检索结果-内蒙古大学图书馆

Automatic detection of QRS Complexes Using Dual Channels Based on U-Net and Bidirectional Long Short-Term Memory

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2021年第4期25卷 1052-1061页

作者： He, Runnan Liu, Yang Wang, Kuanquan Zhao, Na Yuan, Yongfeng Li, Qince Zhang, Henggui Peng Cheng Lab Shenzhen 518055 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China Univ Manchester Dept Phys & Astron Manchester Lancs England Pilot Natl Lab Marine Sci & Technol Qingdao Peoples R China Northeastern Univ Minist Educ Int Lab Smart Syst Shenyang 110004 Peoples R China Northeastern Univ Minist Educ Key Lab Intelligent Comp Med Image Shenyang 110004 Peoples R China

Objective: Detecting changes in the QRS complexes in ECG signals is regarded as a straightforward, noninvasive, inexpensive, and preliminary diagnosis approach for evaluating the cardiac health of patients. Therefore, detecting QRS complexes in ECG signals must be accurate over short times. However, the reliability of automatic QRS detection is restricted by all kinds of noise and complex signal morphologies. The objective of this paper is to address automatic detection of QRS complexes. Methods: In this paper, we proposed a new algorithm for automatic detection of QRS complexes using dual channels based on U-Net and bidirectional long short-term memory. First, a proposed preprocessor with mean filtering and discrete wavelet transform was initially applied to remove different types of noise. Next the signal was transformed and annotations were relabeled. Finally, a method combining U-Net and bidirectional long short-term memory with dual channels was used for the automatic detection of QRS complexes. Results: The proposed algorithm was trained and tested using 44 ECG records from the MIT-BIH arrhythmia database and CPSC2019 dataset, which achieved 99.06% and 95.13% for sensitivity, 99.22% and 82.03% for positive predictivity, and 98.29% and 78.73% accuracy on the two datasets respectively. Conclusion: Experimental results prove that the proposed method may be useful for automatic detection of QRS complex task. Significance: The proposed method not only has application potential for QRS complex detecting for large ECG data, but also can be extended to other medical signal research fields.

关键词： Electrocardiography Feature extraction Discrete wavelet transforms Filtering Cutoff frequency detection algorithms Filtering algorithms Electrocardiogram (ECG) QRS complex detection U-Net Bidirectional long short-term memory (LSTM)

来源：评论

学校读者我要写书评

暂无评论

Hey ChatGPT, is this message phishing? 22

Hey ChatGPT, is this message phishing?

引用

22nd Mediterranean Communication and Computer Networking Conference (MedComNet)

作者： De Rosa, Sara Gringoli, Francesco Bellicini, Gabriele Univ Brescia Dept Informat Engn Brescia Italy Verxo SrL Brescia Italy

ISBN: (纸本)9798350390476;9798350390483

This paper investigates the effectiveness of AI-based tools, with a focus on utilizing ChatGPT as a test platform, in email phishing detection, addressing the pressing need to combat this pervasive threat in the digital landscape. With phishing attacks causing substantial financial losses amounting to billions of dollars annually, innovative approaches are essential to mitigate their impact. We propose leveraging ChatGPT for email phishing detection, capitalizing on its machine learning and natural language processing capabilities to classify emails based on nuanced patterns and variations in phishing techniques. Unlike traditional rule-based approaches, AI-based tools offer a promising avenue for enhanced detection accuracy. Through rigorous testing focusing on binary mail classification, incorporating metrics such as accuracy, specificity, sensitivity, precision, and the F1 Score, ChatGPT demonstrates commendable performance, achieving an overall accuracy of 75.75% and an impressive sensitivity of 98.4%. These findings underscore the potential of AI-based tools as valuable assets in mitigating phishing threats. Comparison with FortiSandbox, a widely-used solution, reveals promising potential for AI-based tools, albeit with recognition of the need for further development and refinement. Our evaluation, encompassing various aspects of .eml file analysis, highlights both the strengths and limitations of AI-based tools in identifying phishing emails. While this research lays a strong foundation for leveraging AI-based tools in email phishing detection, there exist opportunities for improvement. Addressing identified limitations and exploring future research directions will be crucial in advancing the efficacy of AI-based tools and enhancing email security measures against cyber threats.

