Visible to the public Biblio

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Yaseen, Q., Panda, B..  2012.  Tackling Insider Threat in Cloud Relational Databases. 2012 IEEE Fifth International Conference on Utility and Cloud Computing. :215—218.
Cloud security is one of the major issues that worry individuals and organizations about cloud computing. Therefore, defending cloud systems against attacks such asinsiders' attacks has become a key demand. This paper investigates insider threat in cloud relational database systems(cloud RDMS). It discusses some vulnerabilities in cloud computing structures that may enable insiders to launch attacks, and shows how load balancing across multiple availability zones may facilitate insider threat. To prevent such a threat, the paper suggests three models, which are Peer-to-Peer model, Centralized model and Mobile-Knowledgebase model, and addresses the conditions under which they work well.
Yamaguchi, A., Mizuno, O..  2020.  Reducing Processing Delay and Node Load Using Push-Based Information-Centric Networking. 2020 3rd World Symposium on Communication Engineering (WSCE). :59–63.
Information-Centric Networking (ICN) is attracting attention as a content distribution method against increasing network traffic. Content distribution in ICN adopts a pull-type communication method that returns data to Interest. However, in this case, the push-type communication method is advantageous. Therefore, the authors have proposed a method in which a server pushes content to reduce the node load in an environment where a large amount of Interest to specific content occurs in a short time. In this paper, we analyze the packet processing delay time with and without the proposed method in an environment where a router processes a large number of packets using a simulator. Simulation results show that the proposed method can reduce packet processing delay time and node load.
Nasir, N. A., Jeong, S.-H..  2020.  Testbed-based Performance Evaluation of the Information-Centric Network. 2020 International Conference on Information and Communication Technology Convergence (ICTC). :166–169.
Proliferation of the Internet usage is rapidly increasing, and it is necessary to support the performance requirements for multimedia applications, including lower latency, improved security, faster content retrieval, and adjustability to the traffic load. Nevertheless, because the current Internet architecture is a host-oriented one, it often fails to support the necessary demands such as fast content delivery. A promising networking paradigm called Information-Centric Networking (ICN) focuses on the name of the content itself rather than the location of that content. A distinguished alternative to this ICN concept is Content-Centric Networking (CCN) that exploits more of the performance requirements by using in-network caching and outperforms the current Internet in terms of content transfer time, traffic load control, mobility support, and efficient network management. In this paper, instead of using the saturated method of validating a theory by simulation, we present a testbed-based performance evaluation of the ICN network. We used several new functions of the proposed testbed to improve the performance of the basic CCN. In this paper, we also show that the proposed testbed architecture performs better in terms of content delivery time compared to the basic CCN architecture through graphical results.
Jin, Z., Yu, P., Guo, S. Y., Feng, L., Zhou, F., Tao, M., Li, W., Qiu, X., Shi, L..  2020.  Cyber-Physical Risk Driven Routing Planning with Deep Reinforcement-Learning in Smart Grid Communication Networks. 2020 International Wireless Communications and Mobile Computing (IWCMC). :1278—1283.
In modern grid systems which is a typical cyber-physical System (CPS), information space and physical space are closely related. Once the communication link is interrupted, it will make a great damage to the power system. If the service path is too concentrated, the risk will be greatly increased. In order to solve this problem, this paper constructs a route planning algorithm that combines node load pressure, link load balance and service delay risk. At present, the existing intelligent algorithms are easy to fall into the local optimal value, so we chooses the deep reinforcement learning algorithm (DRL). Firstly, we build a risk assessment model. The node risk assessment index is established by using the node load pressure, and then the link risk assessment index is established by using the average service communication delay and link balance degree. The route planning problem is then solved by a route planning algorithm based on DRL. Finally, experiments are carried out in a simulation scenario of a power grid system. The results show that our method can find a lower risk path than the original Dijkstra algorithm and the Constraint-Dijkstra algorithm.
Liu, S., Kosuru, R., Mugombozi, C. F..  2020.  A Moving Target Approach for Securing Secondary Frequency Control in Microgrids. 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). :1–6.
Microgrids' dependency on communication links exposes the control systems to cyber attack threats. In this work, instead of designing reactive defense approaches, a proacitve moving target defense mechanism is proposed for securing microgrid secondary frequency control from denial of service (DoS) attack. The sensor data is transmitted by following a Markov process, not in a deterministic way. This uncertainty will increase the difficulty for attacker's decision making and thus significantly reduce the attack space. As the system parameters are constantly changing, a gain scheduling based secondary frequency controller is designed to sustain the system performance. Case studies of a microgrid with four inverter-based DGs show the proposed moving target mechanism can enhance the resiliency of the microgrid control systems against DoS attacks.
Lee, J..  2020.  CanvasMirror: Secure Integration of Third-Party Libraries in a WebVR Environment. 2020 50th Annual IEEE-IFIP International Conference on Dependable Systems and Networks-Supplemental Volume (DSN-S). :75—76.

