scholarly journals Industry 4.0-Oriented Chipless RFID Backscatter Signal Variable Polarization Amplitude Deep Learning Coding

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Guolong Shi ◽  
Yigang He ◽  
Lichuan Gu ◽  
Jun Jiao
Keyword(s):  
Deep Learning ◽  
Industry 4.0 ◽  
Detection Rate ◽  
Superior Performance ◽  
Scattered Signal ◽  
Control Network ◽  
Industrial Control ◽  
Backscatter Signal ◽  
Intrusion Prevention ◽  
Network Intrusion

Due to the weak network security protection capabilities of control system network protocols under Industry 4.0, the research on industrial control network intrusion detection is still in its infancy. This article discussed and researched the intrusion prevention technology of industrial control networks based on deep learning. According to the electromagnetic scattering theory, the backscatter signal model of the chipless tag was established as a chipless tag structure. Polarized deep learning coding was used for the label; that was, deep learning coding was performed on the copolarization component and the cross-polarization component at the same time, and a 16-bit deep learning coding bit number was obtained. The wave crest deep learning coding was used for the split ellipse ring patch label, and the 6-bit deep learning coding bit number was obtained. Then, the poles of the scattered signal of the tag were extracted to identify the tag. The variable polarization effect was achieved by adopting the dipole resonant unit with the two ends bent. Aiming at the problem of low detection rate caused by the shallow selection of feature classification of intrusion prevention systems, an industrial control network intrusion prevention model based on self-deep learning encoders and extreme learning machines was proposed to extract features from industrial control network data through deep learning. For accurate classification, the theoretical judgment was also verified through simulation experiments, and it was proved that the detection rate of the model has also improved. It forms a set of industrial control network intrusion prevention system with complete functions and superior performance with data acquisition module, system log module, defense response module, central control module, etc. The matrix beam algorithm was used to extract the poles and residues for the late response, and the extracted poles and residues were used to reconstruct the signal. The reconstructed signal was compared with the scattered signal to verify the correctness of the pole extraction. Finally, the tags were processed and tested in the actual environment, and the measured results were consistent with the theoretical analysis and simulation results.

scholarly journals Explaining the Attributes of a Deep Learning Based Intrusion Detection System for Industrial Control Networks

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3817 ◽  
Author(s):  
Zhidong Wang ◽  
Yingxu Lai ◽  
Zenghui Liu ◽  
Jing Liu
Keyword(s):  
Deep Learning ◽  
Control System ◽  
Intrusion Detection ◽  
Security System ◽  
Classification Model ◽  
Industrial Control System ◽  
Industrial Control ◽  
Data Set ◽  
Network Intrusion ◽  
Calculation Process

Intrusion detection is only the initial part of the security system for an industrial control system. Because of the criticality of the industrial control system, professionals still make the most important security decisions. Therefore, a simple intrusion alarm has a very limited role in the security system, and intrusion detection models based on deep learning struggle to provide more information because of the lack of explanation. This limits the application of deep learning methods to industrial control network intrusion detection. We analyzed the deep neural network (DNN) model and the interpretable classification model from the perspective of information, and clarified the correlation between the calculation process of the DNN model and the classification process. By comparing the normal samples with the abnormal samples, the abnormalities that occur during the calculation of the DNN model compared to the normal samples could be found. Based on this, a layer-wise relevance propagation method was designed to map the abnormalities in the calculation process to the abnormalities of attributes. At the same time, considering that the data set may already contain some useful information, we designed filtering rules for a kind of data set that can be obtained at a low cost, so that the calculation result is presented in a more accurate manner, which should help professionals lock and address intrusion threats more quickly.

scholarly journals Comparison of Machine Learning and Deep Learning Models for Network Intrusion Detection Systems

2020 ◽  
Vol 12 (10) ◽  
pp. 167
Author(s):  
Niraj Thapa ◽  
Zhipeng Liu ◽  
Dukka B. KC ◽  
Balakrishna Gokaraju ◽  
Kaushik Roy
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Detection Rate ◽  
Intrusion Detection Systems ◽  
Network Intrusion Detection ◽  
High Detection Rate ◽  
Detection Systems ◽  
Network Intrusion ◽  
High Detection

The development of robust anomaly-based network detection systems, which are preferred over static signal-based network intrusion, is vital for cybersecurity. The development of a flexible and dynamic security system is required to tackle the new attacks. Current intrusion detection systems (IDSs) suffer to attain both the high detection rate and low false alarm rate. To address this issue, in this paper, we propose an IDS using different machine learning (ML) and deep learning (DL) models. This paper presents a comparative analysis of different ML models and DL models on Coburg intrusion detection datasets (CIDDSs). First, we compare different ML- and DL-based models on the CIDDS dataset. Second, we propose an ensemble model that combines the best ML and DL models to achieve high-performance metrics. Finally, we benchmarked our best models with the CIC-IDS2017 dataset and compared them with state-of-the-art models. While the popular IDS datasets like KDD99 and NSL-KDD fail to represent the recent attacks and suffer from network biases, CIDDS, used in this research, encompasses labeled flow-based data in a simulated office environment with both updated attacks and normal usage. Furthermore, both accuracy and interpretability must be considered while implementing AI models. Both ML and DL models achieved an accuracy of 99% on the CIDDS dataset with a high detection rate, low false alarm rate, and relatively low training costs. Feature importance was also studied using the Classification and regression tree (CART) model. Our models performed well in 10-fold cross-validation and independent testing. CART and convolutional neural network (CNN) with embedding achieved slightly better performance on the CIC-IDS2017 dataset compared to previous models. Together, these results suggest that both ML and DL methods are robust and complementary techniques as an effective network intrusion detection system.

