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Proof-of-Work (PoW) blockchain systems like Bitcoin and Ethereum are vulnerable to selfholding attack. The prior modeling-based works about this attack only considered Bitcoin and assumed that there were at most two honest pools in a perfect network (no natural fork in such networks). However, a blockchain network is imperfect due to block propagation delay, which can lead to forking. Moreover, there may be more than two pools under attack. This paper aims for a quantitative analysis of an imperfect PoW blockchain network system under selfholding attack. We develop a novel stochastic model and derive formulas to evaluate the effect of selfholding attack on miner revenue, system security and system performance. Our work can be used to analyze the scenario where there are any number of pools suffering selfholding attack in both Ethereum and Bitcoin. The model in this paper can capture the behaviors of a more realistic and more general scenario, compared with the existing models. Moreover, our model and formulas can also be applied to evaluate a blockchain system, which uses a similar reward mechanism and is vulnerable to selfholding attack. Our work can help design a more secure blockchain incentive mechanism and an in-pool reward mechanism.

Selfish mining, as a serious threat to blockchain, has been attracting attentions from academic and industry. Stochastic modeling has been explored to quantitatively investigate selfish mining in imperfect blockchain networks. However, prior modeling-based analysis approaches have some of the following issues: (1) only focus on Bitcoin or Ethereum, or (2) ignore extended forks and just consider natural forks, or (3) only compute the mining revenue without assessing the performance and security of the blockchain system when the system suffers from selfish mining. In this paper, we aim to address these issues. We build a Markov chain to make quantitative analysis of selfish mining in imperfect Bitcoin and Ethereum networks with natural and extended forks. Formulas are derived to calculate the mining revenue for the selfish pool (comprising selfish miners) and honest miners, respectively. Moreover, we derive the formulas of performance metrics (namely, transactions per second and stale block ratio) and the formula of security metric (namely, the probability of double-spending success) of the system. These quantitative results can help understand the impact of selfish mining on imperfect blockchain networks and then help the detection of selfish mining.

Connected vehicles (CVs) are getting increasing attention in intelligent transportation systems (ITSs). In terms of the stringent security and anonymity issues, a novel anonymous and decentralized payment platform is demanded. Bitcoin is regarded as a potential solution. But the existing Bitcoin and its various improvements lack a beforehand anti-fraud Bitcoin deposit scheme, which is necessary to prevent potential fraud risks from Bitcoin to CVs. This paper aims to build a novel anti-fraud deposit scheme as a seamless bridge between CV networks and the Bitcoin payment platform. We propose a Bitcoin-to-Connected-Vehicle deposit scheme (Bit2CV) with an outsourcing endorsement in order to build an anti-fraud deposit transaction ( dtr ). Firstly, Bit2CV leverages a special Bitcoin-opcode-OP_RETURN based method to record the dtr , the CV’s request, and the endorsement together on Blockchain ledger. This method can achieve security features, including non-repudiation, privacy-friendly endorsement, future audit, and anti-fraud. Meanwhile, Bit2CV is fully decentralized without any involvement of centralized authorities and fully compatible with the current Bitcoin network. We conduct the security analysis and also simulations for performance evaluation. In a typical scenario of our simulation, Bit2CV total time cost is less than 477.03 ms, and the size of the endorsement is 1,732 bytes, which is much less and smaller than transaction confirmation time and the average block size, respectively. These designs and results demonstrate that Bit2CV is feasible and practical.

Recently, researchers are being more interested in performing operations directly on the encrypted database with the help of the $O$ rder- $P$ reserving $E$ ncryption (OPE) scheme. This mechanism enables executing many types of queries efficiently, such as range query and comparison, since it can preserve the relative order of underlying plaintext on ciphertexts. However, traditional OPE schemes cannot achieve ideal security against IND-OCPA ( IND istinguishability under $O$ rdered $C$ hosen- $P$ laintext $A$ ttack) in a linear length of static encoding. Popa’s mutable scheme (namely, mOPE) is an effective solution to perform the range query in the database environment. In this paper, we propose a novel scheme, $B$ oundary $O$ rder- $P$ reserving $E$ ncryption (BOPE), to achieve high performance under ideal security. BOPE comprises two algorithms. One is a searching algorithm in which we propose a data structure, the boundary tree, to optimize the algorithm by cutting the scope of each iteration and reducing the rounds of interaction. The second algorithm is an updating algorithm for stale encoding, which determines whether to update the lookup table according to the type of each query, in order to avoid the time cost of redundant updates. We implemented and evaluated BOPE on a practical environment where we have achieved a performance increase of more than 10% from mOPE.