Web technology has evolved to offer 360-degree immersive browsing experiences. This new technology, called WebVR, enables virtual reality by rendering a three-dimensional world on an HTML canvas. Unfortunately, there exists no browser-supported way of sharing this canvas between different parties. As a result, third-party library providers with ill intent (e.g., stealing sensitive information from end-users) can easily distort the entire WebVR site. To mitigate the new threats posed in WebVR, we propose CanvasMirror, which allows publishers to specify the behaviors of third-party libraries and enforce this specification. We show that CanvasMirror effectively separates the third-party context from the host origin by leveraging the privilege separation technique and safely integrates VR contents on a shared canvas.

Wang, C., Huang, N., Sun, L., Wen, G..  2018.  A Titration Mechanism Based Congestion Model. 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). :491—496.

Congestion diffusion resulting from the coupling by resource competing is a kind of typical failure propagation in network systems. The existing models of failure propagation mainly focused on the coupling by direct physical connection between nodes, the most efficiency path, or dependence group, while the coupling by resource competing is ignored. In this paper, a model of network congestion diffusion with resource competing is proposed. With the analysis of the similarities to resource competing in biomolecular network, the model describing the dynamic changing process of biomolecule concentration based on titration mechanism provides reference for our model. Then the innovation on titration mechanism is proposed to describe the dynamic changing process of link load in networks, and a novel congestion model is proposed. By this model, the global congestion can be evaluated. Simulations show that network congestion with resource competing can be obtained from our model.

Paul, S., Padhy, N. P., Mishra, S. K., Srivastava, A. K..  2019.  UUCA: Utility-User Cooperative Algorithm for Flexible Load Scheduling in Distribution System. 2019 8th International Conference on Power Systems (ICPS). :1—6.
Demand response analysis in smart grid deployment substantiated itself as an important research area in recent few years. Two-way communication between utility and users makes peak load reduction feasible by delaying the operation of deferrable appliances. Flexible appliance rescheduling is preferred to the users compared to traditional load curtailment. Again, if users' preferences are accounted into appliance transferring process, then customers concede a little discomfort to help the utility in peak reduction. This paper presents a novel Utility-User Cooperative Algorithm (UUCA) to lower total electricity cost and gross peak demand while preserving users' privacy and preferences. Main driving force in UUCA to motivate the consumers is a new cost function for their flexible appliances. As a result, utility will experience low peak and due to electricity cost decrement, users will get reduced bill. However, to maintain privacy, the behaviors of one customer have not be revealed either to other customers or to the central utility. To justify the effectiveness, UUCA is executed separately on residential, commercial and industrial customers of a distribution grid. Harmony search optimization technique has proved itself superior compared to other heuristic search techniques to prove efficacy of UUCA.
Chin, J., Zufferey, T., Shyti, E., Hug, G..  2019.  Load Forecasting of Privacy-Aware Consumers. 2019 IEEE Milan PowerTech. :1—6.

