Ransomware Detection Using Deep Learning in the SCADA System of Electric Vehicle Charging Station

Basnet, Manoj; Poudyal, Subash; Ali, Mohd. Hasan; Dasgupta, Dipankar

doi:10.1109/isgtlatinamerica52371.2021.9543031

Cited by 29 publications

(27 citation statements)

References 12 publications

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“…Consequently, Basnet et al furthered their study to create an IDS to detect FDI and DDoS attacks on photovoltaic controllers [32] whereas we detect oscillatory load attacks on the cyber-layer of the EV ecosystem. Moreover, in [33] the authors devised a ransomware detection mechanism while assuming that the ransomware can initiate DDoS and FDI attacks that might alter the state of charge thresholds. The detection mechanism is based on assembly instructions that are generated after the ransomware starts executing, whereas in [34], the authors proposed an early detection mechanism based on pre-attack (paranoiac) activity that the ransomware performs before executing.…”

Section: Related Workmentioning

confidence: 99%

“…The detection mechanism is based on assembly instructions that are generated after the ransomware starts executing, whereas in [34], the authors proposed an early detection mechanism based on pre-attack (paranoiac) activity that the ransomware performs before executing. In [33], the authors utilized 561 ransomware samples to train and test their deep-learning model. However, there are various classes/families, wherein [34] the authors collected about 3000 ransomware samples, which makes the data set created in [33] unrepresentative.…”

Section: Related Workmentioning

confidence: 99%

“…In [33], the authors utilized 561 ransomware samples to train and test their deep-learning model. However, there are various classes/families, wherein [34] the authors collected about 3000 ransomware samples, which makes the data set created in [33] unrepresentative.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

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“…The authors of Ref. [43] research ways of detecting ransomware within Supervisory Control and Data Acquisition (SCADA) systems for Electric Vehicle Charging Stations. They proposed a novel, scalable, and interoperable deep learning‐based ransomware detection framework that could be implemented at multiple vulnerable attack points.…”

Section: Background Informationmentioning

confidence: 99%

Cross‐layered distributed data‐driven framework for enhanced smart grid cyber‐physical security

et al. 2022

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Smart Grid (SG) research and development has drawn much attention from academia, industry and government due to the great impact it will have on society, economics and the environment. Securing the SG is a considerably significant challenge due the increased dependency on communication networks to assist in physical process control, exposing them to various cyber-threats. In addition to attacks that change measurement values using False Data Injection (FDI) techniques, attacks on the communication network may disrupt the power system's real-time operation by intercepting messages, or by flooding the communication channels with unnecessary data. Addressing these attacks requires a cross-layer approach. In this paper a cross-layered strategy is presented, called Cross-Layer Ensemble CorrDet with Adaptive Statistics(CECD-AS), which integrates the detection of faulty SG measurement data as well as inconsistent network inter-arrival times and transmission delays for more reliable and accurate anomaly detection and attack interpretation. Numerical results show that CECD-AS can detect multiple False Data Injections, Denial of Service (DoS) and Man In The Middle (MITM) attacks with a high F1-score compared to current approaches that only use SG measurement data for detection such as the traditional physics-based State Estimation, ECD-AS strategy and other machine learning classification-based detection schemes.

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“…Basnet et al [13] developed the novel DL-based ransomware detection architecture in the Supervisory control and data acquisition (SCADA) controlled electric vehicle charging station (EVCS) with the performance assessment of 3 DL systems, namely DNN, 1D-CNN, and LSTM-RNN. Nisa et al [14] developed a feature fusion system for integrating the feature extracting in pre-trained Inception-v3 and AlexNet DNN with features accomplished through segmentation-based fractal texture analysis (SFTA) of image expressive the malware code.…”

Section: Introductionmentioning

confidence: 99%

Artificial Algae Optimization with Deep Belief Network Enabled Ransomware Detection in IoT Environment

Duhayyim¹,

Mohamed²,

Alrowais³

et al. 2023

Computer Systems Science and Engineering

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The Internet of Things (IoT) has gained more popularity in research because of its large-scale challenges and implementation. But security was the main concern when witnessing the fast development in its applications and size. It was a dreary task to independently set security systems in every IoT gadget and upgrade them according to the newer threats. Additionally, machine learning (ML) techniques optimally use a colossal volume of data generated by IoT devices. Deep Learning (DL) related systems were modelled for attack detection in IoT. But the current security systems address restricted attacks and can be utilized outdated datasets for evaluations. This study develops an Artificial Algae Optimization Algorithm with Optimal Deep Belief Network (AAA-ODBN) Enabled Ransomware Detection in an IoT environment. The presented AAA-ODBN technique mainly intends to recognize and categorize ransomware in the IoT environment. The presented AAA-ODBN technique follows a three-stage process: feature selection, classification, and parameter tuning. In the first stage, the AAA-ODBN technique uses AAA based feature selection (AAA-FS) technique to elect feature subsets. Secondly, the AAA-ODBN technique employs the DBN model for ransomware detection. At last, the dragonfly algorithm (DFA) is utilized for the hyperparameter tuning of the DBN technique. A sequence of simulations is implemented to demonstrate the improved performance of the AAA-ODBN algorithm. The experimental values indicate the significant outcome of the AAA-ODBN model over other models.

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Ransomware Detection Using Deep Learning in the SCADA System of Electric Vehicle Charging Station

Cited by 29 publications

References 12 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

Cross‐layered distributed data‐driven framework for enhanced smart grid cyber‐physical security

Artificial Algae Optimization with Deep Belief Network Enabled Ransomware Detection in IoT Environment

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