Biblio

PCIe (Peripheral Component Interconnect express) protocol is the de facto protocol to bridge CPU and peripheral devices like GPU, NIC, and SSD drive. There is an increasing demand to install more peripheral devices on a single machine, but the PCIe interfaces offered by Intel CPUs are fixed. To resolve such contention, PCIe switch, PCH (Platform Controller Hub), or virtualization cards are installed on the machine to allow multiple devices to share a PCIe interface. Congestion happens when the collective PCIe traffic from the devices overwhelm the PCIe link capacity, and transmission delay is then introduced.In this work, we found the PCIe delay not only harms device performance but also leaks sensitive information about a user who uses the machine. In particular, as user’s activities might trigger data movement over PCIe (e.g., between CPU and GPU), by measuring PCIe congestion, an adversary accessing another device can infer the victim’s secret indirectly. Therefore, the delay resulted from I/O congestion can be exploited as a side-channel. We demonstrate the threat from PCIe congestion through 2 attack scenarios and 4 victim settings. Specifically, an attacker can learn the workload of a GPU in a remote server by probing a RDMA NIC that shares the same PCIe switch and measuring the delays. Based on the measurement, the attacker is able to know the keystroke timings of the victim, what webpage is rendered on the GPU, and what machine-learning model is running on the GPU. Besides, when the victim is using a low-speed device, e.g., an Ethernet NIC, an attacker controlling an NVMe SSD can launch a similar attack when they share a PCH or virtualization card. The evaluation result shows our attack can achieve high accuracy (e.g., 96.31% accuracy in inferring webpage visited by a victim).

With the development of cloud computing, remote attestation of virtual machines has received extensive attention. However, the current schemes mainly concentrate on the single prover, and the attestation of a large-scale virtualization environment will cause TPM bottleneck and network congestion, resulting in low efficiency of attestation. This paper proposes CloudTA, an extensible remote attestation architecture. CloudTA groups all virtual machines on each cloud server and introduces an integrity measurement group (IMG) to measure virtual machines and generate trusted evidence by a group. Subsequently, the cloud server reports the physical platform and VM group's trusted evidence for group verification, reducing latency and improving efficiency. Besides, CloudTA designs a hybrid high concurrency communication framework for supporting remote attestation of large-scale virtual machines by combining active requests and periodic reports. The evaluation results suggest that CloudTA has good efficiency and scalability and can support remote attestation of ten thousand virtual machines.

With the rapid development of cloud computing, IaaS (Infrastructure as a Service) becomes more and more popular. IaaS customers may not clearly know the actual performance of each cloud platform. Moreover, there are no unified standards in performance evaluation of IaaS VMs (virtual machine). The underlying virtualization technology of IaaS cloud is transparent to customers. In this paper, we will design an automated performance benchmarking platform which can automatically install, configure and execute each benchmarking tool with a configuration center. This platform can easily visualize multidimensional benchmarking parameters data of each IaaS cloud platform. We also rented four IaaS VMs from AliCloud-Beijing, AliCloud-Qingdao, UCloud and Huawei to validate our benchmarking system. Performance comparisons of multiple parameters between multiple platforms were shown in this paper. However, in practice, customers' applications running on VMs are often complex. Performance of complex applications may not depend on single benchmarking parameter (e.g. CPU, memory, disk I/O etc.). We ran a TPC-C test for example to get overall performance in MySQL application scenario. The effects of different benchmarking parameters differ in this specific scenario.

Network function virtualization (NFV) simplies the coniguration and management of security services by migrating the network security functions from dedicated hardware devices to software middle-boxes that run on commodity servers. Under the paradigm of NFV, the service function chain (SFC) consisting of a series of ordered virtual network security functions is becoming a mainstream form to carry network security services. Allocating the underlying physical network resources to the demands of SFCs under given constraints over time is known as the SFC deployment problem. It is a crucial issue for infrastructure providers. However, SFC deployment is facing new challenges in trading off between pursuing the objective of a high revenue-to-cost ratio and making decisions in an online manner. In this paper, we investigate the use of reinforcement learning to guide online deployment decisions for SFC requests and propose a Policy network Assisted Monte Carlo Tree search approach named PACT to address the above challenge, aiming to maximize the average revenue-to-cost ratio. PACT combines the strengths of the policy network, which evaluates the placement potential of physical servers, and the Monte Carlo Tree Search, which is able to tackle problems with large state spaces. Extensive experimental results demonstrate that our PACT achieves the best performance and is superior to other algorithms by up to 30% and 23.8% on average revenue-to-cost ratio and acceptance rate, respectively.

