Biblio

DefendR is a Security operation used to block the access of the user to edit or overwrite the contents in our personal file that is stored in our system. This approach of applying a certain filter for the sensitive or sensitive data that are applicable exclusively in read-only mode. This is an improvisation of security for the personal data that restricts undo or redo related operations in the shared file. We use a mini-filter driver tool. Specifically, IRP (Incident Response Plan)-based I/O operations, as well as fast FSFilter callback activities, may additionally all be filtered with a mini-filter driver. A mini-filter can register a preoperation callback procedure, a postoperative Each of the I/O operations it filters is filtered by a callback procedure. By registering all necessary callback filtering methods in a filter manager, a mini-filter driver interfaces to the file system indirectly. When a mini-filter is loaded, the latter is a Windows file system filter driver that is active and connects to the file system stack.

Static security assessment is of great significance to ensure the stable transmission of electric power and steady operation of load. The scale of power system trends to expand due to the development of interconnected grid, and the security analysis of the entire network has become time-consuming. On the basis of synthesizing the efficiency and accuracy, a new method is developed. This method adopts a novel dynamic power flow (DPF) model considering the influence of governor deadband and amplitude-limit on the steady state quantitatively. In order to reduce the computation cost, a contingency screening algorithm based on binary search method is proposed. Static security assessment based on the proposed DPF models is applied to calculate the security margin constrained by severe contingencies. The ones with lower margin are chosen for further time-domain (TD) simulation analysis. The case study of a practical grid verifies the accuracy of the proposed model compared with the conventional one considering no governor nonlinearity. Moreover, the test of a practical grid in China, along with the TD simulation, demonstrates that the proposed method avoids massive simulations of all contingencies as well as provides detail information of severe ones, which is effective for security analysis of practical power grids.

Throughout the years, honeypots have been very useful in tracking down attackers and preventing different types of cyber attacks on a very large scale. It's been almost 3 decades since the discover of honeypots and still more than 80% of the companies rely on this system because of intrusion detection features and low false positive rate. But with time, the attackers tend to start discovering loopholes in the system. Hence it is very important to be up to date with the technology when it comes to protecting a computing device from the emerging cyber attacks. Timely advancements in the security model provided by the honeypots helps in a more efficient use of the resource and also leads to better innovations in that field. The following paper reviews different methods of honeypot network and also gives an insight about the problems that those techniques can face along with their solution. Further it also gives the detail about the most preferred solution among all of the listed techniques in the paper.

Named Data Networking, a future internet network architecture design that can change the network's perspective from previously host-centric to data-centric. It can reduce the network load, especially on the server part, and can provide advantages in multicast cases or re-sending of content data to users due to transmission errors. In NDN, interest messages are sent to the router, and if they are not immediately found, they will continue to be forwarded, resulting in a large load. NDNS or a DNS-Like Name Service for NDN is needed to know exactly where the content is to improve system performance. NDNS is a database that provides information about the zone location of the data contained in the network. In this study, a simulation was conducted to test the NDNS mechanism on the NDN network to support caching on the NDN network by testing various topologies with changes in the size of the content store and the number of nodes used. NDNS is outperform compared to NDN without NDNS for cache hit ratio and load parameters.

Magnetic soft robots are increasingly popular as they provide many advantages such as miniaturization and tetherless control that are ideal for applications inside the human body or in previously inaccessible locations.While non-magnetic elastomers have been extensively characterized and modelled for optimizing the fabrication of soft robots, a systematic material characterization of their magnetic counterparts is still missing. In this paper, commonly employed magnetic materials made out of Ecoflex™ 00-30 and Dragon Skin™ 10 with different concentrations of NdFeB microparticles were mechanically and magnetically characterized. The magnetic materials were evaluated under uniaxial tensile testing and their behavior analyzed through linear and hyperelastic model comparison. To determine the corresponding magnetic properties, we present a method to determine the magnetization vector, and magnetic remanence, by means of a force and torque load cell and large reference permanent magnet; demonstrating a high level of accuracy. Furthermore, we study the influence of varied magnitude impulse magnetizing fields on the resultant magnetizations. In combination, by applying improved, material-specific mechanical and magnetic properties to a 2-segment discrete magnetic robot, we show the potential to reduce simulation errors from 8.5% to 5.4%.