关键词： ChatGPT phishing email Artificial Intelligence cybersecurity machine learning natural language processing detection algorithms

来源：评论

学校读者我要写书评

暂无评论

A Novel Local Community detection Method Using Evolutionary Computation

引用

IEEE TRANSACTIONS ON CYBERNETICS 2021年第6期51卷 3348-3360页

作者： Lyu, Chao Shi, Yuhui Sun, Lijun Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Key Lab Computat Intelligence Shenzhen 518055 Peoples R China

The local community detection is a significant branch of the community detection problems. It aims at finding the local community to which a given starting node belongs. The local community detection plays an important role in analyzing the complex networks and recently has drawn much attention from the researchers. In the past few years, several local community detection algorithms have been proposed. However, the previous methods only make use of the limited local information of networks but overlook the other valuable information. In this article, we propose an evolutionary computation-based algorithm called evolutionary-based local community detection (ELCD) algorithm to detect local communities in the complex networks by taking advantages of the entire obtained information. The performance of the proposed algorithm is evaluated on both synthetic and real-world benchmark networks. The experimental results show that the proposed algorithm has a superior performance compared with the state-of-the-art local community detection methods. Furthermore, we test the proposed algorithm on incomplete real-world networks to show its effectiveness on the networks whose global information cannot be obtained.

关键词： Optimization Complex networks Image edge detection Measurement Evolutionary computation detection algorithms Heuristic algorithms Community detection evolutionary computation (EC) local community detection

来源：评论

学校读者我要写书评

暂无评论

detection of Message Injection Attacks Onto the CAN Bus Using Similarities of Successive Messages-Sequence Graphs

引用

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2021年 16卷 4133-4146页

作者： Jedh, Mubark Ben Othmane, Lotfi Ahmed, Noor Bhargava, Bharat Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA Air Force Res Lab Rome NY 13441 USA Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA

The smart features of modern cars are enabled by a number of Electronic Control Units (ECUs) components that communicate through an in-vehicle network, known as Controller Area Network (CAN) bus. The fundamental challenge is the security of the communication link where an attacker can inject messages (e.g., increase the speed) that may impact the safety of the driver. Most of existing practical IDS solutions rely on the knowledge of the identity of the ECUs, which is proprietary information. This paper proposes a message injection attack detection solution that is independent of the IDs of the ECUs. First, we represent the sequencing of the messages in a given time-interval as a direct graph and compute the similarities of the successive graphs using the cosine similarity and Pearson correlation. Then, we apply threshold, change point detection, and Long Short-Term Memory (LSTM)-Recurrent Neural Network (RNN) to detect and predict malicious message injections into the CAN bus. The evaluation of the methods using a dataset collected from a moving vehicle under malicious RPM and speed reading message injections show a detection accuracy of 97.32% and detection speed of 2.5 milliseconds when using a threshold method. The performance metrics makes the IDS suitable for real-time control mechanisms for vehicle resiliency to cyber-attacks.

关键词： Security Automobiles Connected vehicles Protocols Safety Universal Serial Bus Time-frequency analysis Industry applications security information security intrusion detection intelligent transportation systems transportation vehicle mathematics algorithms detection algorithms

来源：评论

学校读者我要写书评

暂无评论

Hybrid MAP and PIC detection for OTFS Modulation

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第7期70卷 7193-7198页

作者： Li, Shuangyang Yuan, Weijie Wei, Zhiqiang Yuan, Jinhong Bai, Baoming Ng, Derrick Wing Kwan Xie, Yixuan Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Kensington NSW 2052 Australia Friedrich Alexander Univ Erlangen Nuremberg Dept Elect Engn Elect Informat Technol D-91054 Erlangen Germany Xidian Univ State Key Lab ISN Xian 710071 Peoples R China

Orthogonal time frequency space (OTFS) modulation has attracted substantial attention recently due to its great potential of providing reliable communications in high-mobility scenarios. In this article, we propose a novel hybrid signal detection algorithm for OTFS modulation. Based on the system model, we first derive the near-optimal symbol-wise maximum a posteriori (MAP) detection algorithm for OTFS modulation. Then, in order to reduce the detection complexity, we propose a partitioning rule that separates the related received symbols into two subsets for detecting each transmitted symbol, according to the corresponding path gains. According to the partitioning rule, we design the hybrid detection algorithm to exploit the power discrepancy of each subset, where the MAP detection is applied to the subset with larger channel gains, while the parallel interference cancellation (PIC) detection is applied to the subset with smaller channel gains. We also provide the error performance analysis of the proposed hybrid detection algorithm. Simulation results show that the proposed hybrid detection algorithm can not only approach the performance of the near-optimal symbol-wise MAP algorithms, but also offer a substantial performance gain compared with existing algorithms.

