Deep Learning Based Multi-Channel Intelligent Attack Detection for Data Security

Jiang, Feng; Fu, Yunsheng; Gupta, Brij B.; Liang, Yonghui; Rho, Seungmin; Lou, Fang; Meng, Fanzhi; Tian, Zhihong

doi:10.1109/tsusc.2018.2793284

Cited by 190 publications

(82 citation statements)

References 40 publications

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“…The STL technique consists of two stages for the classification, including, 1) feature representation is learned from a large collection of unlabeled data and 2) apply the first stage to labeled data for the classification task. The recurrent neural network algorithms are used by IDS models, such as long short-term memory (LSTM) [44], [49], forward propagation and back propagation (FPBP) [45], and gated recurrent unit (GRU) [47]. Therefore, many existing IDS systems utilize three datasets, including, KDD Cup 1999 dataset, NSL-KDD dataset, and UNSW-NB15 dataset, which are outdated and of very limited practical value for a modern IDS.…”

Section: Evaluation Resultsmentioning

confidence: 99%

DeepCoin: A Novel Deep Learning and Blockchain-Based Energy Exchange Framework for Smart Grids

Ferrag

Μαγλαράς

2020

IEEE Trans. Eng. Manage.

270

121

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In this paper, we propose a novel deep learning and blockchain-based energy framework for Smart Grids, entitled DeepCoin. The DeepCoin framework uses two schemes, a blockchain-based scheme and a deep learning-based scheme. The blockchain-based scheme consists of five phases; setup phase, agreement phase, creating a block phase and consensus-making phase, and view change phase. It incorporates a novel reliable peer-to-peer energy system that is based on the practical Byzantine fault tolerance algorithm and it achieves high throughput. In order to prevent smart grid attacks, the proposed framework makes the generation of blocks using short signatures and hash functions. The proposed deep learning-based scheme is an intrusion detection system (IDS), which employs recurrent neural networks (RNNs) for detecting network attacks and fraudulent transactions in the blockchain-based energy network. We study the performance of the proposed IDS on three different sources the CICIDS2017 dataset, a Power System dataset, and a Bot-IoT dataset.

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Section: Evaluation Resultsmentioning

confidence: 99%

DeepCoin: A Novel Deep Learning and Blockchain-Based Energy Exchange Framework for Smart Grids

Ferrag

Μαγλαράς

2020

IEEE Trans. Eng. Manage.

270

121

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“…In recent years, CNN based approaches retain the overwhelming benefits compared to some previous handcrafted features extraction methods, particularly in semantics representation of image [12,34,35]. In the deep learning based approaches, the features of the earlier layers contain higher spatial resolution for precise local features, while the features in later layers indicates more semantic or global information [36].…”

Section: B Proposed Facial Features Extractionmentioning

confidence: 99%

Efficient Image Recognition and Retrieval on IoT-Assisted Energy-Constrained Platforms From Big Data Repositories

Mehmood

Ullah

Muhammad

et al. 2019

IEEE Internet Things J.

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“…In this paper, we calculate the angle cosine value which reflects the similarity between documents and cluster documents with DBSCAN. DBSCAN, proposed by Martin Ester in 1996, is a density-based clustering algorithm which is widely cited in scientific literature [23], and it is awarded the test of time award in 2014 [24]. When clustering, other thanmeans, DBSCAN does not need to specify the number of clusters and it can find the clusters of arbitrary shape.…”

Section: Clustering Of Textual Documentsmentioning

confidence: 99%

A Data Leakage Prevention Method Based on the Reduction of Confidential and Context Terms for Smart Mobile Devices

Tian

Qiu

et al. 2018

Wireless Communications and Mobile Computing

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Early data leakage protection methods for smart mobile devices usually focus on confidential terms and their context, which truly prevent some kinds of data leakage events. However, with the high dimensionality and redundancy of text data, it is difficult to detect the documents which contain confidential contents accurately. Our approach updates cluster graph structure based on CBDLP (Data Leakage Protection Based on Context) model by computing the importance of confidential terms and the terms within the range of their context. By applying CBDLP with pruning procedure which has been validated, we further remove the redundancy terms and noise terms. Actually, not only can confidential terms be accurately detected but also the sophisticated rephrased confidential contents are detected during the experiments.

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Deep Learning Based Multi-Channel Intelligent Attack Detection for Data Security

Cited by 190 publications

References 40 publications

DeepCoin: A Novel Deep Learning and Blockchain-Based Energy Exchange Framework for Smart Grids

DeepCoin: A Novel Deep Learning and Blockchain-Based Energy Exchange Framework for Smart Grids

Efficient Image Recognition and Retrieval on IoT-Assisted Energy-Constrained Platforms From Big Data Repositories

A Data Leakage Prevention Method Based on the Reduction of Confidential and Context Terms for Smart Mobile Devices

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