Mobile cloud computing has suggested as a viable technology as a result of the fast growth of mobile applications and the emergence of the cloud computing idea. Mobile cloud computing incorporates cloud computing into the mobile environment and addresses challenges in mobile cloud computing applications like (processing capacity, battery storage capacity, privacy, and security). We discuss the enabling technologies and obstacles that we will face when we transition from mobile computing to mobile cloud computing to develop next-generation mobile cloud applications. This paper provides an overview of the processes and open concerns for mobility in mobile cloud computing for augmented reality service provisioning. This paper outlines the concept, system architecture, and taxonomy of virtualization technology, as well as research concerns related to virtualization security, and suggests future study fields. Furthermore, we highlight open challenges to provide light on the future of mobile cloud computing and future development.

The ongoing trend of moving data and computation to the cloud is met with concerns regarding privacy and protection of intellectual property. Cloud Service Providers (CSP) must be fully trusted to not tamper with or disclose processed data, hampering adoption of cloud services for many sensitive or critical applications. As a result, CSPs and CPU manufacturers are rushing to find solutions for secure and trustworthy outsourced computation in the Cloud. While enclaves, like Intel SGX, are strongly limited in terms of throughput and size, AMD’s Secure Encrypted Virtualization (SEV) offers hardware support for transparently protecting code and data of entire VMs, thus removing the performance, memory and software adaption barriers of enclaves. Through attestation of boot code integrity and means for securely transferring secrets into an encrypted VM, CSPs are effectively removed from the list of trusted entities. There have been several attacks on the security of SEV, by abusing I/O channels to encrypt and decrypt data, or by moving encrypted code blocks at runtime. Yet, none of these attacks have targeted the attestation protocol, the core of the secure computing environment created by SEV. We show that the current attestation mechanism of Zen 1 and Zen 2 architectures has a significant flaw, allowing us to manipulate the loaded code without affecting the attestation outcome. An attacker may abuse this weakness to inject arbitrary code at startup–and thus take control over the entire VM execution, without any indication to the VM’s owner. Our attack primitives allow the attacker to do extensive modifications to the bootloader and the operating system, like injecting spy code or extracting secret data. We present a full end-to-end attack, from the initial exploit to leaking the key of the encrypted disk image during boot, giving the attacker unthrottled access to all of the VM’s persistent data.

Modern enterprises increasingly take advantage of cloud infrastructures. Yet, outsourcing code and data into the cloud requires enterprises to trust cloud providers not to meddle with their data. To reduce the level of trust towards cloud providers, AMD has introduced Secure Encrypted Virtualization (SEV). By encrypting Virtual Machines (VMs), SEV aims to ensure data confidentiality, despite a compromised or curious Hypervisor. The SEV Encrypted State (SEV-ES) extension additionally protects the VM’s register state from unauthorized access. Yet, both extensions do not provide integrity of the VM’s memory, which has already been abused to leak the protected data or to alter the VM’s control-flow. In this paper, we introduce the SEVerity attack; a missing puzzle piece in the series of attacks against the AMD SEV family. Specifically, we abuse the system’s lack of memory integrity protection to inject and execute arbitrary code within SEV-ES-protected VMs. Contrary to previous code execution attacks against the AMD SEV family, SEVerity neither relies on a specific CPU version nor on any code gadgets inside the VM. Instead, SEVerity abuses the fact that SEV-ES prohibits direct memory access into the encrypted memory. Specifically, SEVerity injects arbitrary code into the encrypted VM through I/O channels and uses the Hypervisor to locate and trigger the execution of the encrypted payload. This allows us to sidestep the protection mechanisms of SEV-ES. Overall, our results demonstrate a success rate of 100% and hence highlight that memory integrity protection is an obligation when encrypting VMs. Consequently, our work presents the final stroke in a series of attacks against AMD SEV and SEV-ES and renders the present implementation as incapable of protecting against a curious, vulnerable, or malicious Hypervisor.

