scholarly journals DDoS Defense Method in Software-Defined Space-Air-Ground Network from Dynamic Bayesian Game Perspective

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Zhaobin Li ◽  
Bin Yang ◽  
Xinyu Zhang ◽  
Chao Guo
Keyword(s):  
Dynamic Balance ◽  
Denial Of Service ◽  
Detection Methods ◽  
Support Vector ◽  
Detection Accuracy ◽  
Real Time Control ◽  
Bayesian Game ◽  
Integrated Networks ◽  
Time Control ◽  
Attack And Defense

The centralized management of Software-Defined Network (SDN) brings convenience to Space-Air-Ground Integrated Networks (SAGIN), which also makes it vulnerable to Distributed Denial of Service (DDoS). At present, the popular detection methods are based on machine learning, but most of them are fixed detection strategies with high overhead and real-time control, so the efficiency is not high. This paper designs different defense methods for different DDoS attacks and constructs a multitype DDoS defense model based on a dynamic Bayesian game in the Software-Defined Space-Air-Ground Integrated Networks (SD-SAGIN). The proposed game model’s Nash equilibrium is solved based on the different costs and payoffs of each method. We simulated the attack and defense of DDoS in Ryu controller and Mininet. The results show that, under our model, the attacker and defender’s strategies are in a dynamic balance, and the controller can effectively reduce the defense cost while ensuring detection accuracy. Compared with the existing traditional Support Vector Machine (SVM) defense method, the performance of the proposed method is better, and it provides one of the references for DDoS defense in SD-SAGIN.

scholarly journals On the Detection of Low-Rate Denial of Service Attacks at Transport and Application Layers

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2105
Author(s):  
Vasudha Vedula ◽  
Palden Lama ◽  
Rajendra V. Boppana ◽  
Luis A. Trejo
Keyword(s):  
Network Traffic ◽  
Short Term Memory ◽  
Transmission Control Protocol ◽  
Denial Of Service ◽  
Detection Methods ◽  
Support Vector ◽  
Detection Accuracy ◽  
Ddos Attacks ◽  
Network Bandwidth ◽  
Low Rate

Distributed denial of service (DDoS) attacks aim to deplete the network bandwidth and computing resources of targeted victims. Low-rate DDoS attacks exploit protocol features such as the transmission control protocol (TCP) three-way handshake mechanism for connection establishment and the TCP congestion-control induced backoffs to attack at a much lower rate and still effectively bring down the targeted network and computer systems. Most of the statistical and machine/deep learning-based detection methods proposed in the literature require keeping track of packets by flows and have high processing overheads for feature extraction. This paper presents a novel two-stage model that uses Long Short-Term Memory (LSTM) and Random Forest (RF) to detect the presence of attack flows in a group of flows. This model has a very low data processing overhead; it uses only two features and does not require keeping track of packets by flows, making it suitable for continuous monitoring of network traffic and on-the-fly detection. The paper also presents an LSTM Autoencoder to detect individual attack flows with high detection accuracy using only two features. Additionally, the paper presents an analysis of a support vector machine (SVM) model that detects attack flows in slices of network traffic collected for short durations. The low-rate attack dataset used in this study is made available to the research community through GitHub.

scholarly journals A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Jiang Wu ◽  
Yanju Ji ◽  
Ling Zhao ◽  
Mengying Ji ◽  
Zhuang Ye ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Detection ◽  
Detection Methods ◽  
Support Vector ◽  
Detection Accuracy ◽  
Analysis Method ◽  
Data Set ◽  
Specificity And Sensitivity ◽  
Svm Model ◽  
Tof Ms

Background. Surfaced-enhanced laser desorption-ionization-time of flight mass spectrometry (SELDI-TOF-MS) technology plays an important role in the early diagnosis of ovarian cancer. However, the raw MS data is highly dimensional and redundant. Therefore, it is necessary to study rapid and accurate detection methods from the massive MS data.Methods. The clinical data set used in the experiments for early cancer detection consisted of 216 SELDI-TOF-MS samples. An MS analysis method based on probabilistic principal components analysis (PPCA) and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the data set. Additionally, by the same data set, we also established a traditional PCA-SVM model. Finally we compared the two models in detection accuracy, specificity, and sensitivity.Results. Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the PCA-SVM model were 83.34%, 82.70%, and 83.88%, respectively. In contrast, those of the PPCA-SVM model were 90.80%, 92.98%, and 88.97%, respectively.Conclusions. The PPCA-SVM model had better detection performance. And the model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer.