scholarly journals FCNN: An Efficient Intrusion Detection Method Based on Raw Network Traffic

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yue Wang ◽  
Yiming Jiang ◽  
Julong Lan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Superior Performance ◽  
Detection Accuracy ◽  
Input Unit ◽  
Learning Methods ◽  
Training Time ◽  
Machine Learning Methods ◽  
Network Intrusion

When traditional machine learning methods are applied to network intrusion detection, they need to rely on expert knowledge to extract feature vectors in advance, which incurs lack of flexibility and versatility. Recently, deep learning methods have shown superior performance compared with traditional machine learning methods. Deep learning methods can learn the raw data directly, but they are faced with expensive computing cost. To solve this problem, a preprocessing method based on multipacket input unit and compression is proposed, which takes m data packets as the input unit to maximize the retention of information and greatly compresses the raw traffic to shorten the data learning and training time. In our proposed method, the CNN network structure is optimized and the weights of some convolution layers are assigned directly by using the Gabor filter. Experimental results on the benchmark data set show that compared with the existing models, the proposed method improves the detection accuracy by 2.49% and reduces the training time by 62.1%. In addition, the experiments show that the proposed compression method has obvious advantages in detection accuracy and computational efficiency compared with the existing compression methods.

scholarly journals A Deep Learning Approach to Network Intrusion Detection Using Deep Autoencoder

2020 ◽  
Vol 34 (4) ◽  
pp. 457-463
Author(s):  
Srikanthyadav Moraboena ◽  
Gayatri Ketepalli ◽  
Padmaja Ragam
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Human Interaction ◽  
Classification Model ◽  
Knowledge Discovery In Databases ◽  
Network Intrusion Detection ◽  
Detection Accuracy ◽  
Intrusion Prevention ◽  
Network Intrusion

The security of computer networks is critical for network intrusion detection systems (NIDS). However, concerns exist about the suitability and sustainable development of current approaches in light of modern networks. Such concerns are particularly related to increasing levels of human interaction required and decreased detection accuracy. These concerns are also highlighted. This post presents a modern intrusion prevention deep learning methodology. For unattended function instruction, we clarify our proposed Symmetric Deep Autoencoder (SDAE). Also, we are proposing our latest deep research classification model developed with stacked SDAEs. The classification proposed by the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Canadian Institute for Cybersecurity -Intrusion Detection System (CICIDS 2017) data sets was implemented in Tensor Flow, a Graphics Procedure Unit (GPU) enabled and evaluated. We implemented and tested our experiment with different batch sizes using Adam optimizer. Promising findings from our model have been achieved so far, which demonstrates improvements over current solutions and the subsequent improvement for use in advanced NIDS.

Attack Graph Generation and Visualization for Industrial Control Network

2020 ◽  
Author(s):  
Yanli Feng ◽  
Gongliang Sun ◽  
Zhiyao Liu ◽  
Chenrui Wu ◽  
Xiaoyang Zhu ◽  
...  
Keyword(s):  
Control Network ◽  
Industrial Control ◽  
Attack Graph ◽  
Graph Generation

A Survey of Network Embedding for Drug Analysis and Prediction

2020 ◽  
Vol 21 ◽  
Author(s):  
Zhixian Liu ◽  
Qingfeng Chen ◽  
Wei Lan ◽  
Jiahai Liang ◽  
Yiping Pheobe Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Function ◽  
Dimensional Space ◽  
Auxiliary Information ◽  
Matrix Decomposition ◽  
Drug Analysis ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Network Embedding ◽  
Similarity Estimation

: Traditional network-based computational methods have shown good results in drug analysis and prediction. However, these methods are time consuming and lack universality, and it is difficult to exploit the auxiliary information of nodes and edges. Network embedding provides a promising way for alleviating the above problems by transforming network into a low-dimensional space while preserving network structure and auxiliary information. This thus facilitates the application of machine learning algorithms for subsequent processing. Network embedding has been introduced into drug analysis and prediction in the last few years, and has shown superior performance over traditional methods. However, there is no systematic review of this issue. This article offers a comprehensive survey of the primary network embedding methods and their applications in drug analysis and prediction. The network embedding technologies applied in homogeneous network and heterogeneous network are investigated and compared, including matrix decomposition, random walk, and deep learning. Especially, the Graph neural network (GNN) methods in deep learning are highlighted. Further, the applications of network embedding in drug similarity estimation, drug-target interaction prediction, adverse drug reactions prediction, protein function and therapeutic peptides prediction are discussed. Several future potential research directions are also discussed.

scholarly journals Deep convolutional neural networks for cardiovascular vulnerable plaque detection

2019 ◽  
Vol 277 ◽  
pp. 02024 ◽  
Author(s):  
Lincan Li ◽  
Tong Jia ◽  
Tianqi Meng ◽  
Yizhe Liu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Vulnerable Plaque ◽  
Recall Rate ◽  
Superior Performance ◽  
Learning Approaches ◽  
Deep Convolutional Neural Networks ◽  
Vulnerable Plaques ◽  
Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

scholarly journals Network intrusion detection method based on deep learning

2021 ◽  
Vol 1966 (1) ◽  
pp. 012051
Author(s):  
Shuai Zou ◽  
Fangwei Zhong ◽  
Bing Han ◽  
Hao Sun ◽  
Tao Qian ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Detection Method ◽  
Network Intrusion Detection ◽  
Network Intrusion

scholarly journals Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4736
Author(s):  
Sk. Tanzir Mehedi ◽  
Adnan Anwar ◽  
Ziaur Rahman ◽  
Kawsar Ahmed
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Real Time ◽  
Transfer Learning ◽  
Security Requirements ◽  
Detection Accuracy ◽  
Area Network ◽  
Complex Data ◽  
Network Intrusion

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

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