Bitcoin is being explored for applications in various Internet of Things (IoT) scenarios as a peer-to-peer payment platform. However, security and anonymity problems exist with Bitcoin, which threaten vulnerable IoT facilities. This paper aims to achieve conditional anonymity inside Bitcoin transactions. We first propose an identity-based conditionally anonymous signature (ICAS) algorithm and then design a lightweight Bitcoin script scheme (named pay-to-public-key-hash-with-conditional-anonymity or P2PKHCA), which applies the ICAS algorithm to make conditionally anonymous Bitcoin transactions. P2PKHCA allows the identity manager to trace the real identity of users while preserving users’ anonymity. Furthermore, P2PKHCA is backward compatible in terms of being able to work seamlessly with the existing Bitcoin script scheme pay-to-public-key-hash (P2PKH) in the Bitcoin network. We conduct a security analysis to verify the security features of P2PKHCA and employ a performance evaluation in terms of the cryptographic time and space costs by comparison with P2PKH. The simulation results demonstrate the effectiveness of P2PKHCA in reducing both time cost and data size.

Virtualization technology has promoted the fast development and deployment of cloud computing, and is now becoming an enabler of Internet of Everything. Virtual machine monitor (VMM), playing a critical role in a virtualized system, is software and hence it suffers from software aging after a long continuous running as well as software crashes due to elusive faults. Software rejuvenation techniques can be adopted to reduce the impact of software aging. Although there existed analytical model-based approaches for evaluating software rejuvenation techniques, none analyzed both application service (AS) availability and job completion time in a virtualized system with live virtual machine (VM) migration. This paper aims to quantitatively analyze software rejuvenation techniques from service provider and user views in a virtualized system deploying VMM reboot and live VM migration techniques for rejuvenation, under the condition that all the aging time, failure time, VMM fixing time and live VM migration time follow general distributions. We construct an analytical model by using a semi-Markov process (SMP) and derive formulas for calculating AS availability and job completion time. By analytical experiments, we can obtain the optimal migration trigger intervals for achieving the approximate maximum AS availability and the approximate minimum job completion time, and then service providers can make decisions for maximizing the benefits of service providers and users by adjusting parameter values.

Over the last few years, the malware propagation on PC platforms, especially on Windows OS has been even severe. For the purpose of resisting a large scale of malware variants, machine learning (ML) classifiers for malicious Portable Executable (PE) files have been proposed to achieve automated classification. Recently, function call graph (FCG) vectorization (FCGV) representation was explored as the input feature to achieve higher ML classification accuracy, but FCGV representation loses some critical features of PE files due to the hash technique. This paper aims to further improve the classification accuracy of FCGV-based ML model by applying both graph and non-graph features. We propose an FCGV-SF based Random Forest classification model, which applies both FCGV features (graph features) and statistical features (SF, non-graph features) extracted from disassembled PE files. Six types of effective non-graph features are chosen for our integrated vector, namely, metadata, symbol, operation code, register, section and data definition. We evaluate our model on a dataset provided by Microsoft hosted at Kaggle, and the experimental results indicate that the classification accuracy increases from 0.9851 to 0.9957 compared with the existing model based on FCGV only.

Electronic health (e-health) information system relies on cloud computing technologies to provide massive medical data computing and storage services. Especially, the recently proposed Machine Learning as a Service (MLaaS) on these medical data can not only effectively improve the healthcare service quality, but also support the end users with limited computing resources. However, MLaaS on the massive medical data faces the challenge of privacy. Homomorphic encryption technology has been explored to assure the privacy of medical data owners in MLaaS but with the weaknesses of limited homomorphic operations and low efficiency. To alleviate these weaknesses, this paper proposes a novel privacy-preserving non-collusion dualcloud (NCDC) model-based e-health information system using neural network (NN) computing. The system can not only assure medical data privacy through adopting homomorphic encryption technology but also assure NN model privacy by adding fake neurons to the NN. In addition, the proposed e-health information system also has the following advantages: (i) Simple key generation. (ii) No constraint on the size of medical data to be encrypted. (iii) The less loss of prediction accuracy between encrypted and original medical data. (iv) Supporting more homomorphic operations and having better computing efficiency through experiment verification.

