Visible to the public Biblio

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2020-02-18
Nasr, Milad, Shokri, Reza, Houmansadr, Amir.  2019.  Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-Box Inference Attacks against Centralized and Federated Learning. 2019 IEEE Symposium on Security and Privacy (SP). :739–753.
Deep neural networks are susceptible to various inference attacks as they remember information about their training data. We design white-box inference attacks to perform a comprehensive privacy analysis of deep learning models. We measure the privacy leakage through parameters of fully trained models as well as the parameter updates of models during training. We design inference algorithms for both centralized and federated learning, with respect to passive and active inference attackers, and assuming different adversary prior knowledge. We evaluate our novel white-box membership inference attacks against deep learning algorithms to trace their training data records. We show that a straightforward extension of the known black-box attacks to the white-box setting (through analyzing the outputs of activation functions) is ineffective. We therefore design new algorithms tailored to the white-box setting by exploiting the privacy vulnerabilities of the stochastic gradient descent algorithm, which is the algorithm used to train deep neural networks. We investigate the reasons why deep learning models may leak information about their training data. We then show that even well-generalized models are significantly susceptible to white-box membership inference attacks, by analyzing state-of-the-art pre-trained and publicly available models for the CIFAR dataset. We also show how adversarial participants, in the federated learning setting, can successfully run active membership inference attacks against other participants, even when the global model achieves high prediction accuracies.
2020-01-20
Noma, Adamu Muhammad, Muhammad, Abdullah.  2019.  Stochastic Heuristic Approach to Addition Chain Problem in PKC for Efficiency and Security Effectiveness. 2019 International Conference on Information Networking (ICOIN). :55–59.

This paper shows that stochastic heuristic approach for implicitly solving addition chain problem (ACP) in public-key cryptosystem (PKC) enhances the efficiency of the PKC and improves the security by blinding the multiplications/squaring operations involved against side-channel attack (SCA). We show that while the current practical heuristic approaches being deterministic expose the fixed pattern of the operations, using stochastic method blinds the pattern by being unpredictable and generating diffident pattern of operation for the same exponent at a different time. Thus, if the addition chain (AC) is generated implicitly every time the exponentiation operation is being made, needless for such approaches as padding by insertion of dummy operations and the operation is still totally secured against the SCA. Furthermore, we also show that the stochastic approaches, when carefully designed, further reduces the length of the operation than state-of-the-art practical methods for improving the efficiency. We demonstrated our investigation by implementing RSA cryptosystem using the stochastic approach and the results benchmarked with the existing current methods.

2019-12-02
Elfar, Mahmoud, Zhu, Haibei, Cummings, M. L., Pajic, Miroslav.  2019.  Security-Aware Synthesis of Human-UAV Protocols. 2019 International Conference on Robotics and Automation (ICRA). :8011–8017.
In this work, we synthesize collaboration protocols for human-unmanned aerial vehicle (H-UAV) command and control systems, where the human operator aids in securing the UAV by intermittently performing geolocation tasks to confirm its reported location. We first present a stochastic game-based model for the system that accounts for both the operator and an adversary capable of launching stealthy false-data injection attacks, causing the UAV to deviate from its path. We also describe a synthesis challenge due to the UAV's hidden-information constraint. Next, we perform human experiments using a developed RESCHU-SA testbed to recognize the geolocation strategies that operators adopt. Furthermore, we deploy machine learning techniques on the collected experimental data to predict the correctness of a geolocation task at a given location based on its geographical features. By representing the model as a delayed-action game and formalizing the system objectives, we utilize off-the-shelf model checkers to synthesize protocols for the human-UAV coalition that satisfy these objectives. Finally, we demonstrate the usefulness of the H-UAV protocol synthesis through a case study where the protocols are experimentally analyzed and further evaluated by human operators.
2019-11-12
Vizarreta, Petra, Sakic, Ermin, Kellerer, Wolfgang, Machuca, Carmen Mas.  2019.  Mining Software Repositories for Predictive Modelling of Defects in SDN Controller. 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). :80-88.