关键词： Modulation Delays Complexity theory Time-frequency analysis detection algorithms Partitioning algorithms Approximation algorithms Orthogonal time frequency space (OTFS) reduced-complexity detection sum-product algorithm

来源：评论

学校读者我要写书评

暂无评论

On The detection Of Adversarial Attacks Through Reliable AI

On The Detection Of Adversarial Attacks Through Reliable AI

引用

41st IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Vaccari, Ivan Carlevaro, Alberto Narteni, Sara Cambiaso, Enrico Mongelli, Maurizio Consiglio Nazl Ric CNR IEIIT Inst Genoa Italy Univ Genoa DITEN Dept CNR IEIIT Genoa Italy

ISBN: (纸本)9781665409261

Adversarial machine learning manipulates datasets to mislead machine learning algorithm decisions. We propose a new approach able to detect adversarial attacks, based on eXplainable and Reliable AI. The results obtained show how canonical algorithms may have difficulty in identifying attacks, while the proposed approach is able to correctly identify different adversarial settings.

关键词： machine learning detection algorithms adversarial machine learning reliable

来源：评论

学校读者我要写书评

暂无评论

A DVB-S-Based Multichannel Passive Radar System for Vehicle detection

引用

IEEE ACCESS 2021年 9卷 2900-2912页

作者： Li, Junjie Wei, Junkang Cao, Zhihui Chen, Qin Yang, Lijie Song, Chunyi Xu, Zhiwei Zhejiang Univ Ocean Coll Inst Marine Elect & Intelligent Syst Zhoushan 316021 Peoples R China Minist Educ Engn Res Ctr Ocean Sensing Technol & Equipment Zhoushan 316021 Peoples R China Key Lab Ocean Observat Imaging Testbed Zhejiang P Zhoushan 316021 Peoples R China Zhejiang Lab Hangzhou 310006 Peoples R China

Vehicle detection systems play a significant role in intelligent transportation systems (ITSs). At present, vehicle detection is realized either by very costly inductive loops, by video cameras that require good weather conditions and high computing resources, or by recently developed magnetic sensors that only obtain limited detection accuracy in congested traffic conditions. To overcome these challenges, this paper develops a passive radar system that is robust to bad weather conditions, low cost and radio silent for vehicle detection. The device is installed at the side of the road under crowded traffic conditions. In the developed system, cross-correlation (CC) operation is applied to obtain suboptimal detection gain and computational complexity. To decrease the impact of interference that is independently and identically distributed (i.i.d.) across isolated frequency channels and that of the Doppler effect produced by a mobile vehicle, a multichannel joint detection scheme based on multichannel fusion (MCF) is proposed and implemented. Furthermore, to achieve high resolution, an illuminator of opportunity (IO) with wide bandwidth is selected, and the system is then designed to receive a digital video broadcast-satellite (DVB-S) signal with a bandwidth of 37.5 MHz over multiple channels simultaneously. Simulation results show that the proposed MCF achieves better performance than the single-channel-based CC, and the advantage can be enhanced by increasing the number of channels for joint detection. Field test results further verify the novelty of the MCF and the developed passive radar prototype.

关键词： Intelligent transportation systems vehicle detection passive radar detection algorithms digital video broadcasting-satellite (DVB-S) multichannel fusion

来源：评论

学校读者我要写书评

暂无评论

UN-AVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant detection Scoring

引用

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2021年 16卷 5195-5210页

作者： Yousef, Waleed A. Traore, Issa Briguglio, William Univ Victoria Dept Elect & Comp Engn Victoria BC V8P 5C2 Canada Helwan Univ Dept Comp Sci Human Comp Interact Lab HCILab Cairo 11795 Egypt

