scholarly journals DDoS Detection Using a Cloud-Edge Collaboration Method Based on Entropy-Measuring SOM and KD-Tree in SDN

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yuhua Xu ◽  
Yunfeng Yu ◽  
Hanshu Hong ◽  
Zhixin Sun
Keyword(s):  
Detection Method ◽  
Detection Efficiency ◽  
Detection Accuracy ◽  
Self Organizing Maps ◽  
Fine Grained ◽  
Tree Detection ◽  
Entropy Measurement ◽  
Ddos Detection ◽  
The Internet Of Things ◽  
The Ideal

Software-defined networking (SDN) emerges as an innovative network paradigm, which separates the control plane from the data plane to improve the network programmability and flexibility. It is widely applied in the Internet of Things (IoT). However, SDN is vulnerable to DDoS attacks, which can cause network disasters. In order to protect SDN security, a DDoS detection method using cloud-edge collaboration based on Entropy-Measuring Self-organizing Maps and KD-tree (EMSOM-KD) is designed for SDN. Entropy measurement is utilized to select the ideal SOM map and classify SOM neurons considering the limitation of dead and suspicious neurons. EMSOM can detect most flows directly and filter out a few doubtable flows. Then these flows are fine-grained, identified by KD-tree. Due to the limited and precious resources of the controller, parameter computation is performed in the cloud. The edge controller implements DDoS detection by EMSOM-KD. The experiments are conducted to evaluate the performance of the proposed method. The results show that EMSOM-KD has better detection accuracy; moreover, it improves the KD-tree detection efficiency.

scholarly journals Detecting Shilling Attacks with Automatic Features from Multiple Views

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yaojun Hao ◽  
Fuzhi Zhang ◽  
Jian Wang ◽  
Qingshan Zhao ◽  
Jianfang Cao
Keyword(s):  
Principal Component Analysis ◽  
Recommender Systems ◽  
Detection Method ◽  
Principal Component ◽  
Component Analysis ◽  
Experimental Results ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Multiple Views ◽  
Fine Grained

Due to the openness of the recommender systems, the attackers are likely to inject a large number of fake profiles to bias the prediction of such systems. The traditional detection methods mainly rely on the artificial features, which are often extracted from one kind of user-generated information. In these methods, fine-grained interactions between users and items cannot be captured comprehensively, leading to the degradation of detection accuracy under various types of attacks. In this paper, we propose an ensemble detection method based on the automatic features extracted from multiple views. Firstly, to collaboratively discover the shilling profiles, the users’ behaviors are analyzed from multiple views including ratings, item popularity, and user-user graph. Secondly, based on the data preprocessed from multiple views, the stacked denoising autoencoders are used to automatically extract user features with different corruption rates. Moreover, the features extracted from multiple views are effectively combined based on principal component analysis. Finally, according to the features extracted with different corruption rates, the weak classifiers are generated and then integrated to detect attacks. The experimental results on the MovieLens, Netflix, and Amazon datasets indicate that the proposed method can effectively detect various attacks.

scholarly journals Research on insulator defect detection algorithm of transmission line based on CenterNet

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0255135
Author(s):  
Chunming Wu ◽  
Xin Ma ◽  
Xiangxu Kong ◽  
Haichao Zhu
Keyword(s):  
Transmission Line ◽  
Defect Detection ◽  
Detection Method ◽  
Detection Efficiency ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Detection Algorithm ◽  
Electric Power System ◽  
Stable Operation ◽  
Detection Accuracy

The reliability of the insulator has directly affected the stable operation of electric power system. The detection of defective insulators has always been an important issue in smart grid systems. However, the traditional transmission line detection method has low accuracy and poor real-time performance. We present an insulator defect detection method based on CenterNet. In order to improve detection efficiency, we simplified the backbone network. In addition, an attention mechanism is utilized to suppress useless information and improve the accuracy of network detection. In image preprocessing, the blurring of some detected images results in the samples being discarded, so we use super-resolution reconstruction algorithm to reconstruct the blurred images to enhance the dataset. The results show that the AP of the proposed method reaches 96.16% and the reasoning speed reaches 30FPS under the test condition of NVIDIA GTX 1080 test conditions. Compared with Faster R-CNN, YOLOV3, RetinaNet and FSAF, the detection accuracy of proposed method is greatly improved, which fully proves the effectiveness of the proposed method.