SUMMARY & CONCLUSIONSA Multi-access Edge Computing (MEC) micro data center (MEDC) consists of multiple MEC hosts close to endpoint devices. MEC service is delivered by instantiating a virtualization system (e.g., Virtual Machines or Containers) on a MEC host. MEDC faces more new security risks due to various device connections in an open environment. When more and more IoT/CPS systems are connected to MEDC, it is necessary for MEC service providers to quantitatively analyze any security loss and then make defense-related decision. This paper develops a CTMC model for quantitatively analyzing the security and dependability of a vulnerable MEDC system under lateral movement attacks, from the adversary’s initial successful access until the MEDC becomes resistant to the attack. The proposed model captures the behavior of the system in a scenario where (i) the rate of vulnerable MEC servers being infected increases with the increasing number of infected MEC servers, (ii) each infected MEC server can perform its compromising activity independently and randomly, and (iii) any infected MEC may fail and then cannot provide service. We also introduce the formulas for computing metrics. The proposed model and formula are verified to be approximately accurate by comparing numerical results and simulation results.

Adversarial Malware Example (AME)-based adversarial training can effectively enhance the robustness of Machine Learning (ML)-based malware detectors against AME. AME quality is a key factor to the robustness enhancement. Generative Adversarial Network (GAN) is a kind of AME generation method, but the existing GAN-based AME generation methods have the issues of inadequate optimization, mode collapse and training instability. In this paper, we propose a novel approach (denote as LSGAN-AT) to enhance ML-based malware detector robustness against Adversarial Examples, which includes LSGAN module and AT module. LSGAN module can generate more effective and smoother AME by utilizing brand-new network structures and Least Square (LS) loss to optimize boundary samples. AT module makes adversarial training using AME generated by LSGAN to generate ML-based Robust Malware Detector (RMD). Extensive experiment results validate the better transferability of AME in terms of attacking 6 ML detectors and the RMD transferability in terms of resisting the MalGAN black-box attack. The results also verify the performance of the generated RMD in the recognition rate of AME.

Moving target defense (MTD), emerging as a game-changer in the cyber defense area, has got a lot of attention and development recently. As a proactive defense technique, MTD dynamically changes system attributes in order to create more uncertainties of the system and has been proved to be effective against cyber attacks. Beyond this, there is still a lack of researches with respect to the quantitative analysis of the effect of MTD on system performance. This paper aims to quantitatively investigate how MTD affects system performance while bringing security. We develop Markov process-based models for two different MTD strategies and derive the formulas for metrics of interest. We carry out simulation experiments to validate our proposed models with Mininet. Furthermore, numerical analysis is conducted for comparing these two different strategies in terms of system performance. The numerical results also show how different parameters affect the evaluation metrics. Our models can help defenders conFigure the MTD system in the most suitable way.

Deep learning (DL) has exhibited its exceptional performance in fields like intrusion detection. Various augmentation methods have been proposed to improve data quality and eventually to enhance the performance of DL models. However, the classic augmentation methods cannot be applied to those DL models which exploit the system-call sequences to detect intrusion. Previously, the seq2seq model has been explored to augment system-call sequences. Following this work, we propose a gated convolutional neural network (GCNN) model to thoroughly extract the potential information of augmented sequences. Also, in order to enhance the model’s robustness, we adopt adversarial training to reduce the impact of adversarial examples on the model. Adversarial examples used in adversarial training are generated by the proposed adversarial sequence generation algorithm. The experimental results on different verified models show that GCNN model can better obtain the potential information of the augmented data and achieve the best performance. Furthermore, GCNN with adversarial training can enhance robustness significantly.