Commodity hypervisors are widely deployed to support virtual machines (VMs) on multiprocessor hardware. Their growing complexity poses a security risk. To enable formal verification over such a large codebase, we introduce microverification, a new approach that decomposes a commodity hypervisor into a small core and a set of untrusted services so that we can prove security properties of the entire hypervisor by verifying the core alone. To verify the multiprocessor hypervisor core, we introduce security-preserving layers to modularize the proof without hiding information leakage so we can prove each layer of the implementation refines its specification, and the top layer specification is refined by all layers of the core implementation. To verify commodity hypervisor features that require dynamically changing information flow, we introduce data oracles to mask intentional information flow. We can then prove noninterference at the top layer specification and guarantee the resulting security properties hold for the entire hypervisor implementation. Using microverification, we retrofitted the Linux KVM hypervisor with only modest modifications to its codebase. Using Coq, we proved that the hypervisor protects the confidentiality and integrity of VM data, while retaining KVM’s functionality and performance. Our work is the first machine-checked security proof for a commodity multiprocessor hypervisor.

Cloud computing systems (CCSs) enable the sharing of physical computing resources through virtualisation, where a group of virtual machines (VMs) can share the same physical resources of a given machine. However, this sharing can lead to a so-called side-channel attack (SCA), widely recognised as a potential threat to CCSs. Specifically, malicious VMs can capture information from (target) VMs, i.e., those with sensitive information, by merely co-located with them on the same physical machine. As such, a VM allocation algorithm needs to be cognizant of this issue and attempts to allocate the malicious and target VMs onto different machines, i.e., the allocation algorithm needs to be security-aware. This paper investigates the allocation patterns of VM allocation algorithms that are more likely to lead to a secure allocation. A driving objective is to reduce the number of VM migrations during allocation. We also propose a graph-based secure VMs allocation algorithm (GbSRS) to minimise SCA threats. Our results show that algorithms following a stacking-based behaviour are more likely to produce secure VMs allocation than those following spreading or random behaviours.

To deal with the increasingly serious threat of industrial information malicious code, the simulations and characteristics of the domestic security and controllable operating system and office software were implemented in the virtual sandbox environment based on virtualization technology in this study. Firstly, the serialization detection scheme based on the convolution neural network algorithm was improved. Then, the API sequence was modeled and analyzed by the improved convolution neural network algorithm to excavate more local related information of variant sequences. Finally the variant detection of malicious code was realized. Results showed that this improved method had higher efficiency and accuracy for a large number of malicious code detection, and could be applied to the malicious code detection in security and controllable operating system.

The virtualization technology in cloud environment brings some data and privacy security issues to users. Aiming at the problems of virtual machines singleness, homogeneity and static state in cloud environment, a negative feedback dynamic scheduling algorithm is proposed. This algorithm is based on mimic defense and creates multiple virtual machines to complete user request services together through negative feedback control mechanism which can achieve real-time monitor of the running state of virtual machines. When virtual machines state is found to be inconsistent, this algorithm will dynamically change its execution environment, resulting in the attacker's information collection and vulnerability exploitation process being disrupting. Experiments show that the algorithm can better solve security threats caused by the singleness, homogeneity and static state of virtual machines in the cloud, and improve security and reliability of cloud users.

Internet of Medical Things (IoMT) are getting popular in the smart healthcare domain. These devices are resource-constrained and are vulnerable to attack. As the IoMTs are connected to the healthcare network infrastructure, it becomes the primary target of the adversary due to weak security and privacy measures. In this regard, this paper proposes a security architecture for smart healthcare network infrastructures. The architecture uses various security components or services that are developed and deployed as virtual network functions. This makes the security architecture ready for future network frameworks such as OpenMANO. Besides, in this security architecture, only authenticated and trusted IoMTs serve the patients along with an encryption-based communication protocol, thus creating a secure, privacy-preserving and trusted healthcare network infrastructure.