Demand response (DR) is one of the most economical methods for peak demand reduction, renewable energy integration and ancillary service support. Residential electrical energy consumption takes approximately 33% of the total electricity usage and hence has great potentials in DR applications. However, residential DR encounters various challenges such as small individual magnitude, stochastic consuming patterns and privacy issues. In this study, we propose a stochastic optimal mechanism to tackle these issues and try to reveal the benefits from residential DR implementation. Stochastic residential load (SRL) models, a generation cost prediction (GCP) model and a stochastic optimal load aggregation (SOLA) model are developed. A set of uniformly distributed scalers is introduced into the SOLA model to efficiently avoid the peak demand rebound problem in DR applications. The SOLA model is further transformed into a deterministic LP model. Time-of-Use (TOU) tariff is adopted as the price structure because of its similarity and popularity. Case studies show that the proposed mechanism can significantly reduce the peak-to-average power ratio (PAPR) of the load profile as well as the electrical energy cost. Furthermore, the impacts of consumers' participation levels in the DR program are investigated. Simulation results show that the 50% participation level appears as the best case in terms system stability. With the participation level of 80%, consumers' electrical energy cost is minimized. The proposed mechanism can be used by a residential load aggregator (LA) or a utility to plan a DR program, predict its impacts, and aggregate residential loads to minimize the electrical energy cost.

Smart meters (SMs) play a pivotal rule in the smart grid by being able to report the electricity usage of consumers to the utility provider (UP) almost in real-time. However, this could leak sensitive information about the consumers to the UP or a third-party. Recent works have leveraged the availability of energy storage devices, e.g., a rechargeable battery (RB), in order to provide privacy to the consumers with minimal additional energy cost. In this paper, a privacy-cost management unit (PCMU) is proposed based on a model-free deep reinforcement learning algorithm, called deep double Q-learning (DDQL). Empirical results evaluated on actual SMs data are presented to compare DDQL with the state-of-the-art, i.e., classical Q-learning (CQL). Additionally, the performance of the method is investigated for two concrete cases where attackers aim to infer the actual demand load and the occupancy status of dwellings. Finally, an abstract information-theoretic characterization is provided.

The reform of electricity marketization has bred multiple market agents. In order to maximize the total social benefits on the premise of ensuring the security of the system and taking into account the interests of multiple market agents, a bi-level optimal allocation model of distribution network with multiple agents participating is proposed. The upper level model considers the economic benefits of energy and service providers, which are mainly distributed power investors, energy storage operators and distribution companies. The lower level model considers end-user side economy and actively responds to demand management to ensure the highest user satisfaction. The K-means multi scenario analysis method is used to describe the time series characteristics of wind power, photovoltaic power and load. The particle swarm optimization (PSO) algorithm is used to solve the bi-level model, and IEEE33 node system is used to verify that the model can effectively consider the interests of multiple agents while ensuring the security of the system.

The growing complexity of modern electronic systems often leads to the design of more sophisticated power delivery networks (PDNs). Similar to other system-level shared resources, the on-board PDN unintentionally introduces side channels across design layers and voltage domains, despite the fact that PDNs are not part of the functional design. Recent work have demonstrated that exploitation of the side channel can compromise the system security (i.e. information leakage and fault injection). In this work, we systematically investigate the PDN-based side channel as well as the countermeasures. To facilitate our goal, we develop PowerScout, a security-oriented PDN simulation framework that unifies the modeling of different PDN-based side-channel attacks. PowerScout performs fast nodal analysis of complex PDNs at the system level to quantitatively evaluate the severity of side-channel vulnerabilities. With the support of PowerScout, for the first time, we validate PDN side-channel attacks in literature through simulation results. Further, we are able to quantitatively measure the security impact of PDN parameters and configurations. For example, towards information leakage, removing near-chip capacitors can increase intra-chip information leakage by a maximum of 23.23dB at mid-frequency and inter-chip leakage by an average of 31.68dB at mid- and high-frequencies. Similarly, the optimal toggling frequency and duty cycle are derived to achieve fault injection attacks with higher success rate and more precise control.

