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Investigating the Effects of Attack detection for In-Vehicle Networks Based on Clock Drift of ECUs

IEEE ACCESS 2018年 6卷 49375-49384页

作者： Ji, Haojie Wang, Yunpeng Qin, Hongmao Wu, Xinkai Yu, Guizhen Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China

As external interfaces of vehicles multiply, information security of an automobile network system has become increasingly troubling. Mounting attacks have raised the attention of researchers to seek optimal solutions. Therefore, they set forth attack detection to demonstrate the vulnerability of in-vehicle networks, yet most of them focus on packet information directly. This paper comprehensively analyzes the vulnerability of in-vehicle networks and investigates a unique detection method based on clock drift of electronic control units. To investigate the applications of the proposed method further, we take attack time and attack density into consideration and present different patterns of two typical attack scenarios, i.e., injecting attack and suspension attack. In addition, we develop a prototype for data acquisition in a controller area network and undertake substantial vehicle experiments. The results show that the attack detection method has advantages in both recognition accuracy and application range compared with the method based on information entropy theory. This research work is expected to contribute to the further development of attacks detection system applied in vehicular networks.

关键词： Intelligent vehicles network security intrusion detection change detection algorithms

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A fast effective scene change detection and adaptive rate control algorithm

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IEEE International Conference on Image Processing

作者： Yue Yu Jian Zhou Yiliang Wang Department of Electronic Engineering Tsinghua University China

ISBN: (纸本)0818688211

The rate control algorithm of Test Model 5 (TM5) can not handle scene change properly, so the visual quality is consequently worsened. A fast effective scene changes detection method without much additional computation is proposed and an adaptive rate control algorithm is given, which not only guarantees the buffer not to overflow or underflow, but also compensates the visual degradation at scene change point and keeps consistent visual quality. Simulation results show that this algorithm can effectively improve the visual quality when scene change occurs.

关键词： Layout Programmable control Adaptive control change detection algorithms Testing Bit rate Degradation Computational modeling Digital TV Video coding

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An effective error concealment method based on abrupt scene change detection algorithm

An effective error concealment method based on abrupt scene ...

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IET International Conference on Information Science and Control Engineering 2012 (ICISCE 2012)

作者： Jielong Chen Jian Wang School of Electronic and Engineering Nanjing University China

ISBN: (纸本)9781629939254

Compressed video bit-streams are extremely sensitive to packet loss over error-prone channels. Error concealment (EC) has been considered as one of error control techniques to improve the reconstructed picture quality against transmission errors. However, EC methods show poor image reconstruction performance due to unavailable scene change information of the video sequence, especially when an abrupt scene change occurs. In this paper, we propose an effective EC method based on scene change detection algorithm (SCDA), which provides information to decide whether spatial or temporal EC is better to be used for intra and inter frames respectively. The simulation results show that the proposed method highly improves the subjective quality of incorrectly decoded frames and obtains an average gain of 0.7dB compared with the H.264/AVC Joint Model reference software.

关键词： H.264/AVC Scene change detection compressed domain change detection algorithms Data Security European Community Frame

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Cramer-Rao lower bound on locations of sudden changes in a steplike signal

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1996年第10期44卷 2551-2556页

作者： Reza, AM Doroodchi, M Department of Electrical Engineering and Computer Science University of Wisconsin Milwaukee WI USA

Estimation of locations of sudden changes in a steplike signal has many signal processing applications;e.g., well-log signal segmentation, ionic-channel signal classification, edge detection, and segmentation of images, In this work, the Cramer-Rao lower bound (CRLB) on locations of steps in one-dimensional (1-D) steplike signals is calculated. The calculation is based on the use of a sigmoidal function to model a sudden-change (step) in the signal, The introduced model has an adjustable parameter that can be used to fit the CRLB calculation to a particular class of steplike signals.