In Software Defined Networking (SDN) control plane of forwarding devices is concentrated in the SDN controller, which assumes the role of a network operating system. Big share of today's commercial SDN controllers are based on OpenDaylight, an open source SDN controller platform, whose bug repository is publicly available. In this article we provide a first insight into 8k+ bugs reported in the period over five years between March 2013 and September 2018. We first present the functional components in OpenDaylight architecture, localize the most vulnerable modules and measure their contribution to the total bug content. We provide high fidelity models that can accurately reproduce the stochastic behaviour of bug manifestation and bug removal rates, and discuss how these can be used to optimize the planning of the test effort, and to improve the software release management. Finally, we study the correlation between the code internals, derived from the Git version control system, and software defect metrics, derived from Jira issue tracker. To the best of our knowledge, this is the first study to provide a comprehensive analysis of bug characteristics in a production grade SDN controller.

2019-03-28
Sahabandu, D., Xiao, B., Clark, A., Lee, S., Lee, W., Poovendran, R..  2018.  DIFT Games: Dynamic Information Flow Tracking Games for Advanced Persistent Threats. 2018 IEEE Conference on Decision and Control (CDC). :1136-1143.
Dynamic Information Flow Tracking (DIFT) has been proposed to detect stealthy and persistent cyber attacks that evade existing defenses such as firewalls and signature-based antivirus systems. A DIFT defense taints and tracks suspicious information flows across the network in order to identify possible attacks, at the cost of additional memory overhead for tracking non-adversarial information flows. In this paper, we present the first analytical model that describes the interaction between DIFT and adversarial information flows, including the probability that the adversary evades detection and the performance overhead of the defense. Our analytical model consists of a multi-stage game, in which each stage represents a system process through which the information flow passes. We characterize the optimal strategies for both the defense and adversary, and derive efficient algorithms for computing the strategies. Our results are evaluated on a realworld attack dataset obtained using the Refinable Attack Investigation (RAIN) framework, enabling us to draw conclusions on the optimal adversary and defense strategies, as well as the effect of valid information flows on the interaction between adversary and defense.
2019-03-22
Liu, Y., Li, X., Xiao, L..  2018.  Service Oriented Resilience Strategy for Cloud Data Center. 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). :269-274.

As an information hinge of various trades and professions in the era of big data, cloud data center bears the responsibility to provide uninterrupted service. To cope with the impact of failure and interruption during the operation on the Quality of Service (QoS), it is important to guarantee the resilience of cloud data center. Thus, different resilience actions are conducted in its life circle, that is, resilience strategy. In order to measure the effect of resilience strategy on the system resilience, this paper propose a new approach to model and evaluate the resilience strategy for cloud data center focusing on its core part of service providing-IT architecture. A comprehensive resilience metric based on resilience loss is put forward considering the characteristic of cloud data center. Furthermore, mapping model between system resilience and resilience strategy is built up. Then, based on a hierarchical colored generalized stochastic petri net (HCGSPN) model depicting the procedure of the system processing the service requests, simulation is conducted to evaluate the resilience strategy through the metric calculation. With a case study of a company's cloud data center, the applicability and correctness of the approach is demonstrated.

2019-03-06
Liu, Y., Wang, Y., Lombardi, F., Han, J..  2018.  An Energy-Efficient Stochastic Computational Deep Belief Network. 2018 Design, Automation Test in Europe Conference Exhibition (DATE). :1175-1178.

Deep neural networks (DNNs) are effective machine learning models to solve a large class of recognition problems, including the classification of nonlinearly separable patterns. The applications of DNNs are, however, limited by the large size and high energy consumption of the networks. Recently, stochastic computation (SC) has been considered to implement DNNs to reduce the hardware cost. However, it requires a large number of random number generators (RNGs) that lower the energy efficiency of the network. To overcome these limitations, we propose the design of an energy-efficient deep belief network (DBN) based on stochastic computation. An approximate SC activation unit (A-SCAU) is designed to implement different types of activation functions in the neurons. The A-SCAU is immune to signal correlations, so the RNGs can be shared among all neurons in the same layer with no accuracy loss. The area and energy of the proposed design are 5.27% and 3.31% (or 26.55% and 29.89%) of a 32-bit floating-point (or an 8-bit fixed-point) implementation. It is shown that the proposed SC-DBN design achieves a higher classification accuracy compared to the fixed-point implementation. The accuracy is only lower by 0.12% than the floating-point design at a similar computation speed, but with a significantly lower energy consumption.