The roll-out of smart meters (SMs) in the electric grid has enabled data-driven grid management and planning techniques. SM data can be used together with short-term load forecasts (STLFs) to overcome polling frequency constraints for better grid management. However, the use of SMs that report consumption data at high spatial and temporal resolutions entails consumer privacy risks, motivating work in protecting consumer privacy. The impact of privacy protection schemes on STLF accuracy is not well studied, especially for smaller aggregations of consumers, whose load profiles are subject to more volatility and are, thus, harder to predict. In this paper, we analyse the impact of two user demand shaping privacy protection schemes, model-distribution predictive control (MDPC) and load-levelling, on STLF accuracy. Support vector regression is used to predict the load profiles at different consumer aggregation levels. Results indicate that, while the MDPC algorithm marginally affects forecast accuracy for smaller consumer aggregations, this diminishes at higher aggregation levels. More importantly, the load-levelling scheme significantly improves STLF accuracy as it smoothens out the grid visible consumer load profile.

Sun, Y., Wang, J., Lu, Z..  2019.  Asynchronous Parallel Surrogate Optimization Algorithm Based on Ensemble Surrogating Model and Stochastic Response Surface Method. :74—84.
{Surrogate model-based optimization algorithm remains as an important solution to expensive black-box function optimization. The introduction of ensemble model enables the algorithm to automatically choose a proper model integration mode and adapt to various parameter spaces when dealing with different problems. However, this also significantly increases the computational burden of the algorithm. On the other hand, utilizing parallel computing resources and improving efficiency of black-box function optimization also require combination with surrogate optimization algorithm in order to design and realize an efficient parallel parameter space sampling mechanism. This paper makes use of parallel computing technology to speed up the weight updating related computation for the ensemble model based on Dempster-Shafer theory, and combines it with stochastic response surface method to develop a novel parallel sampling mechanism for asynchronous parameter optimization. Furthermore, it designs and implements corresponding parallel computing framework and applies the developed algorithm to quantitative trading strategy tuning in financial market. It is verified that the algorithm is both feasible and effective in actual application. The experiment demonstrates that with guarantee of optimizing performance, the parallel optimization algorithm can achieve excellent accelerating effect.
Liang, Y., He, D., Chen, D..  2019.  Poisoning Attack on Load Forecasting. 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia). :1230—1235.

Short-term load forecasting systems for power grids have demonstrated high accuracy and have been widely employed for commercial use. However, classic load forecasting systems, which are based on statistical methods, are subject to vulnerability from training data poisoning. In this paper, we demonstrate a data poisoning strategy that effectively corrupts the forecasting model even in the presence of outlier detection. To the best of our knowledge, poisoning attack on short-term load forecasting with outlier detection has not been studied in previous works. Our method applies to several forecasting models, including the most widely-adapted and best-performing ones, such as multiple linear regression (MLR) and neural network (NN) models. Starting with the MLR model, we develop a novel closed-form solution to quickly estimate the new MLR model after a round of data poisoning without retraining. We then employ line search and simulated annealing to find the poisoning attack solution. Furthermore, we use the MLR attacking solution to generate a numerical solution for other models, such as NN. The effectiveness of our algorithm has been tested on the Global Energy Forecasting Competition (GEFCom2012) data set with the presence of outlier detection.

Khezrimotlagh, Darius, Khazaei, Javad, Asrari, Arash.  2019.  MILP Modeling of Targeted False Load Data Injection Cyberattacks to Overflow Transmission Lines in Smart Grids. 2019 North American Power Symposium (NAPS). :1—7.
Cyber attacks on transmission lines are one of the main challenges in security of smart grids. These targeted attacks, if not detected, might cause cascading problems in power systems. This paper proposes a bi-level mixed integer linear programming (MILP) optimization model for false data injection on targeted buses in a power system to overflow targeted transmission lines. The upper level optimization problem outputs the optimized false data injections on targeted load buses to overflow a targeted transmission line without violating bad data detection constraints. The lower level problem integrates the false data injections into the optimal power flow problem without violating the optimal power flow constraints. A few case studies are designed to validate the proposed attack model on IEEE 118-bus power system.
Xu, Shuiling, Ji, Xinsheng, Liu, Wenyan.  2019.  Enhancing the Reliability of NFV with Heterogeneous Backup. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). :923–927.
Virtual network function provides tenant with flexible and scalable end-to-end service chaining in the cloud computing and data center environments. However, comparing with traditional hardware network devices, the uncertainty caused by software and virtualization of Network Function Virtualization expands the attack surface, making the network node vulnerable to a certain types of attacks. The existing approaches for solving the problem of reliability are able to reduce the impact of failure of physical devices, but pay little attention to the attack scenario, which could be persistent and covert. In this paper, a heterogeneous backup strategy is brought up, enhancing the intrusion tolerance of NFV SFC by dynamically switching the VNF executor. The validity of the method is verified by simulation and game theory analysis.
Kërçi, Taulant, Milano, Federico.  2019.  A Framework to embed the Unit Commitment Problem into Time Domain Simulations. 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe). :1—5.

