. are more potent to bypass the existing defense mechanisms designed for VM. To address this issue, Virtual Machine Introspection (VMI) emerged as a promising approach that monitors run state of the VM externally from hypervisor. However, limitation of VMI lies with semantic gap. An open source tool called LibVMI address the semantic gap. Memory Forensic Analysis (MFA) tool such as Volatility can also be used to address the semantic gap. But, it needs to capture a memory dump (RAM) as input. Memory dump acquires time and its analysis time is highly crucial if Intrusion Detection System IDS (IDS) depends on the data supplied by FAM or VMI tool. In this work, live virtual machine RAM dump acquire time of LibVMI is measured. In addition, captured memory dump analysis time consumed by Volatility is measured and compared with other memory analyzer such as Rekall. It is observed through experimental results that, Rekall takes more execution time as compared to Volatility for most of the plugins. Further, Volatility and Rekall are compared with LibVMI. It is noticed that examining the volatile data through LibVMI is faster as it eliminates memory dump acquire time.

The term Cloud Computing is not something that appeared overnight, it may come from the time when computer system remotely accessed the applications and services. Cloud computing is Ubiquitous technology and receiving a huge attention in the scientific and industrial community. Cloud computing is ubiquitous, next generation's in-formation technology architecture which offers on-demand access to the network. It is dynamic, virtualized, scalable and pay per use model over internet. In a cloud computing environment, a cloud service provider offers “house of resources” includes applications, data, runtime, middleware, operating system, virtualization, servers, data storage and sharing and networking and tries to take up most of the overhead of client. Cloud computing offers lots of benefits, but the journey of the cloud is not very easy. It has several pitfalls along the road because most of the services are outsourced to third parties with added enough level of risk. Cloud computing is suffering from several issues and one of the most significant is Security, privacy, service availability, confidentiality, integrity, authentication, and compliance. Security is a shared responsibility of both client and service provider and we believe security must be information centric, adaptive, proactive and built in. Cloud computing and its security are emerging study area nowadays. In this paper, we are discussing about data security in cloud at the service provider end and proposing a network storage architecture of data which make sure availability, reliability, scalability and security.

Datacenter-based Cloud computing has induced new disruptive trends in networking, key among which is network virtualization. Software-Defined Networking overlays aim to improve the efficiency of the next generation multitenant datacenters. While early overlay prototypes are already available, they focus mainly on core functionality, with little being known yet about their impact on the system level performance. Using query completion time as our primary performance metric, we evaluate the overlay network impact on two representative datacenter workloads, Partition/Aggregate and 3-Tier. We measure how much performance is traded for overlay's benefits in manageability, security and policing. Finally, we aim to assist the datacenter architects by providing a detailed evaluation of the key overlay choices, all made possible by our accurate cross-layer hybrid/mesoscale simulation platform.
 

Cloud computing brings in a lot of advantages for enterprise IT infrastructure; virtualization technology, which is the backbone of cloud, provides easy consolidation of resources, reduction of cost, space and management efforts. However, security of critical and private data is a major concern which still keeps back a lot of customers from switching over from their traditional in-house IT infrastructure to a cloud service. Existence of techniques to physically locate a virtual machine in the cloud, proliferation of software vulnerability exploits and cross-channel attacks in-between virtual machines, all of these together increases the risk of business data leaks and privacy losses. This work proposes a framework to mitigate such risks and engineer customer trust towards enterprise cloud computing. Everyday new vulnerabilities are being discovered even in well-engineered software products and the hacking techniques are getting sophisticated over time. In this scenario, absolute guarantee of security in enterprise wide information processing system seems a remote possibility; software systems in the cloud are vulnerable to security attacks. Practical solution for the security problems lies in well-engineered attack mitigation plan. At the positive side, cloud computing has a collective infrastructure which can be effectively used to mitigate the attacks if an appropriate defense framework is in place. We propose such an attack mitigation framework for the cloud. Software vulnerabilities in the cloud have different severities and different impacts on the security parameters (confidentiality, integrity, and availability). By using Markov model, we continuously monitor and quantify the risk of compromise in different security parameters (e.g.: change in the potential to compromise the data confidentiality). Whenever, there is a significant change in risk, our framework would facilitate the tenants to calculate the Mean Time to Security Failure (MTTSF) cloud and allow them to adopt a dynamic mitigation plan. This framework is an add-on security layer in the cloud resource manager and it could improve the customer trust on enterprise cloud solutions.