Research on Technology of the Laser Designator Multi-Parameter Integrated Detection Method

2013 ◽  
Vol 552 ◽  
pp. 276-280
Author(s):  
Jin Song Wang ◽  
Jin Qiu Qi ◽  
Hao Zeng Wang ◽  
Jian Nan Deng ◽  
Zhi Yong An
Keyword(s):  
Image Processing ◽  
Detection Method ◽  
Detection Efficiency ◽  
Detection Accuracy ◽  
High Detection ◽  
Testing Technology ◽  
Beam Displacement ◽  
Focus Spot ◽  
Integrated Detection ◽  
Detection Principle

According to the state of testing technology for laser designator multi-parametric, a multi-parameter integrated detection method on the basis of optical collimation and digital image processing technology is proposed, and the way for the detection of multi-parameter characteristics and integrated detection is analyzed. By using the detection principle of large aperture lens focus spot method, the parameter measurements, such as the divergence angle of the laser designator beam, displacement amount of the light spot move, spot of adjustment range and deviation and the multi-axis consistency are measured. Simultaneously, the parameters of the sight line alteration of daylight aiming sight, the graduation precision can also be tested. By the analysis of experiment,the method has high detection accuracy and detection efficiency.

scholarly journals Multiple-Features-Based Semisupervised Clustering DDoS Detection Method

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Yonghao Gu ◽  
Yongfei Wang ◽  
Zhen Yang ◽  
Fei Xiong ◽  
Yimu Gao
Keyword(s):  
Detection Method ◽  
Convergence Speed ◽  
Detection Accuracy ◽  
Data Sets ◽  
Attack Behavior ◽  
Data Set ◽  
Multiple Features ◽  
Ddos Attack ◽  
Ddos Detection ◽  
Semisupervised Clustering

DDoS attack stream from different agent host converged at victim host will become very large, which will lead to system halt or network congestion. Therefore, it is necessary to propose an effective method to detect the DDoS attack behavior from the massive data stream. In order to solve the problem that large numbers of labeled data are not provided in supervised learning method, and the relatively low detection accuracy and convergence speed of unsupervised k-means algorithm, this paper presents a semisupervised clustering detection method using multiple features. In this detection method, we firstly select three features according to the characteristics of DDoS attacks to form detection feature vector. Then, Multiple-Features-Based Constrained-K-Means (MF-CKM) algorithm is proposed based on semisupervised clustering. Finally, using MIT Laboratory Scenario (DDoS) 1.0 data set, we verify that the proposed method can improve the convergence speed and accuracy of the algorithm under the condition of using a small amount of labeled data sets.

scholarly journals Entropy Based Features Distribution for Anti-DDoS Model in SDN

2021 ◽  
Vol 13 (3) ◽  
pp. 1522
Author(s):  
Raja Majid Ali Ujjan ◽  
Zeeshan Pervez ◽  
Keshav Dahal ◽  
Wajahat Ali Khan ◽  
Asad Masood Khattak ◽  
...  
Keyword(s):  
Network Security ◽  
False Positive ◽  
Denial Of Service ◽  
Network Services ◽  
Detection Accuracy ◽  
Data Sets ◽  
Traffic Patterns ◽  
Average Accuracy ◽  
Ddos Detection ◽  
Quantitative Results