Internet of things (IoT) mainly depends on clouds to process and store their data. Clouds cannot handle the volume and velocity of data generated by IoT. IoT is delay-sensitive and resources limited. Fog computing proposed endorsing the internet of things (IoT) demands. Fog computing extends the cloud computing service to the edge of the network. Fog utilization reduces response time and network overhead while maintaining security aspects. isolation and operating system (OS) dependency achieved by using virtualization. Blockchain proposed to solve the security and privacy of fog computing. Blockchain is a decentralized, immutable ledger. fog computing with blockchain proposed as an IoT infrastructure. Fog computing adopted with lightweight blockchain in this proposed work. This adaptation endorses the IoT demands for low response time with limited resources. This paper explores system applicability. Varies from other papers that focus on one factor such as privacy or security-applicability of the proposed model achieved by concentration different IoT needs and limits. Response time and ram usage with 1000 transactions did not encroach 100s and 300MiB in the proposed model.

Legacy and novel network services are expected to be migrated and designed to be deployed in fully virtualized environments. Starting with 5G, NFV becomes a formally required brick in the specifications, for services integrated within the infrastructure provider networks. This evolution leads to deployment of virtual resources Virtual-Machine (VM)-based, container-based and/or server-less platforms, all calling for a deep virtualization of infrastructure components. Such a network softwarization also unleashes further logical network virtualization, easing multi-layered, multi-actor and multi-access services, so as to be able to fulfill high availability, security, privacy and resilience requirements. However, the derived increased components heterogeneity makes the detection and the characterization of anomalies difficult, hence the relationship between anomaly detection and corresponding reconfiguration of the NFV stack to mitigate anomalies. In this article we propose an unsupervised machine-learning data-driven approach based on Long-Short- Term-Memory (LSTM) autoencoders to detect and characterize anomalies in virtualized networking services. With a radiography visualization, this approach can spot and describe deviations from nominal parameter values of any virtualized network service by means of a lightweight and iterative mean-squared reconstruction error analysis of LSTM-based autoencoders. We implement and validate the proposed methodology through experimental tests on a vIMS proof-of-concept deployed using Kubernetes.

The following topics are dealt with: cloud computing; software defined networking; cryptography; telecommunication traffic; Internet of Things; authorisation; software radio; cryptocurrencies; data privacy; learning (artificial intelligence).

With the widespread of Artificial Intelligence (AI)-enabled security applications, there is a need for collecting heterogeneous and scalable data sources for effectively evaluating the performances of security applications. This paper presents the description of new datasets, named ToNİoT datasets that include distributed data sources collected from Telemetry datasets of Internet of Things (IoT) services, Operating systems datasets of Windows and Linux, and datasets of Network traffic. The paper aims to describe the new testbed architecture used to collect Linux datasets from audit traces of hard disk, memory and process. The architecture was designed in three distributed layers of edge, fog, and cloud. The edge layer comprises IoT and network systems, the fog layer includes virtual machines and gateways, and the cloud layer includes data analytics and visualization tools connected with the other two layers. The layers were programmatically controlled using Software-Defined Network (SDN) and Network-Function Virtualization (NFV) using the VMware NSX and vCloud NFV platform. The Linux ToNİoT datasets would be used to train and validate various new federated and distributed AI-enabled security solutions such as intrusion detection, threat intelligence, privacy preservation and digital forensics. Various Data analytical and machine learning methods are employed to determine the fidelity of the datasets in terms of examining feature engineering, statistics of legitimate and security events, and reliability of security events. The datasets can be publicly accessed from [1].

The concept of federation in 5G and NFV networks aims to provide orchestration of services across multiple administrative domains. Edge robotics, as a field of robotics, implements the robot control on the network edge by relying on low-latency and reliable access connectivity. In this paper, we propose a solution that enables Edge robotics service to expand its service footprint or access coverage over multiple administrative domains. We propose application of Distributed ledger technologies (DLTs) for the federation procedures to enable private, secure and trusty interactions between undisclosed administrative domains. The solution is applied on a real-case Edge robotics experimental scenario. The results show that it takes around 19 seconds to deploy & federate a Edge robotics service in an external/anonymous domain without any service down-time.