Lateral movement attack performs malicious activities by infecting part of a network system first and then moving laterally to the left system in order to compromise more computers. It is widely used in various sophisticated attacks and plays a critical role. This paper aims to quantitatively analyze the transient security and dependability of a critical network system under lateral movement attacks, whose intruding capability increases with the increasing number of attacked computers. We propose a survivability model for capturing the system and adversary behaviors from the time instant of the first intrusion launched from any attacked computer to the other vulnerable computers until defense solution is developed and deployed. Stochastic Reward Nets (SRN) is applied to automatically build and solve the model. The formulas are also derived for calculating the metrics of interest. Simulation is carried out to validate the approximate accuracy of our model and formulas. The quantitative analysis can help network administrators make a trade-off between damage loss and defense cost.

Multi-Access Edge Computing (MEC), making computing resources extend to the edge continuously, plays a key role in 5G (Fifth Generation) networks. User Plane Function (UPF), as an important network function, can steer the traffic to the MEC applications and 5G core network in the integration of MEC and 5G architecture. Edge UPF (EUPF) runs in a virtual machine (VM) hosted by a virtual machine monitor (VMM) at the edge of the network. However, EUPF is likely to suffer more frequent network attacks than in the core network. The vulnerability of the virtualized environment itself also expands the attack surface for adversaries. Security protection of EUPF is urgently needed. Rejuvenation techniques play a key role in defense and service recovery. The existing researches focused on the deployment of EUPF and ignored the dependability analysis of EUPF service.

A NURBS-enhanced interpolating boundary element-free method (NE-IIBEFM) is proposed by coupling the meshfree method with the non-uniform rational B-spline (NURBS). The NURBS plays an essential role in computer aided design (CAD) whereby it can be used as the bridge between boundary type meshless method and CAD, which is convenient and is widely utilized in engineering. The NURBS basis functions used in CAD packages for geometry construction are applied to describe the boundary in the numerical procedure. Thus, the geometry of the curve can be reproduced exactly at all stages. The boundary integral cells and nodes are defined from the knot vector in NURBS, and both the open and closed curves are considered. The fields are approximated by an improved interpolating moving least square method, in which the obtained shape functions have the property of delta function. Thus, the boundary conditions can be imposed directly. A line integration method without domain discretization is further coupled with the proposed method for the treatment of domain integrals for problems with body forces.

Vehicle-to-everything (V2X) has been regarded as an important enabling technology for future intelligent transportation systems (ITS). Safety-critical applications in V2X require authentication of nodes and messages, and also need to preserve the privacy of vehicles. Pseudonymity plays an important role in V2X communication by satisfying both security and privacy requirements. In this paper, we analyze the weaknesses of a recent relevant work named Issue First Activate Later (IFAL), which is the leading European candidate for V2X. We then propose a novel lightweight, secure and privacy-preserving pseudonym changing scheme. Through the security analysis, we show that the proposed scheme achieves the asynchronous key agreement, and realizes both robust forward secrecy and backward secrecy while keeping security functionalities of IFAL. Furthermore, the performance analysis is conducted on Arduino Mega 2560 to illustrate that the proposed scheme is feasible and practical. © 2014 xxxxxxxx. Hosting by Elsevier B.V. All rights reserved.

Non-uniform rational B-spline (NURBS) in Isogeometric analysis (IGA) is coupled with the boundary element method (BEM) for 2D potential problems in this paper. The geometry and field are usually approximated by the same basis functions in IGA, such as the B-spline or the NURBS basis functions. In the proposed method, these two kinds of approximation are performed independently, i.e. the geometry is reproduced by the NURBS basis functions while the field is approximated by the traditional Lagrangian basis functions which are used in the conventional BEM. The proposed method has the advantage that the geometry can be reproduced exactly at all stages in IGA methods. Actually, one can use the computer aided design (CAD) software or NURBS library to perform the operations related to the geometry. The field approximation is performed in parameter space and separated from the geometry. Thus, it can be implemented easily as the conventional BEM since most algorithms for BEM can be applied directly, such as the methods for treatment of the singular integrals, and the boundary conditions can be imposed directly. Numerical examples have demonstrated the accuracy of the proposed method.