Study of Self-Adaptive Control System in CNC Machine

2012 ◽  
Vol 271-272 ◽  
pp. 504-508
Author(s):  
Hua Cong Ye ◽  
Yue Ming Hu
Keyword(s):  
Control System ◽  
Cutting Parameters ◽  
Cnc Machining ◽  
Detection Accuracy ◽  
Cnc Machine ◽  
Cnc System ◽  
Real Time Control ◽  
Time Control ◽  
On Line ◽  
The Stability

A set of solving scheme of full-closed loop adaptive servocontrol control system, which can on-line monitoring and real-time control ,was proposed based on the analysis of a number of factors that affect the processing quality of high-precision slender shaft during CNC machining process.Adopt the "PC+motion controller" mode,on-line detection accuracy index of the workpiece, and feed bake the detection signal to CNC system, through the automatic processing of CNC system, timely adjust cutting parameters, so that the detected error factors remain within a reasonable limits, to ensure the stability of the system and improve the accuracy of workpiece.

Analyze Soft Measurement Technology Based Intelligent Algorithm in Sewage Treatment

2014 ◽  
Vol 556-562 ◽  
pp. 3164-3167
Author(s):  
Wen Cheng Wang ◽  
Zhong Xue Chen ◽  
Ke Li ◽  
Qin Zhou Niu
Keyword(s):  
Sewage Treatment ◽  
Research Strategy ◽  
Support Vector ◽  
Measurement Technology ◽  
Real Time Control ◽  
High Quality Research ◽  
Time Control ◽  
Sensing Technology ◽  
Soft Measurement ◽  
On Line

On the basis of introduction to soft sensing technology, the paper comprehensively discusses the research present situation and the application characteristics of Genetic Algorithm、Artificial Neural Networks、and Support Vector Machine.After comparison and analysis, this paper presents a high quality research strategy of on-line real-time control.

scholarly journals A supervised method for object-based 3D building change detection on aerial stereo images

2014 ◽  
Vol XL-3 ◽  
pp. 259-264 ◽  
Author(s):  
R. Qin ◽  
A. Gruen
Keyword(s):  
Change Detection ◽  
Current Trend ◽  
Detection Methods ◽  
Support Vector ◽  
Detection Accuracy ◽  
Status Change ◽  
Aerial Photos ◽  
Object Based ◽  
The Status

There is a great demand for studying the changes of buildings over time. The current trend for building change detection combines the orthophoto and DSM (Digital Surface Models). The pixel-based change detection methods are very sensitive to the quality of the images and DSMs, while the object-based methods are more robust towards these problems. In this paper, we propose a supervised method for building change detection. After a segment-based SVM (Support Vector Machine) classification with features extracted from the orthophoto and DSM, we focus on the detection of the building changes of different periods by measuring their height and texture differences, as well as their shapes. A decision tree analysis is used to assess the probability of change for each building segment and the traffic lighting system is used to indicate the status "change", "non-change" and "uncertain change" for building segments. The proposed method is applied to scanned aerial photos of the city of Zurich in 2002 and 2007, and the results have demonstrated that our method is able to achieve high detection accuracy.

Abnormal Data Detection of Guidance Angle Based On SMP-SVDD For Seeker

2021 ◽  
Author(s):  
Chao Liang ◽  
Xiangrong Zhang ◽  
Dedong Cui ◽  
Zhengang Yan ◽  
Xiangyu Zhang ◽  
...  
Keyword(s):  
Pitch Angle ◽  
Detection Methods ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Data Detection ◽  
Detection Accuracy ◽  
Angle Data ◽  
Angle Deviation ◽  
Optimal Kernel ◽  
The One

Abstract The accuracy of the pitch angle deviation directly affects the guidance accuracy of the laser seeker. During the guidance process, the abnormal pitch angle deviation data will be produced when the seeker is affected by interference sources. In this paper, aiming to detect abnormal data in seeker pitch angle deviation data, a method based on Smooth Multi-Kernel Polarization Support Vector Data Description (SMP-SVDD) is proposed to detect abnormal data in guidance angle data. On the one hand, the polarization value is used to determine the weight of the multi-kernel combination coefficient to obtain the multi-kernel polarization function, and the particle swarm optimization is used to find the optimal kernel, which improves the detection accuracy. On the other hand, the constrained quadratic programming problem is smooth and differentiable, and the conjugate gradient method can be applied to reduce the complexity of problem solving. Through simulation experiments, it is verified that the SMP-SVDD method has higher detection accuracy and faster calculation speed compared with different detection methods in different guidance stages.

scholarly journals Robust Detection of Bearing Early Fault Based on Deep Transfer Learning

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 323 ◽  
Author(s):  
Wentao Mao ◽  
Di Zhang ◽  
Siyu Tian ◽  
Jiamei Tang
Keyword(s):  
Transfer Learning ◽  
Normal State ◽  
Low Rank ◽  
Detection Methods ◽  
Support Vector ◽  
Detection Accuracy ◽  
Robust Detection ◽  
Detection Model ◽  
Performance Reduction ◽  
The Common