An Internet of Things (IoT) architecture comprised of cloud, fog and resource constrained IoT end devices. The exponential development of IoT has increased the processing and footprint overhead in IoT end devices. All the components of IoT end devices that establish Chain of Trust (CoT) to ensure security are termed as Trusted Computing Base (TCB). The increased overhead in the IoT end device has increased the demand to increase the size of TCB surface area hence increases complexity of TCB surface area and also the increased the visibility of TCB surface area to the external world made the IoT end devices architecture over-architectured and unsecured. The TCB surface area minimization that has been remained unfocused reduces the complexity of TCB surface area and visibility of TCB components to the external un-trusted world hence ensures security in terms of confidentiality, integrity, authenticity (CIA) at the IoT end devices. The TCB minimization thus will convert the over-architectured IoT end device into lightweight and secured architecture highly desired for resource constrained IoT end devices. In this paper we review the IoT end device architectures proposed in the recent past and concluded that these architectures of resource constrained IoT end devices are over-architectured due to larger TCB and ignored bugs and vulnerabilities in TCB hence un-secured. We propose the Novel levelled architecture with TCB minimization by replacing oversized hypervisor with lightweight Micro(μ)-hypervisor i.e. μ-visor and transferring μ-hypervisor based virtualization over fog node for light weight and secured IoT End device architecture. The bug free TCB components confirm stable CoT for guaranteed CIA resulting into robust Trusted Execution Environment (TEE) hence secured IoT end device architecture. Thus the proposed resulting architecture is secured with minimized SRAM and flash memory combined footprint 39.05% of the total available memory per device. In this paper we review the IoT end device architectures proposed in the recent past and concluded that these architectures of resource constrained IoT end devices are over-architectured due to larger TCB and ignored bugs and vulnerabilities in TCB hence un-secured. We propose the Novel levelled architecture with TCB minimization by replacing oversized hypervisor with lightweight Micro(μ)-hypervisor i.e. μ-visor and transferring μ-hypervisor based virtualization over fog node for light weight and secured IoT End device architecture. The bug free TCB components confirm stable CoT for guaranteed CIA resulting into robust Trusted Execution Environment (TEE) hence secured IoT end device architecture. Thus the proposed resulting architecture is secured with minimized SRAM and flash memory combined footprint 39.05% of the total available memory per device.

Digital connectivity is fundamental to the health care system to deliver safe and effective care. However, insecure connectivity could be a major threat to patient safety and privacy (e.g., in August 2017, FDA recalled 465,000 pacemakers because of discovering security flaws). Although connecting a patient's pacemaker to the Internet has many advantages for monitoring the patient, this connectivity opens a new door for cyber-attackers to steal the patient data or even control the pacemaker or damage it. Therefore, patients are forced to choose between connectivity and security. This paper presents a framework for secure and private communications between wearable medical devices and patient monitoring systems. The primary objective of this research is twofold, first to identify and analyze the communication vulnerabilities, second, to develop a framework for combating unauthorized access to data through the compromising of computer security. Specifically, hiding targets from cyber-attackers could prevent our system from future cyber-attacks. This is the most effective way to stop cyber-attacks in their first step.

Existing cyber security solutions have been basically developed using knowledge-based models that often cannot trigger new cyber-attack families. With the boom of Artificial Intelligence (AI), especially Deep Learning (DL) algorithms, those security solutions have been plugged-in with AI models to discover, trace, mitigate or respond to incidents of new security events. The algorithms demand a large number of heterogeneous data sources to train and validate new security systems. This paper presents the description of new datasets, the so-called ToNİoT, which involve federated data sources collected from Telemetry datasets of IoT services, Operating system datasets of Windows and Linux, and datasets of Network traffic. The paper introduces the testbed and description of TONİoT datasets for Windows operating systems. The testbed was implemented in three layers: edge, fog and cloud. The edge layer involves IoT and network devices, the fog layer contains virtual machines and gateways, and the cloud layer involves cloud services, such as data analytics, linked to the other two layers. These layers were dynamically managed using the platforms of software-Defined Network (SDN) and Network-Function Virtualization (NFV) using the VMware NSX and vCloud NFV platform. The Windows datasets were collected from audit traces of memories, processors, networks, processes and hard disks. The datasets would be used to evaluate various AI-based cyber security solutions, including intrusion detection, threat intelligence and hunting, privacy preservation and digital forensics. This is because the datasets have a wide range of recent normal and attack features and observations, as well as authentic ground truth events. The datasets can be publicly accessed from this link [1].

