Showing papers by "Gyorgy Dan published in 2012"

Network-Aware Mitigation of Data Integrity Attacks on Power System State Estimation

Abstract: Critical power system applications like contingency analysis and optimal power flow calculation rely on the power system state estimator. Hence the security of the state estimator is essential for the proper operation of the power system. In the future more applications are expected to rely on it, so that its importance will increase. Based on realistic models of the communication infrastructure used to deliver measurement data from the substations to the state estimator, in this paper we investigate the vulnerability of the power system state estimator to attacks performed against the communication infrastructure. We define security metrics that quantify the importance of individual substations and the cost of attacking individual measurements. We propose approximations of these metrics, that are based on the communication network topology only, and we compare them to the exact metrics. We provide efficient algorithms to calculate the security metrics. We use the metrics to show how various network layer and application layer mitigation strategies, like single and multi-path routing and data authentication, can be used to decrease the vulnerability of the state estimator. We illustrate the efficiency of the algorithms on the IEEE 118 and 300 bus benchmark power systems.

Non-cooperative RSU deployment in vehicular networks

Abstract: This work considers the issue of distributing contents to vehicles through roadside communication infrastructure. Within this scenario, this work studies the dynamics of infrastructure deployment by using game theoretic tools. A strategic game is used to model the case in which the operators perform their deployment decisions concurrently, whereas an extensive game is used to study the dynamics in case one operator is the deployment leader and moves first. The equilibria of the aforementioned games are then assessed as a function of several parameters (nominal infrastructure capacity, interference, vehicle flows). Simulations are used to validate the analytical findings.

Optimal power flow: Closing the loop over corrupted data

Abstract: Recently the power system state estimator was shown to be vulnerable to malicious deception attacks on the measurements, resulting in biased estimates. In this work we analyze the behavior of the Optimal Power Flow (OPF) algorithm in the presence of such maliciously biased estimates and the resulting consequences to the system operator. In particular, we characterize the set of attacks that may lead the operator to apply the erroneous OPF recommendation. Such characterization is used to improve a previously proposed security index by also considering the attack impact, which may be used for allocation and prioritization of protective measures. Additionally, we propose an analytical expression for the optimal solution of a simplified OPF problem with corrupted measurements. A small analytical example is discussed to illustrate and motivate our contributions.

Convergence in Player-Specific Graphical Resource Allocation Games

Abstract: As a model of distributed resource allocation in networked systems, we consider resource allocation games played over a influence graph. The influence graph models limited interaction between the players due to, e.g., the network topology: the payoff that an allocated resource yields to a player depends only on the resources allocated by her neighbors on the graph. We prove that pure strategy Nash equilibria (NE) always exist in graphical resource allocation games and we provide a linear time algorithm to compute equilibria. We show that these games do not admit a potential function: if there are closed paths in the influence graph then there can be best reply cycles. Nevertheless, we show that from any initial allocation of a resource allocation game it is possible to reach a NE by playing best replies and we provide a bound on the maximal number of update steps required. Furthermore we give sufficient conditions in terms of the influence graph topology and the utility structure under which best reply cycles do not exist. Finally we propose an efficient distributed algorithm to reach an equilibrium over an arbitrary graph and we illustrate its performance on different random graph topologies.

Cyber-security of SCADA systems

Abstract: After a general introduction of the VIKING EU FP7 project two specific cyber-attack mechanisms, which have been analyzed in the VIKING project, will be discussed in more detail. Firstly an attack and its consequences on the Automatic Generation Control (AGC) in a power system are investigated, and secondly the cyber security of State Estimators in SCADA systems is scrutinized.

Caching for BitTorrent-like P2P systems: a simple fluid model and its implications

Abstract: Peer-to-peer file-sharing systems are responsible for a significant share of the traffic between Internet service providers (ISPs) in the Internet. In order to decrease their peer-to-peer-related transit traffic costs, many ISPs have deployed caches for peer-to-peer traffic in recent years. We consider how the different types of peer-to-peer caches--caches already available on the market and caches expected to become available in the future--can possibly affect the amount of inter-ISP traffic. We develop a fluid model that captures the effects of the caches on the system dynamics of peer-to-peer networks and show that caches can have adverse effects on the system dynamics depending on the system parameters. We combine the fluid model with a simple model of inter-ISP traffic and show that the impact of caches cannot be accurately assessed without considering the effects of the caches on the system dynamics. We identify scenarios when caching actually leads to increased transit traffic. Motivated by our findings, we propose a proximity-aware peer-selection mechanism that avoids the increase of the transit traffic and improves the cache efficiency. We support the analytical results by extensive simulations and experiments with real BitTorrent clients.

