Edge-Based Detection and Localization of Adversarial Oscillatory Load Attacks Orchestrated by Compromised Ev Charging Stations

Sarieddine, Khaled; Sayed, Mohammad Ali; Torabi, Sadegh; Atallah, Ribal; Assi, Chadi

doi:10.2139/ssrn.4200143

Cited by 2 publications

(6 citation statements)

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“…-Square wave: synchronizing the compromised load and switching them between on and off [36,16]. This attack can be made stealthier by distributing the switching behavior on multiple EVCSs to reduce the number of events per EVCS.…”

Section: Oscillatory Attack Vectormentioning

confidence: 99%

“…From a grid perspective, oscillatory EV loads can be manipulated to have lower power factors [39], thus entailing a larger impact compared to residential loads [8]. Oscillatory load attacks do not require huge loads or injections to cause abnormal behavior on the power grid [16]. Even when the load is not large enough to cause generator tripping, a sustained switching attack can cause frequency and voltage oscillations, which in turn damages the turbines due to the constant acceleration and deceleration [8].…”

Section: Oscillatory Attack Vectormentioning

confidence: 99%

“…• The mobile application which is the component responsible and the enabler for the commercialization of the EVCS ecosystem could be used by the adversary by leveraging the lack of end-end authentication between the user and his vehicle [16], allowing the adversary to opportunistically take advantage of connected vehicles to the charging station.…”

Section: Threat Modelmentioning

confidence: 99%

“…For instance, the adversary can command a swarm of EVCSs to start simultaneous charging operations and then stop them repeatedly to impact the stability of the power grid by altering the generator's speed, tripping power lines, overloading the grid, and/or creating frequency instability [8,15]. In addition to the possible physical impact, such attacks can also impact the power grid's efficiency and operational costs (e.g., line losses and generation costs) [16,15]. Considering the costly physical and financial implications of large-scale cyber attacks against the power grid, there is a need for effective detection mechanisms on different levels of the multi-layered heterogeneous ecosystem [17] to improve grid resiliency and ensure fault-tolerance.…”

Section: Introductionmentioning

confidence: 99%

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Edge-Based Detection and Localization of Adversarial Oscillatory Load Attacks Orchestrated By Compromised EV Charging Stations

Sarieddine¹,

Sayed²,

Torabi³

et al. 2023

Preprint

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Recent reports indicate that Electric Vehicle Charging Stations (EVCS) are susceptible to remote exploitation through their vulnerable software/cyber components. More importantly, compromised EVCS can be leveraged to perform coordinated oscillatory load attacks against the interconnected power grid, leading to power grid instability, increased operational costs, and power line tripping. In this paper, we investigate an edge-based approach for the detection and localization of coordinated oscillatory load attacks initiated by exploited EV charging stations against the power grid. We rely on the behavioral characteristics of the power grid in the presence of interconnected EVCS while combining cyber and physical layer features to implement deep learning algorithms for the effective detection of oscillatory load attacks at the EVCS. We evaluate the proposed detection approach by building a real-time test bed to synthesize benign and malicious data, which was generated by analyzing real-life EV charging data collected during recent years. The results demonstrate the effectiveness of the implemented approach with the Convolutional Long-Short Term Memory model producing optimal classification accuracy (99.4%). Moreover, our analysis results shed light on the impact of such detection mechanisms towards building resiliency into different levels of the EV charging ecosystem while allowing power grid operators to localize attacks and take further mitigation measures. Specifically, we managed to decentralize the detection mechanism of oscillatory load attacks and create an effective alternative for operator-centric mechanisms to mitigate multi-operator and MitM oscillatory load attacks against the power grid. Finally, we leverage the created test bed to evaluate a distributed mitigation technique, which can be deployed on public/private charging stations to average out the impact of oscillatory load attacks while allowing the power system to recover smoothly within 1 second with minimal overhead.

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Section: Oscillatory Attack Vectormentioning

confidence: 99%

Section: Oscillatory Attack Vectormentioning

confidence: 99%

Section: Threat Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Edge-Based Detection and Localization of Adversarial Oscillatory Load Attacks Orchestrated By Compromised EV Charging Stations

Sarieddine¹,

Sayed²,

Torabi³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…It provides a range of functions, which include authentication, data storage and analysis, remote monitoring and control of charging sessions, and integration with billing and grid management systems. As a result, the use of cloud infrastructure in the operation of electric vehicle charging stations has become increasingly important as the adoption of EVs continues to grow [3]. In essence, cloud infrastructure helps ensure the seamless flow of data and will enable advanced functionalities, such as dynamic pricing, load management, and integration with renewable energy sources.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2024

IJCSIT

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The security of Electric Vehicle (EV) charging has gained momentum after the increase in the EV adoption in the past few years. Mobile applications have been integrated into EV charging systems that mainly use a cloud-based platform to host their services and data. Like many complex systems, cloud systems are susceptible to cyberattacks if proper measures are not taken by the organization to secure them. In this paper, we explore the security of key components in the EV charging infrastructure, including the mobile application and its cloud service. We conducted an experiment that initiated a Man in the Middle attack between an EV app and its cloud services. Our results showed that it is possible to launch attacks against the connected infrastructure by taking advantage of vulnerabilities that may have substantial economic and operational ramifications on the EV charging ecosystem. We conclude by providing mitigation suggestions and future research directions.

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Edge-Based Detection and Localization of Adversarial Oscillatory Load Attacks Orchestrated by Compromised Ev Charging Stations

Cited by 2 publications

References 0 publications

Edge-Based Detection and Localization of Adversarial Oscillatory Load Attacks Orchestrated By Compromised EV Charging Stations

Edge-Based Detection and Localization of Adversarial Oscillatory Load Attacks Orchestrated By Compromised EV Charging Stations

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