Network virtualization sits firmly on the Internet evolutionary path allowing researchers to experiment with novel clean-slate designs over the production network and practitioners to manage multi-tenants infrastructures in a flexible and scalable manner. In such scenarios, isolation between virtual networks is often intended as purely logical: this is the case of address space isolation or flow space isolation. This approach neglects the effect that network virtualization has on resource allocation network-wide. In this work we investigate the price paid by a purely logical approach in terms of performance degradation. This performance loss is paid by the actual users of a multi-tenants datacenter network. We propose a solution to this problem leveraging on a new network virtualization primitive, namely an online link utilization feedback mechanism. It provides each tenant with the necessary information to make efficient use of network resources. We evaluate our solution trough a real implementation exploiting the OpenFlow protocol. Empirical results confirm that the proposed scheme is able to support tenants in exploiting virtualized network resources effectively.
 

It is expected that clean-slate network designs will be implemented for wide-area network applications. Multi-tenancy in OpenFlow networks is an effective method to supporting a clean-slate network design, because the cost-effectiveness is improved by the sharing of substrate networks. To guarantee the programmability of OpenFlow for tenants, a complete flow space (i.e., header values of the data packets) virtualization is necessary. Wide-area substrate networks typically have multiple administrators. We therefore need to implement a flow space virtualization over multiple administration networks. In existing techniques, a third party is solely responsible for managing the mapping of header values for flow space virtualization for substrate network administrators and tenants, despite the severity of a third party failure. In this paper, we propose an AutoVFlow mechanism that allows flow space virtualization in a wide-area networks without the need for a third party. Substrate network administrators implement a flow space virtualization autonomously. They are responsible for virtualizing a flow space involving switches in their own substrate networks. Using a prototype of AutoVFlow, we measured the virtualization overhead, the results of which show a negligible amount of overhead.
 

Automated server parameter tuning is crucial to performance and availability of Internet applications hosted in cloud environments. It is challenging due to high dynamics and burstiness of workloads, multi-tier service architecture, and virtualized server infrastructure. In this paper, we investigate automated and agile server parameter tuning for maximizing effective throughput of multi-tier Internet applications. A recent study proposed a reinforcement learning based server parameter tuning approach for minimizing average response time of multi-tier applications. Reinforcement learning is a decision making process determining the parameter tuning direction based on trial-and-error, instead of quantitative values for agile parameter tuning. It relies on a predefined adjustment value for each tuning action. However it is nontrivial or even infeasible to find an optimal value under highly dynamic and bursty workloads. We design a neural fuzzy control based approach that combines the strengths of fast online learning and self-adaptiveness of neural networks and fuzzy control. Due to the model independence, it is robust to highly dynamic and bursty workloads. It is agile in server parameter tuning due to its quantitative control outputs. We implemented the new approach on a testbed of virtualized data center hosting RUBiS and WikiBench benchmark applications. Experimental results demonstrate that the new approach significantly outperforms the reinforcement learning based approach for both improving effective system throughput and minimizing average response time.
 

Cloud computing is a new paradigm and emerged technology for hosting and delivering resources over a network such as internet by using concepts of virtualization, processing power and storage. However, many challenging issues are still unclear in cloud-based environments and decrease the rate of reliability and efficiency for service providers and users. User Authentication is one of the most challenging issues in cloud-based environments and according to this issue this paper proposes an efficient user authentication model that involves both of defined phases during registration and accessing processes. Geo Detection and Digital Signature Authorization (GD2SA) is a user authentication tool for provisional access permission in cloud computing environments. The main aim of GD2SA is to compare the location of an un-registered device with the location of the user by using his belonging devices (e.g. smart phone). In addition, this authentication algorithm uses the digital signature of account owner to verify the identity of applicant. This model has been evaluated in this paper according to three main parameters: efficiency, scalability, and security. In overall, the theoretical analysis of the proposed model showed that it can increase the rate of efficiency and reliability in cloud computing as an emerging technology.

As a new computing mode, cloud computing can provide users with virtualized and scalable web services, which faced with serious security challenges, however. Access control is one of the most important measures to ensure the security of cloud computing. But applying traditional access control model into the Cloud directly could not solve the uncertainty and vulnerability caused by the open conditions of cloud computing. In cloud computing environment, only when the security and reliability of both interaction parties are ensured, data security can be effectively guaranteed during interactions between users and the Cloud. Therefore, building a mutual trust relationship between users and cloud platform is the key to implement new kinds of access control method in cloud computing environment. Combining with Trust Management(TM), a mutual trust based access control (MTBAC) model is proposed in this paper. MTBAC model take both user's behavior trust and cloud services node's credibility into consideration. Trust relationships between users and cloud service nodes are established by mutual trust mechanism. Security problems of access control are solved by implementing MTBAC model into cloud computing environment. Simulation experiments show that MTBAC model can guarantee the interaction between users and cloud service nodes.