关键词： Signal processing Image segmentation Signal processing algorithms Filters Pattern classification Image edge detection Probability density function Signal detection Performance evaluation change detection algorithms

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Deep Atrous Spatial Features-Based Supervised Foreground detection Algorithm for Industrial Surveillance Systems

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第7期17卷 4818-4826页

作者： Shahbaz, Ajmal Jo, Kang-Hyun Univ Ulsan Sch Elect & Comp Engn Ulsan 44610 South Korea Univ Ulsan Dept Elect & Comp Engn Ulsan 44610 South Korea

Camera-based surveillance systems largely perform an intrusion detection task for sensitive areas. The task may seem trivial but is quite challenging due to environmental changes and object behaviors such as those due to night-time, sunlight, IR camera, camouflage, and static foreground objects, etc. Convolutional neural network based algorithms have shown promise in dealing with these challenges. However, they are exclusively focused on accuracy. This article proposes an efficient supervised foreground detection (SFDNet) algorithm based on atrous deep spatial features. The features are extracted using atrous convolution kernels to enlarge the field-of-view of a kernel mask, thereby encoding rich context features without increasing the number of parameters. The network further benefits from a residual dense block strategy that mixes the mid and high-level features to retain the foreground information lost in low-resolution high-level features. The extracted features are expanded using a novel pyramid upsampling network. The feature maps are upsampled using bilinear interpolation and pass through a 3x3 convolutional kernel. The expanded feature maps are concatenated with the corresponding mid and low-level feature maps from an atrous feature extractor to further refine the expanded feature maps. The SFDNet showed better performance than high-ranked foreground detection algorithms on the three standard databases. The testing demo can be found at https://***/file/d/1z_zEj9Yp7GZeM2gSIwYKvSzQlxMAiarw/view?usp=sharing.

关键词： Feature extraction Convolution Kernel Surveillance Standards detection algorithms change detection algorithms Convolutional neural networks (CNN) dual-camera sensors infrared (IR) camera intelligent surveillance systems

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change detection with 1 m resolution satellite and aerial images

Change detection with 1 m resolution satellite and aerial im...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： H. Spitzer R. Franck M. Kollewe N. Rega A. Rothkirch R. Wiemker II. Institut für Experimentalphysik Universit#x00E4 t Hamburg Hamburg Germany

We propose an optimization of a computer based change detection technique based on Iterative Principal Component Analysis (IPCA). We determine and evaluate the changes between an airborne and a spaceborne multispectral image data set, the latter recorded by the commercial satellite IKONOS-2. The change detection algorithm proved to be applicable to large remotely sensed data sets. A vegetation filter, a shadow filter and an oversaturation filter improved the accuracy of the results. When applying all filters more than 80 percent of the objects with changes due to construction activity are detected by the IPCA algorithm. The false alarm rate (change of an object indicated but not verified) is about 5 percent.

关键词： Image resolution Satellites Principal component analysis Vegetation mapping Iterative algorithms Chromium Multispectral imaging Optical filters change detection algorithms Optical scattering

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change detection Method of High Resolution Remote Sensing Image Based on UNet Network

Change Detection Method of High Resolution Remote Sensing Im...

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Intelligent Computing and Human-Computer Interaction (ICHCI), International Conference on

作者： Zihao Liu Yu Zhu Chengkun Zhang Qinghai Provincial Laboratory for Intelligent Computing and Application Qinghai University Xining Qinghai China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

To address the issues of inadequate feature extraction, complicated models, and a large number of network parameters, this paper proposes a change detection method for high-resolution remote sensing images based on UNet network. Firstly, Resnet50 network is selected in the encoder part to obtain deeper features, avoid gradient disappearance and reduce training time. The scSE attention module structure is introduced in the skip connection. This module enhances the network's ability to prioritize the important channels and mine the local information better. Then, in order to improve the segmentation accuracy and obtain the context information better, atrous spatial pyramid pool is carried out in the deepest feature layer of the encoder to enrich the representation of information and obtain the multi-scale information of the target. Finally, the combination of Focal loss function and BCE loss weight was presented with improving model learning strategy. On the LEVIR-CD and CDD datasets, it is shown that proposed method has improved precision rate, recall rate and F1, and the number of model parameters is 5.37M, which is superior to other commonly used change detection networks.

