An Industrial Network Intrusion Detection Algorithm Based on Multifeature Data Clustering Optimization Model

Liang, Wei; Li, Kuan‐Ching; Long, Jing; Kui, Xiaoyan; Zomaya, Albert Y.

doi:10.1109/tii.2019.2946791

Cited by 177 publications

(72 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: Discussionmentioning

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: Discussionmentioning

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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“…There are many kinds of data produced by IoT devices, and most of them are unstructured data [25]. For example, the camera can capture the real-world images and produces pictures or video data, the microphone can capture the external sound and produces audio data.…”

Section: System Model and Design A Resource And Policy Modelmentioning

confidence: 99%

Fabric-iot: A Blockchain-Based Access Control System in IoT

2020

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IoT devices have some special characteristics, such as mobility, limited performance, and distributed deployment, which makes it difficult for traditional centralized access control methods to support access control in current large-scale IoT environment. To address these challenges, this paper proposes an access control system in IoT named fabric-iot, which is based on Hyperledger Fabric blockchain framework and attributed based access control (ABAC). The system contains three kinds of smart contracts, which are Device Contract (DC), Policy Contract (PC), and Access Contract (AC). DC provides a method to store the URL of resource data produced by devices, and a method to query it. PC provides functions to manage ABAC policies for admin users. AC is the core program to implement an access control method for normal users. Combined with ABAC and blockchain technology, fabric-iot can provide decentralized, fine-grained and dynamic access control management in IoT. To verify the performance of this system, two groups of simulation experiments are designed. The results show that fabric-iot can maintain high throughput in largescale request environment and reach consensus efficiently in a distributed system to ensure data consistency.

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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

Self Cite

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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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An Industrial Network Intrusion Detection Algorithm Based on Multifeature Data Clustering Optimization Model

Cited by 177 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

Fabric-iot: A Blockchain-Based Access Control System in IoT

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

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