Content-centric networking (CCN) aims to improve network reliability, scalability, and security by changing the way that information is organized and retrieved in the current Internet. One critical issue in CCN is in-network cache allocation. It is known that in-network caching mechanisms are vulnerable to distributed denial of service attacks, especially to pollution attacks. That is, a caching mechanism under pollution attacks cannot work well. The past years witnessed kinds of proposals of cache allocation mechanisms. However, none of them could effectively allocate in-network cache while defending against attacks. In this paper, we propose a lightweight non-collaborative cache allocation approach (IFDD), which could not only enhance in-network caching performance in terms of the cache hit ratio and the request processing delay, but also defend against pollution attacks. By lightweight, we mean that IFDD generates low communication overhead (due to non-collaboration) and computational overhead at routers. The key idea behind IFDD is to combine the content popularity with the content locality in making caching decision. Extensive simulation results on ndnSIM platform demonstrate the capability of the proposed approach in improving cache allocation performance while reducing the impact of pollution attacks.

This paper aims to analyze transient security and dependability of a vulnerable critical system, under vulnerability-related attack and two reactive defense strategies, from a severe vulnerability announcement until the vulnerability is fully removed from the system. By severe, we mean that the vulnerability-based malware could cause significant damage to the infected system in terms of security and dependability while infecting more and more new vulnerable computer systems. We propose a Markov chain-based survivability model for capturing the vulnerable critical system behaviors during the vulnerability elimination process. A high-level formalism based on Stochastic Reward Nets is applied to automatically generate and solve the survivability model. Survivability metrics are defined to quantify system attributes. The proposed model and metrics not only enable us to quantitatively assess the system survivability in terms of security risk and dependability, but also provide insights on the system investment decision. Numerical experiments are constructed to study the impact of key parameters on system security, dependability and profit.

Healthcare information systems are the next big application area for Blockchain technology. However, straightforward extensions of existing digital cryptocurrency systems such as Bitcoin and Ethereum results in systems that are unsuitable for the challenges posed by healthcare systems. In this paper, we propose an architecture for a blockchain-based healthcare information system in which block validation is performed through collective signatures initiated by a designated leader and executed by a pool of witnesses. Furthermore, we describe a smart-contract based approach that allows data owners to explicitly grant or revoke authorizations for other actors to access healthcare data. All accesses, successful or not, are recorded on the blockchain as separate transactions, thus ensuring transparency and privacy protection.

Machine learning (ML) algorithms provide better performance than traditional algorithms in various applications. However, some unknown flaws in ML classifiers make them sensitive to adversarial examples generated by adding small but fooled purposeful distortions to natural examples. This paper aims to investigate the transferability of adversarial examples generated on a sparse and structured dataset and the ability of adversarial training in resisting adversarial examples. The results demonstrate that adversarial examples generated by DNN can fool a set of ML classifiers such as decision tree, random forest, SVM, CNN and RNN. Also, adversarial training can improve the robustness of DNN in terms of resisting attacks.

Bitcoin and Ethereum, the most famous blockchain-based cryptocurrencies, both use Proof-of-Work protocol to achieve consensus, which is vulnerable to selfish mining. The existing researches on selfish mining analysis focused on Bitcoin, or ignored the blockchain details, or only investigated mining revenue without considering system performance and security. This paper aims to quantitatively evaluate the influence of selfish mining in an imperfect blockchain network from the perspective of honest miners (system performance and security) and selfish miners (selfish mining revenue ratio). We develop a novel Markov model to capture the behaviors of honest and selfish miners in both Bitcoin and Ethereum. Our model can also capture natural forks (occurring due to block propagation delay) and the varying distance between uncle and nephew blocks in Ethereum. Formulas are derived to calculate mining revenue, system performance and security metrics. The proposed model and metric formulas are validated by (1) comparing our numerical with simulation results, and (2) comparing our numerical results with the existing work results. Numerical analysis is carried out to investigate selfish mining impact over diverse parameters. These quantitative results can help detect whether there are any selfish miners in the system, help design blockchain reward mechanisms and enhance security.

This letter considers the IaaS data center where tenant tasks are different in terms of the number of requested physical cores, leading to heterogeneous virtual machines (VMs). We develop a novel analytical model to evaluate the performance of heterogeneous VMs on the same physical machine by applying the Continuous Time Markov Chain.