Cyber-power resiliency analysis of the distribution system is becoming critical with increase in adverse cyberevents. Distribution network operators need to assess and analyze the resiliency of the system utilizing the analytical tool with a carefully designed visualization and be driven by data and model-based analytics. This work introduces the Cyber-Physical Security Assessment Metric (CP-SAM) visualization tool to assist operators in ensuring the energy supply to critical loads during or after a cyber-attack. CP-SAM also provides decision support to operators utilizing measurement data and distribution power grid model and through well-designed visualization. The paper discusses the concepts of cyber-physical resiliency, software design considerations, open-source software components, and use cases for the tool to demonstrate the implementation and importance of the developed tool.

In this paper, a planning method of transportation-power coupled system based on road expansion model is proposed. First of all, based on the Wardrop equilibrium state, the traffic flow is distributed, to build the road expansion model and complete the traffic network modeling. It is assumed that the road charging demand is directly proportional to the road traffic flow, and the charging facilities will cause a certain degree of congestion on the road. This mutual influence relationship to establish a coupling system of transportation network and power network is used for the planning. In the planning method, the decision variables include the location of charging facilities, the setting of energy storage systems and the road expansion scheme. The planning goal is to minimize the investment cost and operation cost. The CPLEX solver is used to solve the mixed integer nonlinear programming problem. Finally, the simulation analysis is carried out to verify the validity and feasibility of the planning method, which can comprehensively consider the road expansion cost and travel time cost, taking a coupled system of 5-node traffic system and IEEE14 node distribution network as example.

The progressive expansion of renewable energies, especially wind power plants being promoted in Germany as part of the energy transition, places new demands on the transmission grid. As an alternative to grid expansion, sector coupling of the gas and electricity sector through Power-to-Gas (PtG) technology is seen as a great opportunity to make the energy transmission more flexible and reliable in the future as well as make use of already existing gas infrastructure. In this paper, PtG plants are dimensioned and placed in a model of the German transmission grid. Time series based load flow calculations are performed allowing conclusions about the line loading for the exemplary year 2016.

The completion and the correction of measurement data are the foundation of the ubiquitous power internet of things construction. However, data missing may occur during the data transporting process. Therefore, a model of missing load situation reconstruction based on the generative adversarial networks is proposed in this paper to overcome the disadvantage of depending on data of other relevant factors in conventional methods. Through the unsupervised training, the proposed model can automatically learn the complex features of loads that are difficult to model explicitly to fill the incomplete load data without using other relevant data. Meanwhile, a method of online correction is put forward to improve the robustness of the reconstruction model in different scenarios. The proposed method is fully data-driven and contains no explicit modeling process. The test results indicate that the proposed algorithm is well-matched for the various scenarios, including the discontinuous missing load reconstruction and the continuous missing load reconstruction even massive data missing. Specifically, the reconstruction error rate of the proposed algorithm is within 4% under the absence of 50% load data.

Traditionally, the management of power distribution networks relies on the centralized implementation of the optimal power flow and, in particular, the minimization of the generation cost and transmission losses. Nevertheless, the increasing penetration of both renewable energy sources and independent players such as ancillary service providers in modern networks have made this centralized framework inadequate. Against this background, we propose a noncooperative game-theoretic framework for optimally controlling energy storage systems (ESSs) in power distribution networks. Specifically, in this paper we address a power grid model that comprehends traditional loads, distributed generation sources and several independent energy storage providers, each owning an individual ESS. Through a rolling-horizon approach, the latter participate in the grid optimization process, aiming both at increasing the penetration of distributed generation and leveling the power injection from the transmission grid. Our framework incorporates not only economic factors but also grid stability aspects, including the power flow constraints. The paper fully describes the distribution grid model as well as the underlying market hypotheses and policies needed to force the energy storage providers to find a feasible equilibrium for the network. Numerical experiments based on the IEEE 33-bus system confirm the effectiveness and resiliency of the proposed framework.

