scholarly journals An Adaptive-Data-Driven Attack Detection Framework on ADS-B Data

2021 ◽  
Author(s):  
Tengyao Li
Keyword(s):  
Traffic Management ◽  
Contextual Information ◽  
Attack Detection ◽  
Training Data ◽  
Data Driven ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Sequential Data ◽  
Security Measures ◽  
Data Share

<p>With the variety and quantity of flights increasing, accurate and efficient surveillance methods are in great demands for the next generation air traffic management. Relying on high accuracy, wide coverage, low deployment cost and data share support, Automatic Dependent Surveillance – Broadcast (ADS-B) is becoming the primary surveillance method in 2020. However, ADS-B data is lacking of sufficient security measures to ensure data integrity and authentication, which makes it face with various attack threats. To detect the malicious data caused by attack behaviours accurately, an adaptive-data-driven attack detection framework is proposed, which is utilized to establish the consistent framework for predictive discriminant detection methods. It is composed of sequential predictor, behaviour discriminator and dynamic updater, enhancing adaptive sequential detection performances. According to the framework, an effective implementation is designed to improve attack detection accuracy: (I) The sequential predictor identifies flight phases to predict sequential data effectively and accomplish model fusion to generate ADS-B predictive data sequences. (II) The behaviour discriminator utilizes value differences and contextual information to distinguish attack data from ADS-B data sequences. (III) The dynamic updater is designed to update the training data sets and discriminate threshold dynamically, improving the adaptation in face of concept drifts for ADS-B data. By experiments on real ADS-B data with diverse attack patterns, the feasibility and efficiency of the framework are validated.</p>

scholarly journals An Adaptive-Data-Driven Attack Detection Framework on ADS-B Data

2021 ◽  
Author(s):  
Tengyao Li
Keyword(s):  
Traffic Management ◽  
Contextual Information ◽  
Attack Detection ◽  
Training Data ◽  
Data Driven ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Sequential Data ◽  
Security Measures ◽  
Data Share

<p>With the variety and quantity of flights increasing, accurate and efficient surveillance methods are in great demands for the next generation air traffic management. Relying on high accuracy, wide coverage, low deployment cost and data share support, Automatic Dependent Surveillance – Broadcast (ADS-B) is becoming the primary surveillance method in 2020. However, ADS-B data is lacking of sufficient security measures to ensure data integrity and authentication, which makes it face with various attack threats. To detect the malicious data caused by attack behaviours accurately, an adaptive-data-driven attack detection framework is proposed, which is utilized to establish the consistent framework for predictive discriminant detection methods. It is composed of sequential predictor, behaviour discriminator and dynamic updater, enhancing adaptive sequential detection performances. According to the framework, an effective implementation is designed to improve attack detection accuracy: (I) The sequential predictor identifies flight phases to predict sequential data effectively and accomplish model fusion to generate ADS-B predictive data sequences. (II) The behaviour discriminator utilizes value differences and contextual information to distinguish attack data from ADS-B data sequences. (III) The dynamic updater is designed to update the training data sets and discriminate threshold dynamically, improving the adaptation in face of concept drifts for ADS-B data. By experiments on real ADS-B data with diverse attack patterns, the feasibility and efficiency of the framework are validated.</p>

scholarly journals Real-time Factory Smoke Detection Based on Two-stage Relation-guided Algorithm

2021 ◽  
Author(s):  
Zhenyu Wang ◽  
Senrong Ji ◽  
Duokun Yin
Keyword(s):  
Real Time ◽  
Contextual Information ◽  
Search Space ◽  
Detection Algorithm ◽  
Image Resolution ◽  
Single Stage ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Smoke Detection ◽  
Two Stage

Abstract Recently, using image sensing devices to analyze air quality has attracted much attention of researchers. To keep real-time factory smoke under universal social supervision, this paper proposes a mobile-platform-running efficient smoke detection algorithm based on image analysis techniques. Since most smoke images in real scenes have challenging variances, it’s difficult for existing object detection methods. To this end, we introduce the two-stage smoke detection (TSSD) algorithm based on the lightweight framework, in which the prior knowledge and contextual information are modeled into the relation-guided module to reduce the smoke search space, which can therefore significantly improve the shortcomings of the single-stage method. Experimental results show that the TSSD algorithm can robustly improve the detection accuracy of the single-stage method and has good compatibility for different image resolution inputs. Compared with various state-of-the-art detection methods, the accuracy AP mean of the TSSD model reaches 59.24%, even surpassing the current detection model Faster R-CNN. In addition, the detection speed of our proposed model can reach 50 ms (20 FPS), which meets the real-time requirements, and can be deployed in the mobile terminal carrier. This model can be widely used in some scenes with smoke detection requirements, providing great potential for practical environmental applications.