The term Cloud Computing is not something that appeared overnight, it may come from the time when computer system remotely accessed the applications and services. Cloud computing is Ubiquitous technology and receiving a huge attention in the scientific and industrial community. Cloud computing is ubiquitous, next generation's in-formation technology architecture which offers on-demand access to the network. It is dynamic, virtualized, scalable and pay per use model over internet. In a cloud computing environment, a cloud service provider offers “house of resources” includes applications, data, runtime, middleware, operating system, virtualization, servers, data storage and sharing and networking and tries to take up most of the overhead of client. Cloud computing offers lots of benefits, but the journey of the cloud is not very easy. It has several pitfalls along the road because most of the services are outsourced to third parties with added enough level of risk. Cloud computing is suffering from several issues and one of the most significant is Security, privacy, service availability, confidentiality, integrity, authentication, and compliance. Security is a shared responsibility of both client and service provider and we believe security must be information centric, adaptive, proactive and built in. Cloud computing and its security are emerging study area nowadays. In this paper, we are discussing about data security in cloud at the service provider end and proposing a network storage architecture of data which make sure availability, reliability, scalability and security.

In cloud data center, shared storage with good management is a main structure used for the storage of virtual machines (VM). In this paper, we proposed Hybrid VM storage (HVSTO), a privacy preserving shared storage system designed for the virtual machine storage in large-scale cloud data center. Unlike traditional shared storage, HVSTO adopts a distributed structure to preserve privacy of virtual machines, which are a threat in traditional centralized structure. To improve the performance of I/O latency in this distributed structure, we use a hybrid system to combine solid state disk and distributed storage. From the evaluation of our demonstration system, HVSTO provides a scalable and sufficient throughput for the platform as a service infrastructure.

This research focuses on hyper visor security from holistic perspective. It centers on hyper visor architecture - the organization of the various subsystems which collectively compromise a virtualization platform. It holds that the path to a secure hyper visor begins with a big-picture focus on architecture. Unfortunately, little research has been conducted with this perspective. This study investigates the impact of monolithic and micro kernel hyper visor architectures on the size and scope of the attack surface. Six architectural features are compared: management API, monitoring interface, hyper calls, interrupts, networking, and I/O. These subsystems are core hyper visor components which could be used as attack vectors. Specific examples and three leading hyper visor platforms are referenced (ESXi for monolithic architecture; Xen and Hyper-V for micro architecture). The results describe the relative strengths and vulnerabilities of both types of architectures. It is concluded that neither design is more secure, since both incorporate security tradeoffs in core processes.

We propose a novel phishing detection architecture based on transparent virtualization technologies and isolation of the own components. The architecture can be deployed as a security extension for virtual machines (VMs) running in the cloud. It uses fine-grained VM introspection (VMI) to extract, filter and scale a color-based fingerprint of web pages which are processed by a browser from the VM's memory. By analyzing the human perceptual similarity between the fingerprints, the architecture can reveal and mitigate phishing attacks which are based on redirection to spoofed web pages and it can also detect “Man-in-the-Browser” (MitB) attacks. To the best of our knowledge, the architecture is the first anti-phishing solution leveraging virtualization technologies. We explain details about the design and the implementation and we show results of an evaluation with real-world data.

Shared resources are an essential part of cloud computing. Virtualization and multi-tenancy provide a number of advantages for increasing resource utilization and for providing on demand elasticity. However, these cloud features also raise many security concerns related to cloud computing resources. In this paper, we propose an architecture and approach for leveraging the virtualization technology at the core of cloud computing to perform intrusion detection security using hypervisor performance metrics. Through the use of virtual machine performance metrics gathered from hypervisors, such as packets transmitted/received, block device read/write requests, and CPU utilization, we demonstrate and verify that suspicious activities can be profiled without detailed knowledge of the operating system running within the virtual machines. The proposed hypervisor-based cloud intrusion detection system does not require additional software installed in virtual machines and has many advantages compared to host-based and network based intrusion detection systems which can complement these traditional approaches to intrusion detection.