关键词： Training Human computer interaction Accuracy Image resolution change detection algorithms Feature extraction Sensors Remote sensing Residual neural networks Context modeling

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Forest Disaster detection Method Based on Ensemble Spatial-Spectral Genetic Algorithm

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 7375-7390页

作者： Cao, Yang Feng, Wei Quan, Yinghui Bao, Wenxing Dauphin, Gabriel Ren, Aifeng Yuan, Xiaoguang Xing, Mengdao Xidian Univ Sch Elect Engn Dept Remote Sensing Sci & Technol Xian 710071 Peoples R China North Minzu Univ Sch Comp Sci & Engn Yinchuan 750021 Ningxia Peoples R China Univ Paris XIII Inst Galilee Lab Informat Proc & Transmiss F-93430 Paris France Xidian Univ Acad Adv Interdisciplinary Res Xian 710071 Peoples R China

Remote sensing image change detection is the key technology for monitoring forest windfall damages. A genetic algorithm (GA) is a branch of intelligent optimization techniques available to contribute to the surveys of windstorm and wildfire detection in forest areas. However, traditional GAs remain challenging due to several issues, such as complex calculation, poor noise immunity, and slow convergence. Analysis at the spatial level allows classifications to utilize the contextual and hierarchical information of image objects in addition to solely using spectral information. In addition, ensemble learning presents a possibility for improving classification accuracy. Ensemble classifiers combined with the spatial-based GA offers a promising method [ensemble spatial-spectral genetic algorithm (E-nGA)] for automating the process of monitoring forest loss. The research in this article is presented in four parts. First, block-matching and 3-D filtering is performed to suppress noises while enhancing valuable information. The difference image is, then, generated using the image difference method. Afterward, context-based saliency detection and fuzzy c-means algorithm are conducted on the difference image to reduce the search space. Finally, the proposed E-nGA is executed to further classify the pixels and produce the final change map. Our first proposition is to design improved genetic operators in the GA, relying not only on pixel values but also on spatial information. Our second proposition is to consider an ensemble classification model based on multiple vegetation features for decision integration. Six frequently used classification methods, as well as the simple GA, are executed to demonstrate the effectiveness of the proposed framework in improving the robustness and detection accuracy.

关键词： Forestry Genetic algorithms Storms Vegetation mapping Linear programming change detection algorithms Remote sensing Decision ensemble forest disaster detection genetic algorithm (GA) locality window multispectral vegetation features

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A Unified Framework for Multimodal Submodular Integrated Circuits Trojan detection

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2011年第1期6卷 162-174页

作者： Koushanfar, Farinaz Mirhoseini, Azalia Rice Univ Dept Elect & Comp Engn Houston TX 77005 USA

This paper presents a unified formal framework for integrated circuits (ICs) Trojan detection that can simultaneously employ multiple noninvasive side-channel measurement types (modalities). After formally defining the IC Trojan detection for each side-channel measurement and analyzing the complexity, we devise a new submodular formulation of the problem objective function. Based on the objective function properties, an efficient Trojan detection method with strong approximation and optimality guarantees is introduced. Signal processing methods for calibrating the impact of interchip and intrachip correlations are presented. We define a new sensitivity metric that formally quantifies the impact of modifications to each existing gate that is affected by Trojan. Using the new metric, we compare the Trojan detection capability of different measurement types for static (quiescent) current, dynamic (transient) current, and timing (delay) side-channel measurements. We propose four methods for combining the detection results that are gained from different measurement modalities and show how the sensitivity results can be used for a systematic combining of the detection results. Experimental evaluations on benchmark designs reveal the low-overhead and effectiveness of the new Trojan detection framework and provides a comparison of different detection combining methods.

关键词： change detection algorithms circuit Trojan detection gate-level characterization hardware malware detection hardware security and trust submodular functions timing/power tests

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Unsupervised Land Cover change Estimation Using Region Covariance Estimates

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2019年第3期16卷 347-351页

作者： Olding, W. C. Olivier, J. C. Salmon, B. P. Kleynhans, W. Univ Tasmania Sch Engn Hobart Tas 7005 Australia CSIR Meraka Inst Remote Sensing Res Unit ZA-0081 Pretoria South Africa

In this letter, we demonstrate the utility of estimating a probabilistic model of the underlying seasonal and interannual variations experienced by land cover time series in a given geographical region. Time series that deviate from these trajectories due to the human-induced change appear as outliers and can be detected using their Mahalanobis distance from the mean under the joint distribution of time samples. We apply this model to a collection of pixel time series acquired by the Moderate Resolution Imaging Spectroradiometer platform over Limpopo province, South Africa, for the task of identifying human settlement expansion. For estimation of the time of change, we present a hypothesis testing approach that tests for a decrease in correlation between samples before and after the change. This was found to be highly effective, yielding a mean absolute error of 52 days.

关键词： change detection algorithms covariance matrices density estimation robust algorithm remote sensing time series analysis

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