In modern network infrastructure, Distributed Denial of Service (DDoS) attacks are considered as severe network security threats. For conventional network security tools it is extremely difficult to distinguish between the higher traffic volume of a DDoS attack and large number of legitimate users accessing a targeted network service or a resource. Although these attacks have been widely studied, there are few works which collect and analyse truly representative characteristics of DDoS traffic. The current research mostly focuses on DDoS detection and mitigation with predefined DDoS data-sets which are often hard to generalise for various network services and legitimate users’ traffic patterns. In order to deal with considerably large DDoS traffic flow in a Software Defined Networking (SDN), in this work we proposed a fast and an effective entropy-based DDoS detection. We deployed generalised entropy calculation by combining Shannon and Renyi entropy to identify distributed features of DDoS traffic—it also helped SDN controller to effectively deal with heavy malicious traffic. To lower down the network traffic overhead, we collected data-plane traffic with signature-based Snort detection. We then analysed the collected traffic for entropy-based features to improve the detection accuracy of deep learning models: Stacked Auto Encoder (SAE) and Convolutional Neural Network (CNN). This work also investigated the trade-off between SAE and CNN classifiers by using accuracy and false-positive results. Quantitative results demonstrated SAE achieved relatively higher detection accuracy of 94% with only 6% of false-positive alerts, whereas the CNN classifier achieved an average accuracy of 93%.

scholarly journals An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu’s Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1581
Author(s):  
Xiaolong Chen ◽  
Jian Li ◽  
Shuowen Huang ◽  
Hao Cui ◽  
Peirong Liu ◽  
...  
Keyword(s):  
Crack Detection ◽  
Detection Method ◽  
Multiple Scales ◽  
Point Clouds ◽  
Depth Image ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Concrete Crack ◽  
3D Point Clouds ◽  
2D Images

Cracks are one of the main distresses that occur on concrete surfaces. Traditional methods for detecting cracks based on two-dimensional (2D) images can be hampered by stains, shadows, and other artifacts, while various three-dimensional (3D) crack-detection techniques, using point clouds, are less affected in this regard but are limited by the measurement accuracy of the 3D laser scanner. In this study, we propose an automatic crack-detection method that fuses 3D point clouds and 2D images based on an improved Otsu algorithm, which consists of the following four major procedures. First, a high-precision registration of a depth image projected from 3D point clouds and 2D images is performed. Second, pixel-level image fusion is performed, which fuses the depth and gray information. Third, a rough crack image is obtained from the fusion image using the improved Otsu method. Finally, the connected domain labeling and morphological methods are used to finely extract the cracks. Experimentally, the proposed method was tested at multiple scales and with various types of concrete crack. The results demonstrate that the proposed method can achieve an average precision of 89.0%, recall of 84.8%, and F1 score of 86.7%, performing significantly better than the single image (average F1 score of 67.6%) and single point cloud (average F1 score of 76.0%) methods. Accordingly, the proposed method has high detection accuracy and universality, indicating its wide potential application as an automatic method for concrete-crack detection.

scholarly journals Examination of Abnormal Behavior Detection Based on Improved YOLOv3

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 197
Author(s):  
Meng-ting Fang ◽  
Zhong-ju Chen ◽  
Krzysztof Przystupa ◽  
Tao Li ◽  
Michal Majka ◽  
...  
Keyword(s):  
Detection Method ◽  
Abnormal Behavior ◽  
Detection Accuracy ◽  
Average Precision ◽  
Research Results ◽  
Behavior Detection ◽  
Abnormal Behavior Detection ◽  
The Mean ◽  
Examination Room ◽  
Detection Speed

Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.

scholarly journals THE IDEAL DISTRIBUTION OF FARMERS: EXPLAINING THE EURO-AMERICAN SETTLEMENT OF UTAH

2017 ◽  
Vol 83 (1) ◽  
pp. 75-90 ◽  
Author(s):  
Peter M. Yaworsky ◽  
Brian F. Codding
Keyword(s):  
Population Density ◽  
Ideal Free Distribution ◽  
Negative Relationship ◽  
Coarse Grained ◽  
Distribution Model ◽  
Test Model ◽  
Fine Grained ◽  
Fine Grain ◽  
Model Predictions ◽  
The Ideal

Explaining how and why populations settle a new landscape is central to many questions in American archaeology. Recent advances in settlement research have adopted predictions from the Ideal Free Distribution model (IFD). While tests of IFD predictions to date rely either on archaeologically derived coarse-grained diachronic data or ethnographically derived fine-grained synchronic data, here we provide the first test using historically derived data that is both fine-grained and diachronic. Fine-grain diachronic data allow us to test model predictions at a temporal scale in line with human settlement decisions and to validate proxies for application in archaeological contexts. To test model predictions pertaining to the relationship between population density and habitat quality, we use data from the historical settlement of Utah. The results demonstrate a negative relationship between population density and the quality of habitats occupied. These results are consistent with IFD predictions, suggesting that Euro-American settlement of Utah resulted from individuals attempting to maximize individual returns via agricultural productivity. Our results provide a quantitative and testable explanation for population dispersion over time and explain the spatial distribution of population density today. The results support predictions derived from a general theory of behavior, providing an explanatory framework for colonization events worldwide.