The rapid and widespread adoption of Internet of Thing-related services drives the advancement of Fog Computing, which is proposed to extend the Cloud Computing paradigm to run geo-distributed applications throughout the network. Fog services could be provisioned to tenants by applying container-based virtualization or system virtualization technology. Within a container/VM (virtual machine), an application can be configured and run. This paper aims to evaluate the performance of Fog services under different physical resource types, such as physical core and memory. For each type of physical resources, the resource amount demanded from different tenants is different and follows a general distribution. These differences make container/VM heterogeneous. We develop a novel analytical model to evaluate the performance of such heterogeneous containers/VMs deployed on the same fog node (a physical device) by applying the Continuous Time Markov Chain. Numerical results obtained from the proposed analytic model are verified through discrete-event simulations. The developed model and computing formulas could help the decision on how to schedule latency-sensitive IoT requests.

Software vulnerability analysis plays a critical role in the prevention and mitigation of software security attacks, and vulnerability classification constitutes a key part of this analysis. This paper proposes a new approach for software vulnerability classification, which is based on vulnerability characteristics including accumulation of errors or resources consumption, strict timing requirement and complex interactions between environment and software. We also present seven attack patterns and explore the mapping between vulnerability types and attack patterns. The proposed methods are used to analyze the vulnerabilities and the corresponding attacks reported by Google Project Zero. Examples of applying our classification approach to specific vulnerabilities are presented, together with a statistical analysis of the occurrence of different types of vulnerabilities. These results allow us to have a better understanding of software vulnerabilities and how they can be exploited, leading in the future to strategies to better equip programmers to avoid introducing them, and also helping us to formulate effective countermeasures. We make three observations regarding software vulnerability classification: 1) Mandel vulnerabilities, especially NMVs (Non-Aging-related Mandel Vulnerabilities), account for the largest share of all classified vulnerabilities. 2) It takes more time and complex strategies to fix NMVs. 3) The major goal for attackers is to get elevation of privilege from a target system. The main cause of vulnerabilities is improper validation mechanisms.

Infrastructure as a Service cloud provides elasticity and scalable virtual machines VMs as computing service to multiple tenants, but the tenants lose the full control of their data. Measuring the integrity of critical files of the VMs and providing the integrity attestation to the tenants on the basis of TCG trusted computing techniques is an effective way to alleviate their anxiety. This paper considers how to measure the integrity of the processes run in guest VMs and files opened in guest VMs. We propose an out-of-the-box integrity measurement approach to measure the integrity of critical files through system call syscall interception without any modification of the guest VMs. Out-of-the-box integrity measurement approach can not only measure the integrity of all files that have been considered by existing approaches but also measure the integrity of the system configuration files, program loaders, and script interpreters, which affect the system behaviors and integrity. The ability of supporting both system and manual measurement policies makes our approach flexible. We implement this approach in Xen hypervisor with little modification of the existing syscall interception method, and this approach can be ported to other virtualization platform easily. Copyright © 2014 John Wiley & Sons, Ltd.

Cloud computing has been bringing fundamental changes to computing models in the past few years. Infrastructure as a Service (IaaS), a kind of basic cloud services, is provisioned to customers in the form of virtual machines (VMs). The increasing demands for IaaS cloud services require the performability analysis of cloud infrastructure. Analytic modeling is one of the effective evaluation approaches. This paper aims to develop a monolithic model, by using continuous time Markov chain (CTMC), for a IaaS CDC, which (1) consists of active and standby physical machines (PMs), (2) allows PM migration among active and standby PM pools, (3) all jobs are homogeneous, and (4) a running job could continue its running by using idle active PMs when the PM working for this job fails. Although a monolithic CTMC model for IaaS Cloud performability analysis may face largeness and stiffness problems, it could be used to verify the scalable approximate model. We present the details of state transition rules of the proposed model and the formula for computing metrics, including the immediate service probability, the mean response time and so on. Numerical analysis and simulations are carried out to verify the accuracy of the proposed model.

Computer systems are potentially targeted by cybercriminals by means of specially crafted malicious software called Advanced Persistent Threats (APTs). As a consequence, any security attribute of the computer system may be compromised: disruption of service (availability), unauthorized data modification (integrity), or exfiltration of sensitive data (confidentiality). An APT starts with the exploitation of software vulnerability within the system. Thus, vulnerability mitigation strategies must be designed and deployed in a timely manner to reduce the window of exposure of vulnerable systems. In this paper, we evaluate the survivability of a computer system under an APT attack using a Markov model. Generation and solution of the Markov model are facilitated by means of a high-level formalism based on stochastic Petri nets. Survivability metrics are defined to quantify security attributes of the system from the public announcement of a software vulnerability and during the system recovery. The proposed model and metrics not only enable us to quantitatively assess the system survivability in terms of security attributes but also provide insights on the cost/revenue trade-offs of investment efforts in system recovery such as vulnerability mitigation strategies. Sensitivity analysis through numerical experiments is carried out to study the impact of key parameters on system secure survivability.

