5,334 research outputs found
On the Efficiency-vs-Security Tradeoff in the Smart Grid
The smart grid is envisioned to significantly enhance the efficiency of
energy consumption, by utilizing two-way communication channels between
consumers and operators. For example, operators can opportunistically leverage
the delay tolerance of energy demands in order to balance the energy load over
time, and hence, reduce the total operational cost. This opportunity, however,
comes with security threats, as the grid becomes more vulnerable to
cyber-attacks. In this paper, we study the impact of such malicious
cyber-attacks on the energy efficiency of the grid in a simplified setup. More
precisely, we consider a simple model where the energy demands of the smart
grid consumers are intercepted and altered by an active attacker before they
arrive at the operator, who is equipped with limited intrusion detection
capabilities. We formulate the resulting optimization problems faced by the
operator and the attacker and propose several scheduling and attack strategies
for both parties. Interestingly, our results show that, as opposed to
facilitating cost reduction in the smart grid, increasing the delay tolerance
of the energy demands potentially allows the attacker to force increased costs
on the system. This highlights the need for carefully constructed and robust
intrusion detection mechanisms at the operator.Comment: A shorter version appears in IEEE CDC 201
The Impact of Stealthy Attacks on Smart Grid Performance: Tradeoffs and Implications
The smart grid is envisioned to significantly enhance the efficiency of
energy consumption, by utilizing two-way communication channels between
consumers and operators. For example, operators can opportunistically leverage
the delay tolerance of energy demands in order to balance the energy load over
time, and hence, reduce the total operational cost. This opportunity, however,
comes with security threats, as the grid becomes more vulnerable to
cyber-attacks. In this paper, we study the impact of such malicious
cyber-attacks on the energy efficiency of the grid in a simplified setup. More
precisely, we consider a simple model where the energy demands of the smart
grid consumers are intercepted and altered by an active attacker before they
arrive at the operator, who is equipped with limited intrusion detection
capabilities. We formulate the resulting optimization problems faced by the
operator and the attacker and propose several scheduling and attack strategies
for both parties. Interestingly, our results show that, as opposed to
facilitating cost reduction in the smart grid, increasing the delay tolerance
of the energy demands potentially allows the attacker to force increased costs
on the system. This highlights the need for carefully constructed and robust
intrusion detection mechanisms at the operator.Comment: Technical report - this work was accepted to IEEE Transactions on
Control of Network Systems, 2016. arXiv admin note: substantial text overlap
with arXiv:1209.176
Uncovering Load-Altering Attacks Against N-1 Secure Power Grids: A Rare-Event Sampling Approach
Load-altering attacks targetting a large number of IoT-based high-wattage
devices (e.g., smart electric vehicle charging stations) can lead to serious
disruptions of power grid operations. In this work, we aim to uncover
spatiotemporal characteristics of LAAs that can lead to serious impact. The
problem is challenging since existing protection measures such as
security ensures that the power grid is naturally resilient to load changes.
Thus, strategically injected load perturbations that lead to network failure
can be regarded as \emph{rare events}. To this end, we adopt a rare-event
sampling approach to uncover LAAs distributed temporally and spatially across
the power network. The key advantage of this sampling method is the ability of
sampling efficiently from multi-modal conditional distributions with
disconnected support. Furthermore, we systematically compare the impacts of
static (one-time manipulation of demand) and dynamic (attack over multiple time
periods) LAAs. We perform extensive simulations using benchmark IEEE test
simulations. The results show (i) the superiority and the need for rare-event
sampling in the context of uncovering LAAs as compared to other sampling
methodologies, (ii) statistical analysis of attack characteristics and impacts
of static and dynamic LAAs, and (iii) cascade sizes (due to LAA) for different
network sizes and load conditions
Uncovering Load-Altering Attacks Against N-1 Secure Power Grids:A Rare-Event Sampling Approach
Load-altering attacks targetting a large number of IoT-based high-wattage devices (e.g., smart electric vehicle charging stations) can lead to serious disruptions of power grid operations. In this work, we aim to uncover spatiotemporal characteristics of LAAs that can lead to serious impact. The problem is challenging since existing protection measures such as security ensures that the power grid is naturally resilient to load changes. Thus, strategically injected load perturbations that lead to network failure can be regarded as \emph{rare events}. To this end, we adopt a rare-event sampling approach to uncover LAAs distributed temporally and spatially across the power network. The key advantage of this sampling method is the ability of sampling efficiently from multi-modal conditional distributions with disconnected support. Furthermore, we systematically compare the impacts of static (one-time manipulation of demand) and dynamic (attack over multiple time periods) LAAs. We perform extensive simulations using benchmark IEEE test simulations. The results show (i) the superiority and the need for rare-event sampling in the context of uncovering LAAs as compared to other sampling methodologies, (ii) statistical analysis of attack characteristics and impacts of static and dynamic LAAs, and (iii) cascade sizes (due to LAA) for different network sizes and load conditions
Smart Grid Technologies in Europe: An Overview
The old electricity network infrastructure has proven to be inadequate, with respect to modern challenges such as alternative energy sources, electricity demand and energy saving policies. Moreover, Information and Communication Technologies (ICT) seem to have reached an adequate level of reliability and flexibility in order to support a new concept of electricity networkāthe smart grid. In this work, we will analyse the state-of-the-art of smart grids, in their technical, management, security, and optimization aspects. We will also provide a brief overview of the regulatory aspects involved in the development of a smart grid, mainly from the viewpoint of the European Unio
Investigating the Impacts of Cyber-Attacks on Pricing Data of Home Energy Management Systems in Demand Response Programs
Ā© 2018 IEEE. Provision of security involves protecting lives and properties, and properties in this context include data and services. This paper investigates the impact of cyber-attacks on load scheduling applications by simulating various possible modes for these attacks while observing possible effects on the users. The attack modes used are in the form of denial of service (DoS) and phishing attacks whereby the attacker is able to interfere with data intake to the Home Energy Management Systems (HEMS) or a modification of critical data to the HEMS. The dynamic pricing information and load profile data is the target here although other types of data utilized by the central controller for load scheduling purposes can also be targeted. The test-bed uses load scheduling applications based on genetic algorithm optimization. Results show the impact on optimized load profiles and how they can discourage active demand response participation if such attacks are not properly managed.Published versio
Demand-Side Threats to Power Grid Operations from IoT-Enabled Edge
The growing adoption of Internet-of-Things (IoT)-enabled energy smart
appliances (ESAs) at the consumer end, such as smart heat pumps, electric
vehicle chargers, etc., is seen as key to enabling demand-side response (DSR)
services. However, these smart appliances are often poorly engineered from a
security point of view and present a new threat to power grid operations. They
may become convenient entry points for malicious parties to gain access to the
system and disrupt important grid operations by abruptly changing the demand.
Unlike utility-side and SCADA assets, ESAs are not monitored continuously due
to their large numbers and the lack of extensive monitoring infrastructure at
consumer sites. This article presents an in-depth analysis of the demand side
threats to power grid operations including (i) an overview of the
vulnerabilities in ESAs and the wider risk from the DSR ecosystem and (ii) key
factors influencing the attack impact on power grid operations. Finally, it
presents measures to improve the cyber-physical resilience of power grids,
putting them in the context of ongoing efforts from the industry and regulatory
bodies worldwide
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