Deep Reinforcement Learning for Resource Protection and Real-Time Detection in IoT Environment

Liang, Wei; Huang, Weihong; Long, Jing; Zhang, Ke; Li, Kuan‐Ching; Zhang, Dafang

doi:10.1109/jiot.2020.2974281

Cited by 165 publications

(41 citation statements)

References 22 publications

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“…In addition, we will explore the possible application of our traffic flow prediction model to the emerging edge/cloud TCPS [33]. Moreover, we will also investigate the adoption of our model to other industrial scenarios like industrial network intrusion detection [34], [35].…”

Section: Resultsmentioning

confidence: 99%

Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber–Physical Systems

Zhou

Dai

Wang

2021

IEEE Trans. Ind. Inf.

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Recently, traffic flow prediction has drawn significant attention because it is a prerequisite in intelligent transportation management in urban informatics. The massively-available traffic data collected from various sensors in Transportation Cyber-Physical Systems brings the opportunities in accurately forecasting traffic trend. Recent advances in deep learning shows the effectiveness on traffic flow prediction though most of them only demonstrate the superior performance on traffic data from a single type of vehicular carriers (e.g., cars) and does not perform well in other types of vehicles. To fill this gap, we propose a wide-attention and deep-composite (WADC) model consisting of a wide-attention module and a deep-composite module in this paper. In particular, the wide-attention module can extract global key features from traffic flows via a linear model with selfattention mechanism. The deep-composite module can generalize local key features via Convolutional Neural Network component and Long Short-Term Memory Network component. We also perform extensive experiments on different types of traffic flow datasets to investigate the performance of WADC model. Our experimental results exhibit that WADC model outperforms other existing approaches.

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Section: Resultsmentioning

confidence: 99%

Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber–Physical Systems

Zhou

Dai

Wang

2021

IEEE Trans. Ind. Inf.

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“…Compared with the existing method MCAN, DCAN can make use of the complex correlation between multimodal features in a more effective way and extract more discriminative features for images and questions. This exploration of modeling dense intra- and inter-modality interactions has been applied to intelligent transportation [ 42 ], intelligent robot [ 43 ], and other fields [ 44 , 45 , 46 ]. Applying it to a wider range of scenarios will be an inevitable trend in the future.…”

Section: Resultsmentioning

confidence: 99%

An Effective Dense Co-Attention Networks for Visual Question Answering

Han

2020

Sensors

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At present, the state-of-the-art approaches of Visual Question Answering (VQA) mainly use the co-attention model to relate each visual object with text objects, which can achieve the coarse interactions between multimodalities. However, they ignore the dense self-attention within question modality. In order to solve this problem and improve the accuracy of VQA tasks, in the present paper, an effective Dense Co-Attention Networks (DCAN) is proposed. First, to better capture the relationship between words that are relatively far apart and make the extracted semantics more robust, the Bidirectional Long Short-Term Memory (Bi-LSTM) neural network is introduced to encode questions and answers; second, to realize the fine-grained interactions between the question words and image regions, a dense multimodal co-attention model is proposed. The model’s basic components include the self-attention unit and the guided-attention unit, which are cascaded in depth to form a hierarchical structure. The experimental results on the VQA-v2 dataset show that DCAN has obvious performance advantages, which makes VQA applicable to a wider range of AI scenarios.

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“…Merkle proposed the Merkle tree [ 18 ], which is widely used in blockchain to verify the integrity of data blocks [ 19 , 20 , 21 ].…”

Section: Merkle Tree-based One-time Password Algorithmmentioning

confidence: 99%

An Efficient Two-Factor Authentication Scheme Based on the Merkle Tree

Yin

Guo

et al. 2020

Sensors

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The Time-based One-Time Password (TOTP) algorithm is commonly used for two-factor authentication. In this algorithm, a shared secret is used to derive a One-Time Password (OTP). However, in TOTP, the client and the server need to agree on a shared secret (i.e., a key). As a consequence, an adversary can construct an OTP through the compromised key if the server is hacked. To solve this problem, Kogan et al. proposed T/Key, an OTP algorithm based on a hash chain. However, the efficiency of OTP generation and verification is low in T/Key. In this article, we propose a novel and efficient Merkle tree-based One-Time Password (MOTP) algorithm to overcome such limitations. Compared to T/Key, this proposal reduces the number of hash operations to generate and verify the OTP, at the cost of small server storage and tolerable client storage. Experimental analysis and security evaluation show that MOTP can resist leakage attacks against the server and bring a tiny delay to two-factor authentication and verification time.

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Deep Reinforcement Learning for Resource Protection and Real-Time Detection in IoT Environment

Cited by 165 publications

References 22 publications

Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber–Physical Systems

Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber–Physical Systems

An Effective Dense Co-Attention Networks for Visual Question Answering

An Efficient Two-Factor Authentication Scheme Based on the Merkle Tree

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