DDOS Attack Detection Strategies in Cloud A Comparative Stud

2017 ◽  
pp. 35-42
Author(s):  
◽  
◽  
Keyword(s):  
Information Technology ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Huge Number ◽  
Ddos Attack ◽  
Ddos Attack Detection ◽  
Detection Strategies ◽  
Hot Target

Cloud is known as a highly-available platform that has become most popular among businesses for all information technology needs. Being a widely used platform, it’s also a hot target for cyber-attacks. Distributed Denial of Services (DDoS) is a great threat to a cloud in which cloud bandwidth, resources, and applications are attacked to cause service unavailability. In a DDoS attack, multiple botnets attack victim using spoofed IPs with a huge number of requests to a server. Since its discovery in 1980, numerous methods have been proposed for detection and prevention of network anomalies. This study provides a background of DDoS attack detection methods in past decade and a survey of some of the latest proposed strategies to detect DDoS attacks in the cloud, the methods are further compared for their detection accuracy.

scholarly journals A PCC-Ensemble-TCN model for wind turbine icing detection using class-imbalanced and label-missing SCADA data

2021 ◽  
Vol 17 (11) ◽  
pp. 155014772110577
Author(s):  
Shenyi Ding ◽  
Zhijie Wang ◽  
Jue Zhang ◽  
Fang Han ◽  
Xiaochun Gu ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Supervisory Control ◽  
Wind Farm ◽  
Pearson Correlation ◽  
Cold Climate ◽  
Data Driven ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Convolutional Network ◽  
Original Dataset

Blade icing problems are ubiquitous for wind turbines located in cold climate zones. Data-driven indirect icing detection methods based on supervisory control and data acquisition system have shown strong potential recently. However, the supervisory control and data acquisition data is annotated through manual observation, which will cause the data between normal condition and icing condition to be unlabeled. In addition, the amount of normal data is far more than icing data. The above two issues restrict the performance of most current data-driven models. In order to solve the label missing problem, this article proposes a Pearson correlation coefficient–based algorithm for measuring the degree of blade icing, which calculates the similarity between the unlabeled data and the icing data as its label. Aiming at the class-imbalance problem, this article constructs multiple class-balanced subsets from the original dataset by under-sampling the normal data. Temporal convolutional networks are trained to extract features and make predictions on each subset. The final prediction result is obtained by ensembling the prediction results of all temporal convolutional network models. The proposed model is validated using the actual supervisory control and data acquisition data collected from a wind farm in northern China, and the results indicate that ensuring the consecutiveness and class-balance of the data are quite advantageous for improving the detection accuracy.

scholarly journals A Study of Gender Bias in Face Presentation Attack and Its Mitigation

2021 ◽  
Vol 13 (9) ◽  
pp. 234
Author(s):  
Norah Alshareef ◽  
Xiaohong Yuan ◽  
Kaushik Roy ◽  
Mustafa Atay
Keyword(s):  
Gender Bias ◽  
Attack Detection ◽  
Training Data ◽  
Latent Structure ◽  
Detection Methods ◽  
Face Presentation ◽  
Detection Technology ◽  
In The Wild ◽  
Algorithmic Bias ◽  
Presentation Attack Detection

In biometric systems, the process of identifying or verifying people using facial data must be highly accurate to ensure a high level of security and credibility. Many researchers investigated the fairness of face recognition systems and reported demographic bias. However, there was not much study on face presentation attack detection technology (PAD) in terms of bias. This research sheds light on bias in face spoofing detection by implementing two phases. First, two CNN (convolutional neural network)-based presentation attack detection models, ResNet50 and VGG16 were used to evaluate the fairness of detecting imposer attacks on the basis of gender. In addition, different sizes of Spoof in the Wild (SiW) testing and training data were used in the first phase to study the effect of gender distribution on the models’ performance. Second, the debiasing variational autoencoder (DB-VAE) (Amini, A., et al., Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure) was applied in combination with VGG16 to assess its ability to mitigate bias in presentation attack detection. Our experiments exposed minor gender bias in CNN-based presentation attack detection methods. In addition, it was proven that imbalance in training and testing data does not necessarily lead to gender bias in the model’s performance. Results proved that the DB-VAE approach (Amini, A., et al., Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure) succeeded in mitigating bias in detecting spoof faces.

scholarly journals Coordinated Cyber-Attack Detection Model of Cyber-Physical Power System Based on the Operating State Data Link

2021 ◽  
Vol 9 ◽  
Author(s):  
Lei Wang ◽  
Pengcheng Xu ◽  
Zhaoyang Qu ◽  
Xiaoyong Bo ◽  
Yunchang Dong ◽  
...  
Keyword(s):  
Power System ◽  
Principal Component ◽  
Attack Detection ◽  
Detection Methods ◽  
Gradient Boosting ◽  
Intensity Difference ◽  
Detection Accuracy ◽  
Cyber Attack ◽  
Data Link ◽  
Detection Model

Existing coordinated cyber-attack detection methods have low detection accuracy and efficiency and poor generalization ability due to difficulties dealing with unbalanced attack data samples, high data dimensionality, and noisy data sets. This paper proposes a model for cyber and physical data fusion using a data link for detecting attacks on a Cyber–Physical Power System (CPPS). The two-step principal component analysis (PCA) is used for classifying the system’s operating status. An adaptive synthetic sampling algorithm is used to reduce the imbalance in the categories’ samples. The loss function is improved according to the feature intensity difference of the attack event, and an integrated classifier is established using a classification algorithm based on the cost-sensitive gradient boosting decision tree (CS-GBDT). The simulation results show that the proposed method provides higher accuracy, recall, and F-Score than comparable algorithms.