This paper presents a user-friendly design method for accurately sizing the distributed energy resources of a stand-alone microgrid to meet the critical load demands of a military, commercial, industrial, or residential facility when the utility power is not available. The microgrid combines renewable resources such as photovoltaics (PV) with an energy storage system to increase energy security for facilities with critical loads. The design tool's novelty includes compliance with IEEE standards 1562 and 1013 and addresses resilience, which is not taken into account in existing design methods. Several case studies, simulated with a physics-based model, validate the proposed design method. Additionally, the design and the simulations were validated by 24-hour laboratory experiments conducted on a microgrid assembled using commercial off the shelf components.

Transactive Energy (TE) is an emerging discipline that utilizes economic and control techniques for operating and managing the power grid effectively. Distributed Energy Resources (DERs) represent a fundamental shift away from traditionally centrally managed energy generation and storage to one that is rather distributed. However, integrating and managing DERs into the power grid is highly challenging owing to the TE implementation issues such as privacy, equity, efficiency, reliability, and security. The TE market structures allow utilities to transact (i.e., buy and sell) power services (production, distribution, and storage) from/to DER providers integrated as part of the grid. Flexible power pricing in TE enables power services transactions to dynamically adjust power generation and storage in a way that continuously balances power supply and demand as well as minimize cost of grid operations. Therefore, it has become important to analyze various market models utilized in different TE applications for their impact on above implementation issues.In this demo, we show-case the Transactive Energy Simulation and Analysis Toolsuite (TE-SAT) with its three publicly available design studios for experimenting with TE markets. All three design studios are built using metamodeling tool called the Web-based Graphical Modeling Environment (WebGME). Using a Git-like storage and tracking backend server, WebGME enables multi-user editing on models and experiments using simply a web-browser. This directly facilitates collaboration among different TE stakeholders for developing and analyzing grid operations and market models. Additionally, these design studios provide an integrated and scalable cloud backend for running corresponding simulation experiments.

The traditional reactive web caching system is getting less popular day by day due to its inefficiency in handling the overwhelming requests for multimedia content. An intelligent web caching system intends to take optimal cache decisions by predicting future popular contents (FPC) proactively. In recent years, a few approaches have proposed some intelligent caching system where they were concerned about proactive caching. Those works intensified the importance of FPC prediction using the prediction models. However, only FPC prediction may not help to get the optimal solution in every scenario. In this paper, a technique named IntellCache has been proposed that increases the caching efficiency by taking a cache decision i.e. content storing decision before storing the predicted FPC. Different deep learning models such as- multilayer perceptron (MLP), Long short-term memory (LSTM) of Recurrent Neural Network (RNN) and ConvLSTM a combination of LSTM and Convolutional Neural Network (CNN) are compared to identify the most efficient model for FPC. The information on the contents of 18 years from the MovieLens data repository has been mined to evaluate the proposed approach. Results show that this proposed scheme outperforms previous solutions by achieving a higher cache hit ratio and lower average delay and thus, ensures users' satisfaction.

The ability to advance the state of the art in automated cybersecurity protections for industrial control systems (ICS) has as a prerequisite of understanding the trade-off space. That is, to enable a cyber feedback loop in a control system environment you must first consider both the security mitigation available, the benefits and the impacts to the control system functionality when the mitigation is used. More damaging impacts could be precipitated that the mitigation was intended to rectify. This paper details networked ICS that controls a simulation of the frequency response represented with the swing equation. The microgrid loads and base generation can be balanced through the control of an emulated battery and power inverter. The simulated plant, which is implemented in Raspberry Pi computers, provides an inexpensive platform to realize the physical effects of cyber attacks to show the trade-offs of available mitigating actions. This network design can include a commercial ICS controller and simple plant or emulated plant to introduce real world implementation of feedback controls, and provides a scalable, physical effects measurable microgrid for cyber resilience analysis (SPEMMCRA).

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.

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.

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.

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.

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.

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.

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.