2019-02-25
Akcay, A., Martagan, T., Corlu, C. G..  2018.  RISK ASSESSMENT IN PHARMACEUTICAL SUPPLY CHAINS UNDER UNKNOWN INPUT-MODEL PARAMETERS. 2018 Winter Simulation Conference (WSC). :3132–3143.
We consider a pharmaceutical supply chain where the manufacturer sources a customized product with unique attributes from a set of unreliable suppliers. We model the likelihood of a supplier to successfully deliver the product via Bayesian logistic regression and use simulation to obtain the posterior distribution of the unknown parameters of this model. We study the role of so-called input-model uncertainty in estimating the likelihood of the supply failure, which is the probability that none of the suppliers in a given supplier portfolio can successfully deliver the product. We investigate how the input-model uncertainty changes with respect to the characteristics of the historical data on the past realizations of the supplier performances and the product attributes.
2019-02-22
Yu, R., Xue, G., Kilari, V. T., Zhang, X..  2018.  Deploying Robust Security in Internet of Things. 2018 IEEE Conference on Communications and Network Security (CNS). :1-9.

Popularization of the Internet-of-Things (IoT) has brought widespread concerns on IoT security, especially in face of several recent security incidents related to IoT devices. Due to the resource-constrained nature of many IoT devices, security offloading has been proposed to provide good-enough security for IoT with minimum overhead on the devices. In this paper, we investigate the inevitable risk associated with security offloading: the unprotected and unmonitored transmission from IoT devices to the offloaded security mechanisms. An important challenge in modeling the security risk is the dynamic nature of IoT due to demand fluctuations and infrastructure instability. We propose a stochastic model to capture both the expected and worst-case security risks of an IoT system. We then propose a framework to efficiently address the optimal robust deployment of security mechanisms in IoT. We use results from extensive simulations to demonstrate the superb performance and efficiency of our approach compared to several other algorithms.

Nie, J., Tang, H., Wei, J..  2018.  Analysis on Convergence of Stochastic Processes in Cloud Computing Models. 2018 14th International Conference on Computational Intelligence and Security (CIS). :71-76.
On cloud computing systems consisting of task queuing and resource allocations, it is essential but hard to model and evaluate the global performance. In most of the models, researchers use a stochastic process or several stochastic processes to describe a real system. However, due to the absence of theoretical conclusions of any arbitrary stochastic processes, they approximate the complicated model into simple processes that have mathematical results, such as Markov processes. Our purpose is to give a universal method to deal with common stochastic processes as long as the processes can be expressed in the form of transition matrix. To achieve our purpose, we firstly prove several theorems about the convergence of stochastic matrices to figure out what kind of matrix-defined systems has steady states. Furthermore, we propose two strategies for measuring the rate of convergence which reflects how fast the system would come to its steady state. Finally, we give a method for reducing a stochastic matrix into smaller ones, and perform some experiments to illustrate our strategies in practice.
2018-12-10
Farooq, M. J., Zhu, Q..  2018.  On the Secure and Reconfigurable Multi-Layer Network Design for Critical Information Dissemination in the Internet of Battlefield Things (IoBT). IEEE Transactions on Wireless Communications. 17:2618–2632.

The Internet of things (IoT) is revolutionizing the management and control of automated systems leading to a paradigm shift in areas, such as smart homes, smart cities, health care, and transportation. The IoT technology is also envisioned to play an important role in improving the effectiveness of military operations in battlefields. The interconnection of combat equipment and other battlefield resources for coordinated automated decisions is referred to as the Internet of battlefield things (IoBT). IoBT networks are significantly different from traditional IoT networks due to battlefield specific challenges, such as the absence of communication infrastructure, heterogeneity of devices, and susceptibility to cyber-physical attacks. The combat efficiency and coordinated decision-making in war scenarios depends highly on real-time data collection, which in turn relies on the connectivity of the network and information dissemination in the presence of adversaries. This paper aims to build the theoretical foundations of designing secure and reconfigurable IoBT networks. Leveraging the theories of stochastic geometry and mathematical epidemiology, we develop an integrated framework to quantify the information dissemination among heterogeneous network devices. Consequently, a tractable optimization problem is formulated that can assist commanders in cost effectively planning the network and reconfiguring it according to the changing mission requirements.

2018-08-23
Lagunas, E., Rugini, L..  2017.  Performance of compressive sensing based energy detection. 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). :1–5.