Brief Survey on Attack Detection Methods for Cyber-Physical Systems

2020 ◽  
Vol 14 (4) ◽  
pp. 5329-5339 ◽  
Author(s):  
Sen Tan ◽  
Josep M. Guerrero ◽  
Peilin Xie ◽  
Renke Han ◽  
Juan C. Vasquez
Keyword(s):  
Cyber Physical Systems ◽  
Attack Detection ◽  
Detection Methods ◽  
Physical Systems

scholarly journals Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector

2019 ◽  
Vol 9 (6) ◽  
pp. 1128 ◽  
Author(s):  
Yundong Li ◽  
Wei Hu ◽  
Han Dong ◽  
Xueyan Zhang
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Hurricane Sandy ◽  
Training Data ◽  
Aerial Images ◽  
Detection Methods ◽  
Single Shot ◽  
Data Set ◽  
Augmentation Strategies ◽  
Post Disaster

Using aerial cameras, satellite remote sensing or unmanned aerial vehicles (UAV) equipped with cameras can facilitate search and rescue tasks after disasters. The traditional manual interpretation of huge aerial images is inefficient and could be replaced by machine learning-based methods combined with image processing techniques. Given the development of machine learning, researchers find that convolutional neural networks can effectively extract features from images. Some target detection methods based on deep learning, such as the single-shot multibox detector (SSD) algorithm, can achieve better results than traditional methods. However, the impressive performance of machine learning-based methods results from the numerous labeled samples. Given the complexity of post-disaster scenarios, obtaining many samples in the aftermath of disasters is difficult. To address this issue, a damaged building assessment method using SSD with pretraining and data augmentation is proposed in the current study and highlights the following aspects. (1) Objects can be detected and classified into undamaged buildings, damaged buildings, and ruins. (2) A convolution auto-encoder (CAE) that consists of VGG16 is constructed and trained using unlabeled post-disaster images. As a transfer learning strategy, the weights of the SSD model are initialized using the weights of the CAE counterpart. (3) Data augmentation strategies, such as image mirroring, rotation, Gaussian blur, and Gaussian noise processing, are utilized to augment the training data set. As a case study, aerial images of Hurricane Sandy in 2012 were maximized to validate the proposed method’s effectiveness. Experiments show that the pretraining strategy can improve of 10% in terms of overall accuracy compared with the SSD trained from scratch. These experiments also demonstrate that using data augmentation strategies can improve mAP and mF1 by 72% and 20%, respectively. Finally, the experiment is further verified by another dataset of Hurricane Irma, and it is concluded that the paper method is feasible.

scholarly journals OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4805
Author(s):  
Saad Abbasi ◽  
Mahmoud Famouri ◽  
Mohammad Javad Shafiee ◽  
Alexander Wong
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Network Architectures ◽  
Design Exploration ◽  
Convolutional Autoencoder ◽  
Acoustic Anomaly ◽  
Human Operators ◽  
Computational Resources

Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model’s latency show that the OutlierNet architectures can achieve as much as 30x lower latency than published networks.

scholarly journals Transcription Alignment of Historical Vietnamese Manuscripts without Human-Annotated Learning Samples

2021 ◽  
Vol 11 (11) ◽  
pp. 4894
Author(s):  
Anna Scius-Bertrand ◽  
Michael Jungo ◽  
Beat Wolf ◽  
Andreas Fischer ◽  
Marc Bui
Keyword(s):  
Object Detection ◽  
State Of The Art ◽  
Positive Impact ◽  
Detection System ◽  
Training Data ◽  
Detection Accuracy ◽  
Current State ◽  
Alignment Task ◽  
Scanned Image ◽  
Automatic Transcription

The current state of the art for automatic transcription of historical manuscripts is typically limited by the requirement of human-annotated learning samples, which are are necessary to train specific machine learning models for specific languages and scripts. Transcription alignment is a simpler task that aims to find a correspondence between text in the scanned image and its existing Unicode counterpart, a correspondence which can then be used as training data. The alignment task can be approached with heuristic methods dedicated to certain types of manuscripts, or with weakly trained systems reducing the required amount of annotations. In this article, we propose a novel learning-based alignment method based on fully convolutional object detection that does not require any human annotation at all. Instead, the object detection system is initially trained on synthetic printed pages using a font and then adapted to the real manuscripts by means of self-training. On a dataset of historical Vietnamese handwriting, we demonstrate the feasibility of annotation-free alignment as well as the positive impact of self-training on the character detection accuracy, reaching a detection accuracy of 96.4% with a YOLOv5m model without using any human annotation.

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