Improving Detection of False Data Injection Attacks Using Machine Learning with Feature Selection and Oversampling

Kumar, Ajit; Saxena, Neetesh; Jung, Souhwan; Choi, Bong Jun

doi:10.3390/en15010212

Cited by 12 publications

(9 citation statements)

References 43 publications

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“…Table 2 and Fig. 3 show the results offered by the HDL-FDIAD model and other existing models on power dataset [19]. The experimental outcomes confirm that the proposed HDL-FDIAD model gained effectual outcomes on both the class labels.…”

Section: Resultsmentioning

confidence: 55%

Optimal Hybrid Deep Learning Enabled Attack Detection and Classification in IoT Environment

Alruwaili¹

2023

Computers, Materials &Amp; Continua

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The Internet of Things (IoT) paradigm enables end users to access networking services amongst diverse kinds of electronic devices. IoT security mechanism is a technology that concentrates on safeguarding the devices and networks connected in the IoT environment. In recent years, False Data Injection Attacks (FDIAs) have gained considerable interest in the IoT environment. Cybercriminals compromise the devices connected to the network and inject the data. Such attacks on the IoT environment can result in a considerable loss and interrupt normal activities among the IoT network devices. The FDI attacks have been effectively overcome so far by conventional threat detection techniques. The current research article develops a Hybrid Deep Learning to Combat Sophisticated False Data Injection Attacks detection (HDL-FDIAD) for the IoT environment. The presented HDL-FDIAD model majorly recognizes the presence of FDI attacks in the IoT environment. The HDL-FDIAD model exploits the Equilibrium Optimizer-based Feature Selection (EO-FS) technique to select the optimal subset of the features. Moreover, the Long Short Term Memory with Recurrent Neural Network (LSTM-RNN) model is also utilized for the purpose of classification. At last, the Bayesian Optimization (BO) algorithm is employed as a hyperparameter optimizer in this study. To validate the enhanced performance of the HDL-FDIAD model, a wide range of simulations was conducted, and the results were investigated in detail. A comparative study was conducted between the proposed model and the existing models. The outcomes revealed that the proposed HDL-FDIAD model is superior to other models.

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Section: Resultsmentioning

confidence: 55%

Optimal Hybrid Deep Learning Enabled Attack Detection and Classification in IoT Environment

Alruwaili¹

2023

Computers, Materials &Amp; Continua

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“…The proposed prepossessing of data and selection of the most promising features improve the performance of the DT model. The authors in [67] used minority oversampling and feature selection to improve the detection of FCIAs on power systems using machine learning. The authors in [68] showed that the performance of various ML models to detect FCIAs is dependent on pre-processing and feature selection.…”

Section: A Artificial Intelligence-based Techniquesmentioning

confidence: 99%

Command Injection Attacks in Smart Grids: A Survey

Usama,

Aman

2024

IEEE Open J. Ind. Applicat.

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Cybersecurity is important in the realization of various smart grid technologies. Several studies have been conducted to discuss different types of cyberattacks and provide their countermeasures. The False Command Injection Attack (FCIA) is considered one of the most critical attacks that have been studied. Various techniques have been proposed in the literature to detect FCIAs on different components of smart grids. The predominant focus of current surveys lies on FCIAs and detection techniques for such attacks. This paper presents a survey of existing works on FCIAs and classifies FCIAs in smart grids according to the targeted component. The impacts of FCIAs on smart grids are also discussed. Subsequently, this paper provides an extensive review of detection studies, categorizing them based on the type of detection technique employed.

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“…As a multi-label classifier, the CNN method was used to evaluate co-occurrence dependency, irrespective of the power flow computations that occur due to possible attacks. Kumar et al [15] examined the efficiency of ML techniques in identifying the FDIC. Specifically, the authors focused on two commonly-employed critical infrastructures, the water treatment plants and the power systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Coot Optimization with Deep Learning-Based False Data Injection Attack Recognition

Murthy¹,

Udayakumar²,

Alenezi³

et al. 2023

Computer Systems Science and Engineering

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The recent developments in smart cities pose major security issues for the Internet of Things (IoT) devices. These security issues directly result from inappropriate security management protocols and their implementation by IoT gadget developers. Cyber-attackers take advantage of such gadgets' vulnerabilities through various attacks such as injection and Distributed Denial of Service (DDoS) attacks. In this background, Intrusion Detection (ID) is the only way to identify the attacks and mitigate their damage. The recent advancements in Machine Learning (ML) and Deep Learning (DL) models are useful in effectively classifying cyber-attacks. The current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition (COADL-FDIAR) model for the IoT environment. The presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT environment. To accomplish this, the COADL-FDIAR model initially preprocesses the input data and selects the features with the help of the Chi-square test. To detect and classify false data injection attacks, the Stacked Long Short-Term Memory (SLSTM) model is exploited in this study. Finally, the COA algorithm effectively adjusts the SLTSM model's hyperparameters effectively and accomplishes a superior recognition efficiency. The proposed COADL-FDIAR model was experimentally validated using a standard dataset, and the outcomes were scrutinized under distinct aspects. The comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.

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Improving Detection of False Data Injection Attacks Using Machine Learning with Feature Selection and Oversampling

Cited by 12 publications

References 43 publications

Optimal Hybrid Deep Learning Enabled Attack Detection and Classification in IoT Environment

Optimal Hybrid Deep Learning Enabled Attack Detection and Classification in IoT Environment

Command Injection Attacks in Smart Grids: A Survey

Coot Optimization with Deep Learning-Based False Data Injection Attack Recognition

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