Role-based access control (RBAC) is being today's dominant access control model due to its potential to mitigate the complexity and cost of access control administration. However, the migration from the access control lists (ACL) to RBAC for a large administration system may consume significant efforts, which challenges the adoption of RBAC. Role mining algorithms can significantly reduce the migration cost by providing a partially automatic construction of an RBAC policy. This paper explores Artificial Intelligence (AI) techniques in designing role mining algorithms, which can optimize policy quality in terms of policy size, user-attribute-based interpretability of the roles, and the combination of size and interpretability. We propose two algorithms, genetic algorithm (GA)-based and ant colony optimization (ACO)-based. GA-based algorithm works by starting with a set of all candidate roles and repeatedly removing roles. ACO-based algorithm works by starting with an empty policy and repeatedly adding candidate roles. We carry out extensive experiments with publicly available access control policies. The simulation results indicate that ®the proposed algorithms achieves better performance than the corresponding existing algorithms. (2) GA-based approach produces better results than ACO-based approach.□

This paper aims to design efficient and effective approaches to virtual network embedding (VNE) problem, which deals with the embedding of a requested virtual network (VN) in an underlying physical (substrate network) infrastructure. When the node and link constraints (including CPU, memory, network bandwidth, and network delay) are both taken into account, the VN embedding problem is NP-hard, even in the offline case. The capabilities of some Artificial Intelligence (AI) techniques have been validated in handling the VN problem. In this paper, we propose two efficient and effective VNE algorithms based on differential evolutionary (DE) technique. The extensive simulation results show that DE technique performs some orders of magnitude faster than GA and PSO-based VNE algorithms in achieving the comparable long-term revenue of Infrastructure providers.

The virtual network embedding (VNE) problem deals with the embedding of virtual network (VN) requests in an underlying physical (substrate network) infrastructure. When both the node and link constraints are considered, the VN embedding problem is NP-hard, even in the offline case. The genetic algorithm (GA) is an excellent approach to solving complex problems in optimization with difficult constraints. This paper explores applying GA to handle the VNE problem. We propose two GA-based VNE algorithms and evaluate them by comparing with the existing state-of-the-art VNE algorithms, including PSO-based VNE approaches. Extensive simulation results validate the capability of the proposed GA-based VNE algorithms in terms of the InP long-term revenue and the VN embedding cost.

Integrating system-level virtualization technology with Trusted Computing technology can significantly improve system security. The open-source virtual TPM facility, shipped with the open-source hyper visor Xen, aims to provide the illusion of a physical TPM to TPM-based trusted software executed in trusted virtual machines (TVMs) such that TPM-based trusted software works well in a TVM as in a native Operating System (OS). However, it is not true for the trusted software which works in a trusted para-virtual machine (TPVM). The TPM command packets sent in the TPVM OS boot phase may cause the TPVM OS boot failure. This paper design and implement E2VT, an effective and efficient mechanism for preventing the TPVM OS boot failure while (1) being transparent to the TPVM system, (2) maintaining the original system performance, (3) making minimal modifications to the existing architecture. We validate our analysis and design through experiments.

The virtual network embedding (VNE) problem has been receiving significant research interest lately. A realistic scenario might entail embedding multiple VN requests (MVNE) that arrive simultaneously (batch arrivals). The existing MVNE approaches either did not consider the coordination among multiple VNR embeddings or embedded all the arriving VNRs simultaneously without considering the substrate network state. This paper considers the MVNE problem in the scenarios where the available physical resources may not be sufficient to satisfy the physical resource demands of all the VNRs in the batch. We formulate the problem as mixed integer programs, which not only provide optimal MVN embedding but also improve the network service provider's long-term revenue. Simulation results validate the revenue gains of the proposed approach over the existing exact and heuristic MVNE approaches.