Heklaite, KNaSiF6, a new fumarolic mineral from Hekla volcano, Iceland

2010 ◽  
Vol 74 (1) ◽  
pp. 147-157 ◽  
Author(s):  
A. Garavelli ◽  
T. Balić-Žunić ◽  
D. Mitolo ◽  
P. Acquafredda ◽  
E. Leonardsen ◽  
...  
Keyword(s):  
Powder Diffraction ◽  
Quantitative Chemical Analysis ◽  
Unit Cell Parameters ◽  
Orthorhombic Space Group ◽  
Synthetic Compound ◽  
Calculated Density ◽  
X Ray ◽  
Cell Parameters ◽  
Fine Grained ◽  
The Ideal

AbstractHeklaite, with the ideal formula KNaSiF6, was found among fumarolic encrustations collected in 1992 on the Hekla volcano, Iceland. Heklaite forms a fine-grained mass of micron- to sub-micron-sized crystals intimately associated with malladrite, hieratite and ralstonite. The mineral is colourless, transparent, non-fluorescent, has a vitreous lustre and a white streak. The calculated density is 2.69 g cm–3. An SEM-EDS quantitative chemical analysis shows the following range of concentrations (wt.%): Na 11.61–12.74 (average 11.98), K 17.02–18.97 (average 18.29), Si 13.48 –14.17 (average 13.91), F 54.88–56.19 (average 55.66). The empirical chemical formula, calculated on the basis of 9 a.p.f.u., is Na1.07K0.96Si1.01F5.97. X-ray powder diffraction indicates that heklaite is orthorhombic, space group Pnma, with the following unit-cell parameters: a = 9.3387(7) Å, b = 5.5032(4) Å, c = 9.7957(8) Å , V = 503.43(7) Å3, Z = 4. The eight strongest reflections in the powder diffraction pattern [d in Å (I/I0) (hkl)] are: 4.33 (53) (102); 4.26 (56) (111); 3.40 (49) (112); 3.37 (47) (202); 3.34 (100) (211); 2.251 (27) (303); 2.050 (52) (123); 2.016 (29) (321). On the basis of chemical analyses and X-ray data, heklaite corresponds to the synthetic compound KNaSiF6. The name is for the type locality, the Hekla volcano, Iceland.

A real time detection method for abnormal strapping of steel coil based on CCD active imaging

2021 ◽  
Author(s):  
Chuan Ye ◽  
Liming Zhao ◽  
Qiyan Wang ◽  
Bo Pan ◽  
Youchun Xie ◽  
...  
Keyword(s):  
Maximum Entropy ◽  
Detection Method ◽  
Saliency Detection ◽  
Automatic Segmentation ◽  
Engineering Application ◽  
Detection Accuracy ◽  
Steel Coil ◽  
Detection Model ◽  
Laser Stripe ◽  
Active Imaging

Abstract In order to accurately detect the abnormal looseness of strapping in the process of steel coil hoisting, an accurate detection method of strapping abnormality based on CCD structured light active imaging is proposed. Firstly, a maximum entropy laser stripe automatic segmentation model integrating multi-scale saliency features is constructed. With the help of saliency detection model, the purpose is to reduce the interference of the environment to the laser stripe and highlight the distinguishability between the stripe and the background. Then, the maximum entropy is used to segment the fused saliency features and accurately extract the stripe contour. Finally, the stripe normal field is obtained by calculating the stripe gradient vector, the stripe center line is extracted based on the stripe distribution normal direction, and the abnormal strapping is recognized online according to the stripe center. Experiments show that the proposed method is effective in terms of detection accuracy and time efficiency, and has certain engineering application value.

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