scholarly journals A Deep Learning-Based Classification Scheme for False Data Injection Attack Detection in Power System

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1459
Author(s):  
Yucheng Ding ◽  
Kang Ma ◽  
Tianjiao Pu ◽  
Xinying Wang ◽  
Ran Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Information And Communication Technologies ◽  
Test System ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Support Vector ◽  
Detection Accuracy ◽  
Detection Mechanism ◽  
False Data Injection ◽  
False Data Injection Attack

A smart grid improves power grid efficiency by using modern information and communication technologies. However, at the same time, due to the dependence on information technology and the deep integration of electrical components and computing information in cyber space, the system might become increasingly vulnerable to cyber-attacks. Among various emerging security problems, a false data injection attack (FDIA) is a new type of attack against the state estimation. In this article, a deep learning-based identification scheme is developed to detect and mitigate information corruption. The scheme implements a conditional deep belief network (CDBN) to analyze time-series input data and leverages captured features to detect the FDIA. The performance of our detection mechanism is validated by using the IEEE 14-bus test system for simulation. Different attack scenarios and parameters are set to demonstrate the feasibility and effectiveness of the developed scheme. Compared with the artificial neural network (ANN) and the support vector machine (SVM), the experimental analyses indicate that the results of our detection mechanism are better than those of the other two in terms of FDIA detection accuracy and robustness.

scholarly journals Utilising Deep Learning Techniques for Effective Zero-Day Attack Detection

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1684
Author(s):  
Hanan Hindy ◽  
Robert Atkinson ◽  
Christos Tachtatzis ◽  
Jean-Noël Colin ◽  
Ethan Bayne ◽  
...  
Keyword(s):  
Deep Learning ◽  
False Negative ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Support Vector ◽  
Detection Accuracy ◽  
Normal Behaviour ◽  
Complex Zero ◽  
Detection Systems ◽  
And Performance

Machine Learning (ML) and Deep Learning (DL) have been used for building Intrusion Detection Systems (IDS). The increase in both the number and sheer variety of new cyber-attacks poses a tremendous challenge for IDS solutions that rely on a database of historical attack signatures. Therefore, the industrial pull for robust IDSs that are capable of flagging zero-day attacks is growing. Current outlier-based zero-day detection research suffers from high false-negative rates, thus limiting their practical use and performance. This paper proposes an autoencoder implementation for detecting zero-day attacks. The aim is to build an IDS model with high recall while keeping the miss rate (false-negatives) to an acceptable minimum. Two well-known IDS datasets are used for evaluation—CICIDS2017 and NSL-KDD. In order to demonstrate the efficacy of our model, we compare its results against a One-Class Support Vector Machine (SVM). The manuscript highlights the performance of a One-Class SVM when zero-day attacks are distinctive from normal behaviour. The proposed model benefits greatly from autoencoders encoding-decoding capabilities. The results show that autoencoders are well-suited at detecting complex zero-day attacks. The results demonstrate a zero-day detection accuracy of 89–99% for the NSL-KDD dataset and 75–98% for the CICIDS2017 dataset. Finally, the paper outlines the observed trade-off between recall and fallout.

scholarly journals Deep learning based method for false data injection attack detection in AC smart islands

2020 ◽  
Vol 14 (24) ◽  
pp. 5756-5765
Author(s):  
Moslem Dehghani ◽  
Abdollah Kavousi-Fard ◽  
Morteza Dabbaghjamanesh ◽  
Omid Avatefipour
Keyword(s):  
Deep Learning ◽  
Attack Detection ◽  
False Data Injection ◽  
False Data Injection Attack

scholarly journals Deep Learning Based-Phishing Attack Detection

2019 ◽  
Vol 8 (3) ◽  
pp. 8428-8432
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Human Life ◽  
Rapid Development ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Support Vector

Due to the rapid development of the communication technologies and global networking, lots of daily human life activities such as electronic banking, social networks, ecommerce, etc are transferred to the cyberspace. The anonymous, open and uncontrolled infrastructure of the internet enables an excellent platform for cyber attacks. Phishing is one of the cyber attacks in which attackers open some fraudulent websites similar to the popular and legal websites to steal the user’s sensitive information. Machine learning techniques such as J48, Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB) and Artificial Neural Network (ANN) were widely to detect the phishing attacks. But, getting goodquality training data is one of the biggest problems in machine learning. So, a deep learning method called Deep Neural Network (DNN) is introduced to detect the phishing Uniform Resource Locators (URLs). Initially, a feature extractor is used to construct a 30-dimension feature vector based on URL-based features, HTML-based features and domain-based features. These features are given as input to the DNN classifier for phishing attack detection. It consists of one input layer, multiple hidden layers and one output layer. The multiple hidden layers in DNN try to learn high-level features in an incremental manner. Finally, the DNN returns a probability value which represent the phishing URLs and legitimate URLs. By using DNN the accuracy, precision and recall of phishing attack detection is improved.

scholarly journals False Data Injection Attack Detection in Power Systems Based on Cyber-Physical Attack Genes

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhaoyang Qu ◽  
Yunchang Dong ◽  
Nan Qu ◽  
Huashun Li ◽  
Mingshi Cui ◽  
...  
Keyword(s):  
Power Systems ◽  
Detection Method ◽  
Attack Detection ◽  
Fine Tuning ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Complex Data ◽  
False Data Injection ◽  
Physical Attack ◽  
False Data Injection Attack

In the process of the detection of a false data injection attack (FDIA) in power systems, there are problems of complex data features and low detection accuracy. From the perspective of the correlation and redundancy of the essential characteristics of the attack data, a detection method of the FDIA in smart grids based on cyber-physical genes is proposed. Firstly, the principle and characteristics of the FDIA are analyzed, and the concept of the cyber-physical FDIA gene is defined. Considering the non-functional dependency and nonlinear correlation of cyber-physical data in power systems, the optimal attack gene feature set of the maximum mutual information coefficient is selected. Secondly, an unsupervised pre-training encoder is set to extract the cyber-physical attack gene. Combined with the supervised fine-tuning classifier to train and update the network parameters, the FDIA detection model with stacked autoencoder network is constructed. Finally, a self-adaptive cuckoo search algorithm is designed to optimize the model parameters, and a novel attack detection method is proposed. The analysis of case studies shows that the proposed method can effectively improve the detection accuracy and effect of the FDIA on cyber-physical power systems.

scholarly journals Cyber Attack Detection based on Wavelet Singular Entropy in AC Smart Islands: False Data Injection Attack

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Moslem Dehghani ◽  
Mohammad Ghiasi ◽  
Taher Niknam ◽  
Abdollah Kavousi-Fard ◽  
Elham Tajik ◽  
...  
Keyword(s):  
Attack Detection ◽  
Cyber Attack ◽  
False Data Injection ◽  
False Data Injection Attack
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