Securing Power Distribution Grid Against Power Botnet Attacks

Wang, Lizhi; Pepin, Lynn; Li, Yan; Miao, Fei; Herzberg, Amir; Zhang, Peng

doi:10.1109/pesgm40551.2019.8974098

Cited by 7 publications

(7 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the same time, the COVID-19 pandemic caused substantial changes in the operations of bulk power systems and electricity markets. One of the most evident effects of the health crisis on EPS is the reduction in peak demand and consumed energy; electricity demand remains lower than typical expectations during the pandemic in many regions of 1 Prior work has used the names "manipulation of demand via IoT attack (MadIoT)" [8], "dynamic load-altering attack (DLAA)" [20], "coordinated load changing attack" [21], "demand-side cyberattacks" [22], and "power botnet attacks" [23]. Our terminology of load-changing attacks refers to such attack strategies aiming in a coordinated way to control IoT connected highwattage appliances and DERs.…”

Section: B Impact Of Covid-19 In Power Systemsmentioning

confidence: 99%

“…Then, these ratios are subtracted to compute the load impact index difference (LIID) for each individual bus in the system as seen in Eq. (23).…”

Section: B Description Of Case Studiesmentioning

confidence: 99%

“…The authors formulated the problem as a close-loop attack in which a dilettante adversary controls the changes in the compromised load based on the system frequency feedback. 1 Prior work has used the names “manipulation of demand via IoT attack (MadIoT)” [8] , “dynamic load-altering attack (DLAA)” [20] , “coordinated load changing attack” [21] , “demand-side cyberattacks” [22] , and “power botnet attacks” [23] . Our terminology of load-changing attacks refers to such attack strategies aiming in a coordinated way to control IoT connected high-wattage appliances and DERs.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

See 2 more Smart Citations

On the Feasibility of Load-Changing Attacks in Power Systems During the COVID-19 Pandemic

et al. 2021

View full text Add to dashboard Cite

The electric power grid is a complex cyberphysical energy system (CPES) in which information and communication technologies (ICT) are integrated into the operations and services of the power grid infrastructure. The growing number of Internet-of-things (IoT) high-wattage appliances, such as air conditioners and electric vehicles, being connected to the power grid, together with the high dependence of ICT and control interfaces, make CPES vulnerable to high-impact, low-probability load-changing cyberattacks. Moreover, the side-effects of the COVID-19 pandemic demonstrate a modification of electricity consumption patterns with utilities experiencing significant net-load and peak reductions. These unusual sustained low load demand conditions could be leveraged by adversaries to cause frequency instabilities in CPES by compromising hundreds of thousands of IoT-connected high-wattage loads. This article presents a feasibility study of the impacts of load-changing attacks on CPES during the low loading conditions caused by the lockdown measures implemented during the COVID-19 pandemic. The load demand reductions caused by the lockdown measures are analyzed using dynamic mode decomposition (DMD), focusing on the March-to-July 2020 period and the New York region as the most impacted time period and location in terms of load reduction due to the lockdowns being in full execution. Our feasibility study evaluates load-changing attack scenarios using real load consumption data from the New York Independent System Operator (NYISO) and shows that an attacker with sufficient knowledge and resources could be capable of producing frequency stability problems, with frequency excursions going up to 60.5 Hz and 63.4 Hz, when no mitigation measures are taken.

show abstract

Section: B Impact Of Covid-19 In Power Systemsmentioning

confidence: 99%

“…Then, these ratios are subtracted to compute the load impact index difference (LIID) for each individual bus in the system as seen in Eq. (23).…”

Section: B Description Of Case Studiesmentioning

confidence: 99%

Section: Related Work and Contributionsmentioning

confidence: 99%

See 1 more Smart Citation

On the Feasibility of Load-Changing Attacks in Power Systems During the COVID-19 Pandemic

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Finally, the Gini Impurity of the whole dataset Γ X in a given state, before or after one or more splits, is the weighted mean of the Gini Impurity of its subsets Γ j as shown in (2), where w j represents the j-th subset weight.…”

Section: ) Gini Importancementioning

confidence: 99%

“…O NE of the main current cyber-threats are botnets: networks of compromised and remote-controlled devices. These networks are dangerous because they can perform massive cyber-attacks, from Distributed Denial-of-Service (DDoS) or spam campaigns [1] to disrupting critical structures such as power distribution grids [2]. In 2018, the average annual cost for a company due to incidents related to botnets was $390,752 [3].…”

Section: Introductionmentioning

confidence: 99%

Efficient Detection of Botnet Traffic by Features Selection and Decision Trees

Velasco-Mata¹,

González-Castro

Fidalgo

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Botnets are one of the online threats with the most significant presence, causing billionaire losses to global economies. Nowadays, the increasing number of devices connected to the Internet makes it necessary to analyze extensive network traffic data. In this work, we focus on increasing the performance of botnet traffic classification by selecting those features that further increase the detection rate. For this purpose, we use two feature selection techniques, i.e., Information Gain and Gini Importance, which led to three pre-selected subsets of five, six and seven features. Then, we evaluate the three feature subsets and three models, i.e., Decision Tree, Random Forest and k-Nearest Neighbors. To test the performance of the three feature vectors and the three models, we generate two datasets based on the CTU-13 dataset, namely QB-CTU13 and EQB-CTU13. Finally, we measure the performance as the macro averaged F1 score over the computational time required to classify a sample. The results show that the highest performance is achieved by Decision Trees using a five feature set, which obtained a mean F1 score of 85% classifying each sample in an average time of 0.78 microseconds.

show abstract

Early detection of cyber–physical attacks on fast charging stations using machine learning considering vehicle-to-grid operation in microgrids

Warraich

Morsi

2023

Sustainable Energy, Grids and Networks

View full text Add to dashboard Cite

Securing Power Distribution Grid Against Power Botnet Attacks

Cited by 7 publications

References 6 publications

On the Feasibility of Load-Changing Attacks in Power Systems During the COVID-19 Pandemic

On the Feasibility of Load-Changing Attacks in Power Systems During the COVID-19 Pandemic

Efficient Detection of Botnet Traffic by Features Selection and Decision Trees

Early detection of cyber–physical attacks on fast charging stations using machine learning considering vehicle-to-grid operation in microgrids

Contact Info

Product

Resources

About