This paper investigates closed-form expressions to evaluate the performance of the Compressive Sensing (CS) based Energy Detector (ED). The conventional way to approximate the probability density function of the ED test statistic invokes the central limit theorem and considers the decision variable as Gaussian. This approach, however, provides good approximation only if the number of samples is large enough. This is not usually the case in CS framework, where the goal is to keep the sample size low. Moreover, working with a reduced number of measurements is of practical interest for general spectrum sensing in cognitive radio applications, where the sensing time should be sufficiently short since any time spent for sensing cannot be used for data transmission on the detected idle channels. In this paper, we make use of low-complexity approximations based on algebraic transformations of the one-dimensional Gaussian Q-function. More precisely, this paper provides new closed-form expressions for accurate evaluation of the CS-based ED performance as a function of the compressive ratio and the Signal-to-Noise Ratio (SNR). Simulation results demonstrate the increased accuracy of the proposed equations compared to existing works.

2018-06-07
Aygun, R. C., Yavuz, A. G..  2017.  Network Anomaly Detection with Stochastically Improved Autoencoder Based Models. 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud). :193–198.

Intrusion detection systems do not perform well when it comes to detecting zero-day attacks, therefore improving their performance in that regard is an active research topic. In this study, to detect zero-day attacks with high accuracy, we proposed two deep learning based anomaly detection models using autoencoder and denoising autoencoder respectively. The key factor that directly affects the accuracy of the proposed models is the threshold value which was determined using a stochastic approach rather than the approaches available in the current literature. The proposed models were tested using the KDDTest+ dataset contained in NSL-KDD, and we achieved an accuracy of 88.28% and 88.65% respectively. The obtained results show that, as a singular model, our proposed anomaly detection models outperform any other singular anomaly detection methods and they perform almost the same as the newly suggested hybrid anomaly detection models.

Kang, E. Y., Mu, D., Huang, L., Lan, Q..  2017.  Verification and Validation of a Cyber-Physical System in the Automotive Domain. 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). :326–333.
Software development for Cyber-Physical Systems (CPS), e.g., autonomous vehicles, requires both functional and non-functional quality assurance to guarantee that the CPS operates safely and effectively. EAST-ADL is a domain specific architectural language dedicated to safety-critical automotive embedded system design. We have previously modified EAST-ADL to include energy constraints and transformed energy-aware real-time (ERT) behaviors modeled in EAST-ADL/Stateflow into UPPAAL models amenable to formal verification. Previous work is extended in this paper by including support for Simulink and an integration of Simulink/Stateflow (S/S) within the same too lchain. S/S models are transformed, based on the extended ERT constraints with probability parameters, into verifiable UPPAAL-SMC models and integrate the translation with formal statistical analysis techniques: Probabilistic extension of EAST-ADL constraints is defined as a semantics denotation. A set of mapping rules is proposed to facilitate the guarantee of translation. Formal analysis on both functional- and non-functional properties is performed using Simulink Design Verifier and UPPAAL-SMC. Our approach is demonstrated on the autonomous traffic sign recognition vehicle case study.
Hinojosa, V., Gonzalez-Longatt, F..  2017.  Stochastic security-constrained generation expansion planning methodology based on a generalized line outage distribution factors. 2017 IEEE Manchester PowerTech. :1–6.

In this study, it is proposed to carry out an efficient formulation in order to figure out the stochastic security-constrained generation capacity expansion planning (SC-GCEP) problem. The main idea is related to directly compute the line outage distribution factors (LODF) which could be applied to model the N - m post-contingency analysis. In addition, the post-contingency power flows are modeled based on the LODF and the partial transmission distribution factors (PTDF). The post-contingency constraints have been reformulated using linear distribution factors (PTDF and LODF) so that both the pre- and post-contingency constraints are modeled simultaneously in the SC-GCEP problem using these factors. In the stochastic formulation, the load uncertainty is incorporated employing a two-stage multi-period framework, and a K - means clustering technique is implemented to decrease the number of load scenarios. The main advantage of this methodology is the feasibility to quickly compute the post-contingency factors especially with multiple-line outages (N - m). This concept would improve the security-constraint analysis modeling quickly the outage of m transmission lines in the stochastic SC-GCEP problem. It is carried out several experiments using two electrical power systems in order to validate the performance of the proposed formulation.

Hinojosa, V..  2017.  A generalized stochastic N-m security-constrained generation expansion planning methodology using partial transmission distribution factors. 2017 IEEE Power Energy Society General Meeting. :1–5.