In recent years, machine learning techniques have been proven to be a promising tool for early fault detection of rolling bearings. In many actual applications, however, bearing whole-life data are not easy to be historically accumulated, while insufficient data may result in training a detection model that is not good enough. If utilizing the available data under different working conditions to facilitate model training, the data distribution of different bearings are usually quite different, which does not meet the precondition of i n d e p e n d e n t a n d i d e n t i c a l d i s t r i b u t i o n ( i . i . d ) and tends to cause performance reduction. In addition, disturbed by the unstable noise under complex conditions, most of the current detection methods are inclined to raise false alarms, so that the reliability of detection results needs to be improved. To solve these problems, a robust detection method for bearings early fault is proposed based on deep transfer learning. The method includes offline stage and online stage. In the offline stage, by introducing a deep auto-encoder network with domain adaptation, the distribution inconsistency of normal state data among different bearings can be weakened, then the common feature representation of the normal state is obtained. With the extracted common features, a new state assessment method based on the robust deep auto-encoder network is proposed to evaluate the boundary between normal state and early fault state in the low-rank feature space. By training a support vector machine classifier, the detection model is established. In the online stage, along with the data batch arriving sequentially, the features of target bearing are extracted using the common representation learnt in the offline stage, and online detection is conducted by feeding them into the SVM model. Experimental results on IEEE PHM Challenge 2012 bearing dataset and XJTU-SY dataset show that the proposed approach outperforms several state-of-the-art detection methods in terms of detection accuracy and false alarm rate.

scholarly journals Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques

2017 ◽  
Vol 10 (1) ◽  
pp. 199-208 ◽  
Author(s):  
Hsu-Yung Cheng ◽  
Chih-Lung Lin
Keyword(s):  
Supervised Learning ◽  
Detection Methods ◽  
Support Vector ◽  
Detection Accuracy ◽  
Cloud Detection ◽  
Multi Scale ◽  
Cloud Models ◽  
Learning Techniques ◽  
Image Patches ◽  
Local Image

Abstract. Cloud detection is important for providing necessary information such as cloud cover in many applications. Existing cloud detection methods include red-to-blue ratio thresholding and other classification-based techniques. In this paper, we propose to perform cloud detection using supervised learning techniques with multi-resolution features. One of the major contributions of this work is that the features are extracted from local image patches with different sizes to include local structure and multi-resolution information. The cloud models are learned through the training process. We consider classifiers including random forest, support vector machine, and Bayesian classifier. To take advantage of the clues provided by multiple classifiers and various levels of patch sizes, we employ a voting scheme to combine the results to further increase the detection accuracy. In the experiments, we have shown that the proposed method can distinguish cloud and non-cloud pixels more accurately compared with existing works.

scholarly journals A Two-Stage Cascaded Detection Scheme for Double HEVC Compression Based on Temporal Inconsistency

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Peisong He ◽  
Hongxia Wang ◽  
Ruimei Zhang ◽  
Yue Li
Keyword(s):  
Video Clip ◽  
Detection Methods ◽  
Support Vector ◽  
Detection Accuracy ◽  
Bit Rate ◽  
Two Stage ◽  
Detection Scheme ◽  
Double Compression ◽  
Leibler Divergence ◽  
Temporal Inconsistency

Nowadays, verifying the integrity of digital videos is significant especially for applications about multimedia communication. In video forensics, detection of double compression can be treated as the first step to analyze whether a suspicious video undergoes any tampering operations. In the last decade, numerous detection methods have been proposed to address this issue, but most existing methods design a universal detector which is hard to handle various recompression settings efficiently. In this work, we found that the statistics of different Coding Unit (CU) types have dissimilar properties when original videos are recompressed by the increased and decreased bit rates. It motivates us to propose a two-stage cascaded detection scheme for double HEVC compression based on temporal inconsistency to overcome limitations of existing methods. For a given video, CU information maps are extracted from each short-time video clip using our proposed value mapping strategy. In the first detection stage, a compact feature is extracted based on the distribution of different CU types and Kullback–Leibler divergence between temporally adjacent frames. This detection feature is fed into the Support Vector Machine classifier to identify abnormal frames with the increased bit rate. In the second stage, a shallow convolutional neural network equipped with dense connections is designed carefully to learn robust spatiotemporal representations, which can identify abnormal frames with the decreased bit rate whose forensic traces are less detectable. In experiments, the proposed method can achieve more promising detection accuracy compared with several state-of-the-art methods under various coding parameter settings, especially when the original video is recompressed with a low quality (e.g., more than 8%).

Sign in / Sign up

Export Citation Format

Share Document