This paper proposes a software framework to embed the unit commitment problem into a power system dynamic simulator. A sub-hourly, mixed-integer linear programming Security Constrained Unit Commitment (SCUC) with a rolling horizon is utilized to account for the variations of the net load of the system. The SCUC is then included into time domain simulations to study the impact of the net-load variability and uncertainty on the dynamic behavior of the system using different scheduling time periods. A case study based on the 39-bus system illustrates the features of the proposed software framework.

Nath, Anubhav, Biswas, Reetam Sen, Pal, Anamitra.  2019.  Application of Machine Learning for Online Dynamic Security Assessment in Presence of System Variability and Additive Instrumentation Errors. 2019 North American Power Symposium (NAPS). :1—6.
Large-scale blackouts that have occurred in the past few decades have necessitated the need to do extensive research in the field of grid security assessment. With the aid of synchrophasor technology, which uses phasor measurement unit (PMU) data, dynamic security assessment (DSA) can be performed online. However, existing applications of DSA are challenged by variability in system conditions and unaccounted for measurement errors. To overcome these challenges, this research develops a DSA scheme to provide security prediction in real-time for load profiles of different seasons in presence of realistic errors in the PMU measurements. The major contributions of this paper are: (1) develop a DSA scheme based on PMU data, (2) consider seasonal load profiles, (3) account for varying penetrations of renewable generation, and (4) compare the accuracy of different machine learning (ML) algorithms for DSA with and without erroneous measurements. The performance of this approach is tested on the IEEE-118 bus system. Comparative analysis of the accuracies of the ML algorithms under different operating scenarios highlights the importance of considering realistic errors and variability in system conditions while creating a DSA scheme.
Wang, Jihe, Zhang, Meng, Qiu, Meikang.  2018.  A Diffusional Schedule for Traffic Reducing on Network-on-Chip. 2018 5th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2018 4th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). :206—210.
pubcrawl, Network on Chip Security, Scalability, resiliency, resilience, metrics, Tasks on NoC (Network-on-Chip) are less efficient because of long-distance data synchronization. An inefficient task schedule strategy can lead to a large number of remote data accessing that ruins the speedup of parallel execution of multiple tasks. Thus, we propose an energy efficient task schedule to reduce task traffic with a diffusional pattern. The task mapping algorithm can optimize traffic distribution by limit tasks into a small area to reduce NoC activities. Comparing to application-layer optimization, our task mapping can obtain 20% energy saving and 15% latency reduction on average.
Zhou, Zichao, An, Changqing, Yang, Jiahai.  2018.  A Programmable Network Management Architecture for Address Driven Network. 2018 10th International Conference on Communications, Circuits and Systems (ICCCAS). :199–206.
The operation and management of network is facing increasing complexities brought by the evolution of network protocols and the demands of rapid service delivery. In this paper, we propose a programmable network management architecture, which manages network based on NETCONF protocol and provides REST APIs to upper layer so that further programming can be done based on the APIs to implement flexible management. Functions of devices can be modeled based on YANG language, and the models can be translated into REST APIs. We apply it to the management of ADN (Address Driven Network), an innovative network architecture proposed by Tsinghua University to inhibit IP spoofing, improve network security and provide high service quality. We model the functions of ADN based on YANG language, and implement the network management functions based on the REST APIs. We deploy and evaluate it in a laboratory environment. Test result shows that the programmable network management architecture is flexible to implement management for new network services.
Shuvro, Rezoan A., Das, Pankaz, Hayat, Majeed M., Talukder, Mitun.  2019.  Predicting Cascading Failures in Power Grids using Machine Learning Algorithms. 2019 North American Power Symposium (NAPS). :1—6.
Although there has been notable progress in modeling cascading failures in power grids, few works included using machine learning algorithms. In this paper, cascading failures that lead to massive blackouts in power grids are predicted and classified into no, small, and large cascades using machine learning algorithms. Cascading-failure data is generated using a cascading failure simulator framework developed earlier. The data set includes the power grid operating parameters such as loading level, level of load shedding, the capacity of the failed lines, and the topological parameters such as edge betweenness centrality and the average shortest distance for numerous combinations of two transmission line failures as features. Then several machine learning algorithms are used to classify cascading failures. Further, linear regression is used to predict the number of failed transmission lines and the amount of load shedding during a cascade based on initial feature values. This data-driven technique can be used to generate cascading failure data set for any real-world power grids and hence, power-grid engineers can use this approach for cascade data generation and hence predicting vulnerabilities and enhancing robustness of the grid.
Pan, Huan, Lian, Honghui, Na, Chunning.  2019.  Vulnerability Analysis of Smart Grid under Community Attack Style. IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. 1:5971—5976.
The smart grid consists of two parts, one is the physical power grid, the other is the information network. In order to study the cascading failure, the vulnerability analysis of the smart grid is done under a kind of community attack style in this paper. Two types of information networks are considered, i.e. topology consistency and scale-free cyber networks, respectively. The concept of control center is presented and the controllable power nodes and observable power lines are defined. Minimum load reduction model(MLRM) is given and described as a linear programming problem. A index is introduced to assess the vulnerability. New England 39 nodes system is applied to simulate the cascading failure process to demonstrate the effectiveness of the proposed MLRM where community the attack methods include attack the power lines among and in power communities.
Luo, Xuesong, Wang, Shaoping.  2018.  Multi-work Condition Modeling and Performance Analysis of Linear Oscillating Actuators. 2018 IEEE International Conference on Prognostics and Health Management (ICPHM). :1—7.