This study proposes to apply an efficient formulation to solve the stochastic security-constrained generation capacity expansion planning (GCEP) problem using an improved method to directly compute the generalized generation distribution factors (GGDF) and the line outage distribution factors (LODF) in order to model the pre- and the post-contingency constraints based on the only application of the partial transmission distribution factors (PTDF). The classical DC-based formulation has been reformulated in order to include the security criteria solving both pre- and post-contingency constraints simultaneously. The methodology also takes into account the load uncertainty in the optimization problem using a two-stage multi-period model, and a clustering technique is used as well to reduce load scenarios (stochastic problem). The main advantage of this methodology is the feasibility to quickly compute the LODF especially with multiple-line outages (N-m). This idea could speed up contingency analyses and improve significantly the security-constrained analyses applied to GCEP problems. It is worth to mentioning that this approach is carried out without sacrificing optimality.

2018-05-01
Wang, X., Zhou, S..  2017.  Accelerated Stochastic Gradient Method for Support Vector Machines Classification with Additive Kernel. 2017 First International Conference on Electronics Instrumentation Information Systems (EIIS). :1–6.

Support vector machines (SVMs) have been widely used for classification in machine learning and data mining. However, SVM faces a huge challenge in large scale classification tasks. Recent progresses have enabled additive kernel version of SVM efficiently solves such large scale problems nearly as fast as a linear classifier. This paper proposes a new accelerated mini-batch stochastic gradient descent algorithm for SVM classification with additive kernel (AK-ASGD). On the one hand, the gradient is approximated by the sum of a scalar polynomial function for each feature dimension; on the other hand, Nesterov's acceleration strategy is used. The experimental results on benchmark large scale classification data sets show that our proposed algorithm can achieve higher testing accuracies and has faster convergence rate.

2018-04-11
Ma, C., Guo, Y., Su, J..  2017.  A Multiple Paths Scheme with Labels for Key Distribution on Quantum Key Distribution Network. 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). :2513–2517.

This paper establishes a probability model of multiple paths scheme of quantum key distribution with public nodes among a set of paths which are used to transmit the key between the source node and the destination node. Then in order to be used in universal net topologies, combining with the key routing in the QKD network, the algorithm of the multiple paths scheme of key distribution we propose includes two major aspects: one is an approach which can confirm the number and the distance of the selection of paths, and the other is the strategy of stochastic paths with labels that can decrease the number of public nodes and avoid the phenomenon that the old scheme may produce loops and often get the nodes apart from the destination node father than current nodes. Finally, the paper demonstrates the rationality of the probability model and strategies about the algorithm.

2018-04-02
He, X., Islam, M. M., Jin, R., Dai, H..  2017.  Foresighted Deception in Dynamic Security Games. 2017 IEEE International Conference on Communications (ICC). :1–6.

Deception has been widely considered in literature as an effective means of enhancing security protection when the defender holds some private information about the ongoing rivalry unknown to the attacker. However, most of the existing works on deception assume static environments and thus consider only myopic deception, while practical security games between the defender and the attacker may happen in dynamic scenarios. To better exploit the defender's private information in dynamic environments and improve security performance, a stochastic deception game (SDG) framework is developed in this work to enable the defender to conduct foresighted deception. To solve the proposed SDG, a new iterative algorithm that is provably convergent is developed. A corresponding learning algorithm is developed as well to facilitate the defender in conducting foresighted deception in unknown dynamic environments. Numerical results show that the proposed foresighted deception can offer a substantial performance improvement as compared to the conventional myopic deception.

2018-03-26
Movahedi, Y., Cukier, M., Andongabo, A., Gashi, I..  2017.  Cluster-Based Vulnerability Assessment Applied to Operating Systems. 2017 13th European Dependable Computing Conference (EDCC). :18–25.

Organizations face the issue of how to best allocate their security resources. Thus, they need an accurate method for assessing how many new vulnerabilities will be reported for the operating systems (OSs) they use in a given time period. Our approach consists of clustering vulnerabilities by leveraging the text information within vulnerability records, and then simulating the mean value function of vulnerabilities by relaxing the monotonic intensity function assumption, which is prevalent among the studies that use software reliability models (SRMs) and nonhomogeneous Poisson process (NHPP) in modeling. We applied our approach to the vulnerabilities of four OSs: Windows, Mac, IOS, and Linux. For the OSs analyzed in terms of curve fitting and prediction capability, our results, compared to a power-law model without clustering issued from a family of SRMs, are more accurate in all cases we analyzed.

2018-03-19
Salem, A., Liao, X., Shen, Y., Lu, X..  2017.  Provoking the Adversary by Dual Detection Techniques: A Game Theoretical Framework. 2017 International Conference on Networking and Network Applications (NaNA). :326–329.