Tradeoffs in cloud and peer-assisted content delivery systems

Abstract: With the proliferation of cloud services, cloud-based systems can become a cost-effective means of on-demand content delivery. In order to make best use of the available cloud bandwidth and storage resources, content distributors need to have a good understanding of the tradeoffs between various system design choices. In this work we consider a peer-assisted content delivery system that aims to provide guaranteed average download rate to its customers. We show that bandwidth demand peaks for contents with moderate popularity, and identify these contents as candidates for cloud-based service. We then consider dynamic content bundling (inflation) and cross-swarm seeding, which were recently proposed to improve download performance, and evaluate their impact on the optimal choice of cloud service use. We find that much of the benefits from peer seeding can be achieved with careful torrent inflation, and that hybrid policies that combine bundling and peer seeding often reduce the delivery costs by 20% relative to only using seeding. Furthermore, all these peer-assisted policies reduce the number of files that would need to be pushed to the cloud. Finally, we show that careful system design is needed if locality is an important criterion when choosing cloud-based service provisioning.

A longitudinal characterization of local and global bittorrent workload dynamics

Abstract: Workload characterization is important for understanding how systems and services are used in practice and to help identify design improvements. To better understand the longitudinal workload dynamics of chunk-based content delivery systems, this paper analyzes the BitTorrent usage as observed from two different vantage points. Using two simultaneously collected 48-week long traces, we analyze the differences in download characteristics and popularity dynamics observed locally at a university campus versus at a global scale. We find that campus users typically download larger files and are early adopters of new content, in the sense that they typically download files well before the time at which the global popularity of the files peak. The noticeable exception is music files, which the campus users are late to download. We also find that there typically is high churn in the set of files that are popular each week, both locally and globally, and that the most popular files peak significantly later than their release date. These findings provide insights that may improve the efficiency of content sharing locally, and thus increase the scalability of the global system.

Cache capacity allocation for BitTorrent-like systems to minimize inter-ISP traffic

Abstract: Many Internet service providers (ISPs) have deployed peer-to-peer (P2P) caches in their networks in order to decrease costly inter-ISP traffic. A P2P cache stores parts of the most popular contents locally, and if possible serves the requests of local peers to decrease the inter-ISP traffic. Traditionally, P2P cache resource management focuses on managing the storage resource of the cache so as to maximize the inter-ISP traffic savings. In this paper we show that when there are many overlays competing for the upload bandwidth of a P2P cache then in order to maximize the inter-ISP traffic savings the cache's upload bandwidth should be actively allocated among the overlays. We formulate the problem of P2P cache bandwidth allocation as a Markov decision process, and describe two approximations to the optimal cache bandwidth allocation policy. Based on the insights obtained from the approximate policies we propose SRP, a priority-based allocation policy for BitTorrent-like P2P systems. We use extensive simulations to evaluate the performance of the proposed policies, and show that cache bandwidth allocation can improve the inter-ISP traffic savings by up to 30 to 60 percent. We validate the results via BitTorrent experiments on Planet-lab.

Playout adaptation for peer-to-peer streaming systems under churn

Abstract: We address the problem of playout adaptation in peer-to-peer streaming systems. We propose two algorithms for playout adaptation: one coordinated and one distributed. The algorithms dynamically adapt the playback delay of the peers so that the playout miss ratio is maintained within a predefined interval. We validate the algorithms and evaluate their performance through simulations under various churn models. We show that playout adaptation is essential in peer-to-peer systems when the system size changes. At the same time, our results show that distributed adaptation performs well only if the peers in the overlay have similar playback delays. Thus, some form of coordination among the peers is necessary for distributed playout adaptation in peer-to-peer streaming systems.