Linear oscillating actuators are emerging electrical motors applied to direct-drive electromechanical systems. They merit high efficiency and quick dynamical property due to the unique structure of spring oscillator. Resonant principle is the base of their high performance, which however, is easily influenced by various load, complex environment and mechanical failure. This paper studies the modeling of linear oscillating actuators in multi-work condition. Three kinds of load are considered in performance evaluation model. Simulations are conducted at different frequencies to obtain the actuator behavior, especially at non-resonance frequencies. A method of constant impedance angle is proposed to search the best working points in sorts of conditions. Eventually, analytical results reflect that the resonant parameter would drift with load, while linear oscillating actuators exhibits robustness in efficiency performance. Several evaluating parameters are concluded to assess the actuator health status.

Noeren, Jannis, Parspour, Nejila.  2019.  A Dynamic Model for Contactless Energy Transfer Systems. 2019 IEEE PELS Workshop on Emerging Technologies: Wireless Power Transfer (WoW). :297—301.

Inductive contactless energy transfer (CET) systems show a certain oscillating transient behavior of inrush currents on both system sides. This causes current overshoots in the electrical components and has to be considered for the system dimensioning. This paper presents a simple and yet very accurate model, which describes the dynamic behavior of series-series compensated inductive CET systems. This model precisely qualifies the systems current courses for both sides in time domain. Additionally, an analysis in frequency domain allows further knowledge for parameter estimation. Since this model is applicable for purely resistive loads and constant voltage loads with bridge rectifiers, it is very practicable and can be useful for control techniques and narameter estimation.