Establishing a secret and reliable wireless communication is a challenging task that is of paramount importance. In this paper, we investigate the physical layer security of a legitimate transmission link between a user that assists an Intrusion Detection System (IDS) in detecting eavesdropping and jamming attacks in the presence of an adversary that is capable of conducting an eavesdropping or a jamming attack. The user is being faced by a challenge of whether to transmit, thus becoming vulnerable to an eavesdropping or a jamming attack, or to keep silent and consequently his/her transmission will be delayed. The adversary is also facing a challenge of whether to conduct an eavesdropping or a jamming attack that will not get him/her to be detected. We model the interactions between the user and the adversary as a two-state stochastic game. Explicit solutions characterize some properties while highlighting some interesting strategies that are being embraced by the user and the adversary. Results show that our proposed system outperform current systems in terms of communication secrecy.

Tajer, A..  2017.  Data Injection Attacks in Electricity Markets: Stochastic Robustness. 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP). :1095–1099.

Deregulated electricity markets rely on a two-settlement system consisting of day-ahead and real-time markets, across which electricity price is volatile. In such markets, locational marginal pricing is widely adopted to set electricity prices and manage transmission congestion. Locational marginal prices are vulnerable to measurement errors. Existing studies show that if the adversaries are omniscient, they can design profitable attack strategies without being detected by the residue-based bad data detectors. This paper focuses on a more realistic setting, in which the attackers have only partial and imperfect information due to their limited resources and restricted physical access to the grid. Specifically, the attackers are assumed to have uncertainties about the state of the grid, and the uncertainties are modeled stochastically. Based on this model, this paper offers a framework for characterizing the optimal stochastic guarantees for the effectiveness of the attacks and the associated pricing impacts.

Jin, X., Haddad, W. M., Hayakawa, T..  2017.  An Adaptive Control Architecture for Cyber-Physical System Security in the Face of Sensor and Actuator Attacks and Exogenous Stochastic Disturbances. 2017 IEEE 56th Annual Conference on Decision and Control (CDC). :1380–1385.

In this paper, we propose a novel adaptive control architecture for addressing security and safety in cyber-physical systems subject to exogenous disturbances. Specifically, we develop an adaptive controller for time-invariant, state-dependent adversarial sensor and actuator attacks in the face of stochastic exogenous disturbances. We show that the proposed controller guarantees uniform ultimate boundedness of the closed-loop dynamical system in a mean-square sense. We further discuss the practicality of the proposed approach and provide a numerical example involving the lateral directional dynamics of an aircraft to illustrate the efficacy of the proposed adaptive control architecture.

2018-02-15
Škach, J., Straka, O., Punčochář, I..  2017.  Efficient active fault diagnosis using adaptive particle filter. 2017 IEEE 56th Annual Conference on Decision and Control (CDC). :5732–5738.
This paper presents a solution to a multiple-model based stochastic active fault diagnosis problem over the infinite-time horizon. A general additive detection cost criterion is considered to reflect the objectives. Since the system state is unknown, the design consists of a perfect state information reformulation and optimization problem solution by approximate dynamic programming. An adaptive particle filter state estimation algorithm based on the efficient sample size is proposed to maintain the estimate quality while reducing computational costs. A reduction of information statistics of the state is carried out using non-resampled particles to make the solution feasible. Simulation results illustrate the effectiveness of the proposed design.
2017-12-28
Vizarreta, P., Heegaard, P., Helvik, B., Kellerer, W., Machuca, C. M..  2017.  Characterization of failure dynamics in SDN controllers. 2017 9th International Workshop on Resilient Networks Design and Modeling (RNDM). :1–7.

With Software Defined Networking (SDN) the control plane logic of forwarding devices, switches and routers, is extracted and moved to an entity called SDN controller, which acts as a broker between the network applications and physical network infrastructure. Failures of the SDN controller inhibit the network ability to respond to new application requests and react to events coming from the physical network. Despite of the huge impact that a controller has on the network performance as a whole, a comprehensive study on its failure dynamics is still missing in the state of the art literature. The goal of this paper is to analyse, model and evaluate the impact that different controller failure modes have on its availability. A model in the formalism of Stochastic Activity Networks (SAN) is proposed and applied to a case study of a hypothetical controller based on commercial controller implementations. In case study we show how the proposed model can be used to estimate the controller steady state availability, quantify the impact of different failure modes on controller outages, as well as the effects of software ageing, and impact of software reliability growth on the transient behaviour.