Network virtualization is considered to be not only an enabler to overcome the inflexibility of the current Internet infrastructure but also an enabler to achieve an energy-efficient Future Internet. Virtual network embedding (VNE) is a critical issue in network virtualization technology. This paper explores a joint power-aware node and link resource allocation approach to handle the VNE problem with the objective of minimizing energy consumption. We first present a generalized power consumption model of embedding a VN. Then we formulate the problem as a mixed integer program and propose embedding algorithms. Simulation results demonstrate that the proposed algorithms perform better than the existing algorithms in terms of the power consumption in the overprovisioned scenarios.

This paper aims to defeat TCG TOCTOU attacks occurring in trusted para-virtualized machines (TPVM). We propose a robust and efficient response mechanism (RERM). Compared to the existing response mechanisms, RERM is more effective in defeating the TPVM TCG TOCTOU attacks without incurring CPU overhead during the normal system execution. We verify the security ability of RERM via both model checking based formal verification techniques and experiments.

The SRTP/ZRTP security mechanism provides a secure channel for VoIP (Voice-over-IP) traffic transmission. This paper examines the use of TCG remote attestation technique to improve the security of SRTP/ZRTP channel technology. We first generalize the security properties which a trust channel technique should have. Then we propose a trusted SRTP/ZRTP channel technique and apply model checker SPIN to verify that the proposed technique can achieve the declared security properties.

LWRM was a method for defeating TCG TOCTOU attacks with less overhead during the normal system execution. However, its security capability was evaluated only through experiments. The uncertainty in real experiments may hide the design-level errors. In this paper we explore applying model checking based formal verification techniques to verify whether LWRM can achieve the declared security properties. We first propose a method of modeling LWRM, a kernel-space mechanism, in PROMELA language. Then we detect the design-level vulnerabilities by using SPIN. At last we verify our analysis via experiments and present the challenges to mitigate the vulnerabilities.

Virtualization technologies have been explored to mitigate the vulnerabilities in the current TCG architecture. But only a small number of efforts address the handling of the detected TCG TOCTOU attacks. This paper aims to defeat TCG TOCTOU attacks occurring in Xen hardware virtual machines. We propose an efficient response approach (ERA) in the environment of the Xen virtual machine monitor and the virtual TPM facility shipped with the Xen. ERA has the following features: (1) Effective in defeating the TCG TOCTOU attacks; (2) Impose less overhead on the system during normal execution; and (3) Transparent to guest virtual machines. We describe the ERA implementation in detail and evaluate its ability via experiments.

The current TCG architecture suffers from the time-of-check-to-time-of-use (TOCTOU) attacks in commodity PC operating systems (OS), in which kernel rootkits can get unrestricted access to OS resources. VMM-based approaches running at a privilege level higher than that of virtual machine (VM) kernel can effectively detect dynamic or static data attacks occurring in VMs. This paper proposes a lightweight response mechanism (LWRM) for TCG TOCTOU attacks occurring in VMs. LWRM has the following features: (1) compared to the existing response mechanism, LWRM is more effective in defeating the TCG TOCTOU attacks; (2) LWRM imposes less overhead on the system during normal execution; (3) LWRM is transparent to the kernel rootkits; and (4) LWRM can work in the scenarios with more than one run-time trusted virtual machine. We describe the design idea and the implementation by using the Xen virtual machine monitor (VMM) and the virtual TPM facility shipped with the Xen.

Recent research studies in over-provisioned networks have shown that the assured forwarding (AF) service in the current differentiated services (DiffServ) architecture fails to provide bandwidth assurance in some situations. The paper focuses on the situation where adaptive and non-adaptive traffic coexist in the same real queue at routers and the buffer management scheme treats the traffic of the same priority in the same AF class indiscriminately. An enhanced RIO is introduced, which, without excessively penalizing non-adaptive flows, can: (1) significantly improve bandwidth assurance of adaptive AF flows; (2) alleviate the starvation imposed on adaptive best-effort flows. These goals are achieved by doing the following when the failure of bandwidth assurance is detected: (1) mapping adaptive OUT traffic and non-adaptive OUT traffic to different virtual queues; (2) adapting the queue length thresholds according to whether bandwidth assurance is achieved. We validate our design through simulations.