de Rooij, Sjors, Laguna, Antonio Jarquin.  2019.  Modelling of submerged oscillating water columns with mass transfer for wave energy extraction. 2019 Offshore Energy and Storage Summit (OSES). :1—9.
Oscillating-water-column (OWC) devices are a very important type of wave energy converters which have been extensively studied over the years. Although most designs of OWC are based on floating or fixed structures exposed above the surface level, little is known from completely submerged systems which can benefit from reduced environmental loads and a simplified structural design. The submerged type of resonant duct consists of two OWCs separated by a weir and air chamber instead of the commonly used single column. Under conditions close to resonance, water flows from the first column into the second one, resulting in a positive flow through the system from which energy can be extracted by a hydro turbine. While existing work has looked at the study of the behaviour of one OWC, this paper addresses the dynamic interaction between the two water columns including the mass transfer mechanism as well as the associated change of momentum. A numerical time-domain model is used to obtain some initial results on the performance and response of the system for different design parameters. The model is derived from 1D conservation of mass and momentum equations, including hydrodynamic effects, adiabatic air compressibility and turbine induced damping. Preliminary results indicate that the mass transfer has an important effect both on the resonance amplification and on the phase between the motion of the two columns. Simulation results are presented for the system performance over several weir heights and regular wave conditions. Further work will continue in design optimization and experimental validation of the proposed model.
Gerl, Armin, Becher, Stefan.  2019.  Policy-Based De-Identification Test Framework. 2019 IEEE World Congress on Services (SERVICES). 2642-939X:356—357.
Protecting privacy of individuals is a basic right, which has to be considered in our data-centered society in which new technologies emerge rapidly. To preserve the privacy of individuals de-identifying technologies have been developed including pseudonymization, personal privacy anonymization, and privacy models. Each having several variations with different properties and contexts which poses the challenge for the proper selection and application of de-identification methods. We tackle this challenge proposing a policy-based de-identification test framework for a systematic approach to experimenting and evaluation of various combinations of methods and their interplay. Evaluation of the experimental results regarding performance and utility is considered within the framework. We propose a domain-specific language, expressing the required complex configuration options, including data-set, policy generator, and various de-identification methods.
Pasyeka, Mykola, Sheketa, Vasyl, Pasieka, Nadiia, Chupakhina, Svitlana, Dronyuk, Ivanna.  2019.  System Analysis of Caching Requests on Network Computing Nodes. 2019 3rd International Conference on Advanced Information and Communications Technologies (AICT). :216–222.

A systematic study of technologies and concepts used for the design and construction of distributed fail-safe web systems has been conducted. The general principles of the design of distributed web-systems and information technologies that are used in the design of web-systems are considered. As a result of scientific research, it became clear that data backup is a determining attribute of most web systems serving. Thus, the main role in building modern web systems is to scaling them. Scaling in distributed systems is used when performing a particular operation requires a large amount of computing resources. There are two scaling options, namely vertical and horizontal. Vertical scaling is to increase the performance of existing components in order to increase overall productivity. However, for the construction of distributed systems, use horizontal scaling. Horizontal scaling is that the system is split into small components and placed on various physical computers. This approach allows the addition of new nodes to increase the productivity of the web system as a whole.

Neema, Himanshu, Vardhan, Harsh, Barreto, Carlos, Koutsoukos, Xenofon.  2019.  Web-Based Platform for Evaluation of Resilient and Transactive Smart-Grids. 2019 7th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES). :1–6.
Today's smart-grids have seen a clear rise in new ways of energy generation, transmission, and storage. This has not only introduced a huge degree of variability, but also a continual shift away from traditionally centralized generation and storage to distributed energy resources (DERs). In addition, the distributed sensors, energy generators and storage devices, and networking have led to a huge increase in attack vectors that make the grid vulnerable to a variety of attacks. The interconnection between computational and physical components through a largely open, IP-based communication network enables an attacker to cause physical damage through remote cyber-attacks or attack on software-controlled grid operations via physical- or cyber-attacks. Transactive Energy (TE) is an emerging approach for managing increasing DERs in the smart-grids through economic and control techniques. Transactive Smart-Grids use the TE approach to improve grid reliability and efficiency. However, skepticism remains in their full-scale viability for ensuring grid reliability. In addition, different TE approaches, in specific situations, can lead to very different outcomes in grid operations. In this paper, we present a comprehensive web-based platform for evaluating resilience of smart-grids against a variety of cyber- and physical-attacks and evaluating impact of various TE approaches on grid performance. We also provide several case-studies demonstrating evaluation of TE approaches as well as grid resilience against cyber and physical attacks.