False Data Injection Attacks Detection with Deep Belief Networks in Smart Grid

Wei, Lei; Dong-huai, Gao; Luo, Cheng

doi:10.1109/cac.2018.8623514

Cited by 49 publications

(23 citation statements)

References 2 publications

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“…For example, Tzortzis and Likas (2007) stated that spam is an unexpected message which contains inappropriate information and first applied to fit DBNs for spam detection. In another paper, Wei et al (2018) proposed a DBN-based method to identify false data injection attacks in the smart grid. They demonstrated that the DBN-based method achieves a better result than the traditional SVM-based approach.…”

Section: Generative Model For Detecting Misinformationmentioning

confidence: 99%

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Islam

Liu

Wang

et al. 2020

Soc. Netw. Anal. Min.

196

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Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has become an inseparable part of our daily lives. It is considered as a convenient platform for users to share personal messages, pictures, and videos. However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation. Besides, spreading the massive amount of misinformation in social networks has become a global risk. Therefore, misinformation detection (MID) in social networks has gained a great deal of attention and is considered an emerging area of research interest. We find that several studies related to MID have been studied to new research problems and techniques. While important, however, the automated detection of misinformation is difficult to accomplish as it requires the advanced model to understand how related or unrelated the reported information is when compared to real information. The existing studies have mainly focused on three broad categories of misinformation: false information, fake news, and rumor detection. Therefore, related to the previous issues, we present a comprehensive survey of automated misinformation detection on (i) false information, (ii) rumors, (iii) spam, (iv) fake news, and (v) disinformation. We provide a state-of-the-art review on MID where deep learning (DL) is used to automatically process data and create patterns to make decisions not only to extract global features but also to achieve better results. We further show that DL is an effective and scalable technique for the state-of-the-art MID. Finally, we suggest several open issues that currently limit real-world implementation and point to future directions along this dimension.

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Section: Generative Model For Detecting Misinformationmentioning

confidence: 99%

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Islam

Liu

Wang

et al. 2020

Soc. Netw. Anal. Min.

196

View full text Add to dashboard Cite

show abstract

“…The proposed attack model focused on the data collecting and the motion control instructions. When the attack vector satisfied a = Hc, the attack model could bypass the classical attack detection approaches [28]. The attack model is defined as:…”

Section: False Data Injection Attackmentioning

confidence: 99%

“…In this section, the performance of the OCSVM is tested. As the proposed OCSVM approach is a kind of classification algorithm, the other two machine learning based methods were selected for the comparisons, which were called the semi-supervised machine learning method [22] and the boosting based machine learning method [28].…”

Section: Performance Of the Anomaly Detection Modelmentioning

confidence: 99%

A Two Stage Intrusion Detection System for Industrial Control Networks Based on Ethernet/IP

Wang

Song

2019

Electronics

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Standard Ethernet (IEEE 802.3 and the TCP/IP protocol suite) is gradually applied in industrial control system (ICS) with the development of information technology. It breaks the natural isolation of ICS, but contains no security mechanisms. An improved intrusion detection system (IDS), which is strongly correlated to specific industrial scenarios, is necessary for modern ICS. On one hand, this paper outlines three kinds of attack models, including infiltration attacks, creative forging attacks, and false data injection attacks. On the other hand, a two stage IDS is proposed, which contains a traffic prediction model and an anomaly detection model. The traffic prediction model, which is based on the autoregressive integrated moving average (ARIMA), can forecast the traffic of the ICS network in the short term and detect infiltration attacks precisely according to the abnormal changes in traffic patterns. Furthermore, the anomaly detection model, using a one class support vector machine (OCSVM), is able to detect malicious control instructions by analyzing the key field in Ethernet/IP packets. The confusion matrix is selected to testify to the effectiveness of the proposed method, and two other innovative IDSs are used for comparison. The experiment results show that the proposed two stage IDS in this paper has an outstanding performance in detecting infiltration attacks, forging attacks, and false data injection attacks compared with other IDSs.

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“…The proposed attack model is focusing on the data collecting and the motion control instructions. When the attack vector satisfies a = Hc, the attack model can bypass the classical attack detection approaches [28]. The attack model is defined as:…”

Section: False Data Injection Attackmentioning

confidence: 99%

“…In this section, the performance of the OCSVM is tested. As the proposed OCSVM approach is the kind of classification algorithm, the other two machine learning based methods are selected as the comparisons, which are called Semi-supervised machine learning method [22] and the Boosting based machine learning method [28].…”

Section: Performance Of the Traffic Prediction Modelmentioning

confidence: 99%

A Hierarchical Intrusion Detection System for Industrial Control Networks based on EtherNet/IP

Yu¹,

Wang²,

Song³

2019

Preprint

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Standard Ethernet (IEEE 802.3 and the TCP/IP protocol suite) is gradually applied in industrial control system (ICS) with the development of information technology. It breaks the natural isolation of ICS, but contains no security mechanism. A modified intrusion detection system (IDS), which is strongly correlated to specific industrial scenario, is necessary for modern ICS. On the one hand, this paper outlines attack models, including infiltration attacks and our creative forging attack. On the other hand, we proposes a hierarchical IDS, which contains a traffic prediction model and an anomaly detection model. The traffic prediction model, which is based on autoregressive integrated moving average (ARIMA), can forecast the traffic of ICS network in the short term and precisely detect the infiltration attacks according to abnormal changes in traffic pattern. The anomaly detection model using one-class support vector machine (OCSVM) is able to detect malicious control instructions by analyzing the key field in EtherNet/IP packets. The experimental results show that the hierarchical IDS has an outstanding performance in detecting infiltration attacks and forging attack compared with other two innovative IDSs.

show abstract

False Data Injection Attacks Detection with Deep Belief Networks in Smart Grid

Cited by 49 publications

References 2 publications

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Deep learning for misinformation detection on online social networks: a survey and new perspectives

A Two Stage Intrusion Detection System for Industrial Control Networks Based on Ethernet/IP

A Hierarchical Intrusion Detection System for Industrial Control Networks based on EtherNet/IP

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