Internet of Things (IoT) is a key emerging technology which aims to connect objects over the internet. Software Defined Networking (SDN) is another new intelligent technology within networking domain which increases the network performance and provides better security, reliability, and privacy using dynamic software programs. In this paper, we have proposed a distributed secure Black SDN-IoT architecture with NFV implementation for smart cities. We have incorporated Black SDN that is highly secured SDN which gives better result in network performances, security, and privacy and secures both metadata and payload within each layer. This architecture also tried to introduce an approach which is more effective for building a cluster by means of Black SDN. Black SDN-loT with NFV concept brings benefits to the related fields in terms of energy savings and load balancing. Moreover, Multiple distributed controller have proposed to improve availability, integrity, privacy, confidentiality and etc. In the proposed architecture, the Black network provides higher security of each network layer comparative to the conventional network. Finally, this paper has discussed the architectural design of distributed secure Black SDN-IoT with NFV for smart cities and research challenges.

The difficult of detecting, response, tracing the malicious behavior in cloud has brought great challenges to the law enforcement in combating cybercrimes. This paper presents a malicious behavior oriented framework of detection, emergency response, traceability, and digital forensics in cloud environment. A cloud-based malicious behavior detection mechanism based on SDN is constructed, which implements full-traffic flow detection technology and malicious virtual machine detection based on memory analysis. The emergency response and traceability module can clarify the types of the malicious behavior and the impacts of the events, and locate the source of the event. The key nodes and paths of the infection topology or propagation path of the malicious behavior will be located security measure will be dispatched timely. The proposed IaaS service based forensics module realized the virtualization facility memory evidence extraction and analysis techniques, which can solve volatile data loss problems that often happened in traditional forensic methods.

Network Function Virtualization (NFV) is a recent concept where virtualization enables the shift from network functions (e.g., routers, switches, load-balancers, proxies) on specialized hardware appliances to software images running on all-purpose, high-volume servers. The resource allocation problem in the NFV environment has received considerable attention in the past years. However, little attention was paid to the security aspects of the problem in spite of the increasing number of vulnerabilities faced by cloud-based applications. Securing the services is an urgent need to completely benefit from the advantages offered by NFV. In this paper, we show how a network service request, composed of a set of service function chains (SFC) should be modified and enriched to take into consideration the security requirements of the supported service. We examine the well-known security best practices and propose a two-step algorithm that extends the initial SFC requests to a more complex chaining model that includes the security requirements of the service.

Recently, data protection has become increasingly important in cloud environments. The cloud platform has global user information, rich storage resource allocation information, and a fuller understanding of data attributes. At the same time, there is an urgent need for data access control to provide data security, and software-defined network, as a ready-made facility, has a global network view, global network management capabilities, and programable network rules. In this paper, we present an approach, named High-Performance Software-Defined Data Access Network (HP-SDDAN), providing software-defined data access network architecture, global data attribute management and attribute-based data access network. HP-SDDAN combines the excellent features of cloud platform and software-defined network, and fully considers the performance to implement software-defined data access network. In evaluation, we verify the effectiveness and efficiency of HP-SDDAN implementation, with only 1.46% overhead to achieve attribute-based data access control of attribute-based differential privacy.

Industrial control systems (ICS) are becoming more integral to modern life as they are being integrated into critical infrastructure. These systems typically lack application layer encryption and the placement of common network intrusion services have large blind spots. We propose the novel architecture, Cloud Based Intrusion Detection and Prevention System (CB-IDPS), to detect and prevent threats in ICS networks by using software defined networking (SDN) to route traffic to the cloud for inspection using network function virtualization (NFV) and service function chaining. CB-IDPS uses Amazon Web Services to create a virtual private cloud for packet inspection. The CB-IDPS framework is designed with considerations to the ICS delay constraints, dynamic traffic routing, scalability, resilience, and visibility. CB-IDPS is presented in the context of a micro grid energy management system as the test case to prove that the latency of CB-IDPS is within acceptable delay thresholds. The implementation of CB-IDPS uses the OpenDaylight software for the SDN controller and commonly used network security tools such as Zeek and Snort. To our knowledge, this is the first attempt at using NFV in an ICS context for network security.