The visualization and detection of anomalies (outliers) are of crucial importance to many fields, particularly cybersecurity. Several approaches have been proposed in these fields, yet to the best of our knowledge, none of them has fulfilled both objectives, simultaneously or cooperatively, in one coherent framework. Moreover, the visualization methods of these approaches were introduced for explaining the output of a detection algorithm, not for data exploration that facilitates a standalone visual detection. This is our point of departure in introducing UN-AVOIDS, an unsupervised and nonparametric approach for both visualization (a human process) and detection (an algorithmic process) of outliers, that assigns invariant anomalous scores (normalized to [0, 1]), rather than hard binary-decision. The main aspect of novelty of UN-AVOIDS is that it transforms data into a new space, which is introduced in this paper as neighborhood cumulative density function (NCDF), in which both visualization and detection are carried out. In this space, outliers are remarkably visually distinguishable, and therefore the anomaly scores assigned by the detection algorithm achieved a high area under the ROC curve (AUC). We assessed UN-AVOIDS on both simulated and two recently published cybersecurity datasets, and compared it to three of the most successful anomaly detection methods: LOF, IF, and FABOD. In terms of AUC, UN-AVOIDS was almost an overall winner with a margin that varied between -0.028 and 0.125, depending on the data. The article concludes by providing a preview of new theoretical and practical avenues for UN-AVOIDS. Among them is designing a visualization aided anomaly detection (VAAD), a type of software that aids analysts by providing UN-AVOIDS' detection algorithm (running in a back engine), NCDF visualization space (rendered to plots), along with other conventional methods of visualization in the original feature space, all of which are linked in one interactive enviro

关键词： Visualization Data visualization Anomaly detection Feature extraction detection algorithms Computer security Complexity theory Unsupervised nonparametric visualization outliers anomaly intrusion detection intrusion analysis

来源：评论

学校读者我要写书评

暂无评论

Proactive Image Manipulation detection

Proactive Image Manipulation Detection

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Asnani, Vishal Yin, Xi Hassner, Tal Liu, Sijia Liu, Xiaoming Michigan State Univ E Lansing MI 48824 USA Meta AI Menlo Pk CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Image manipulation detection algorithms are often trained to discriminate between images manipulated with particular Generative Models (GMs) and genuine/real images, yet generalize poorly to images manipulated with GMs unseen in the training. Conventional detection algorithms receive an input image passively. By contrast, we propose a proactive scheme to image manipulation detection. Our key enabling technique is to estimate a set of templates which when added onto the real image would lead to more accurate manipulation detection. That is, a template protected real image, and its manipulated version, is better discriminated compared to the original real image vs. its manipulated one. These templates are estimated using certain constraints based on the desired properties of templates. For image manipulation detection, our proposed approach outperforms the prior work by an average precision of 16% for CycleGAN and 32% for GauGAN. Our approach is generalizable to a variety of GMs showing an improvement over prior work by an average precision of 10% averaged across 12 GMs. Our code is available at https://***/visha13477/proactive_IMD.

关键词： Training Computer vision Codes Cameras Pattern recognition detection algorithms

来源：评论

学校读者我要写书评

暂无评论

Low-complexity detection for uplink massive MIMO SCMA systems

引用

IET COMMUNICATIONS 2021年第1期15卷 51-59页

作者： Sharma, Sanjeev Deka, Kuntal Beferull-Lozano, Baltasar BHU Indian Inst Technol Varanasi Uttar Pradesh India Indian Inst Technol Veling Goa India Univ Agder Dept Informat & Commun Technol Grimstad Norway

This paper presents a sparse code multiple access (SCMA) system with massive antennas at the base station. This system is referred to as M-SCMA system. A spectrally-efficient and massive access next-generation wireless network is realized through massive antennas and non-orthogonal SCMA techniques. Two detection algorithms, namely, modified message passing algorithm (MMPA) and extended message passing algorithm (EMPA) are proposed to detect multiple users' symbols in M-SCMA. A deep learning (DL)-based detection scheme is also proposed for M-SCMA so as to avoid channel estimation and to lower the detection complexity. Numerical results show that the DL-based detection has similar performance as MMPA even when the channel information is not estimated explicitly. Furthermore, authors also establish the sum rate trade-off between SCMA and orthogonal multiple access in a massive antenna system. The impact of various M-SCMA parameters such as the number of antennas and the overloading factor, on the proposed DL, MMPA, and EMPA-based detection are also investigated.

关键词： multi-access systems massive access next-generation wireless network Communications computing multiple users orthogonal multiple access nonorthogonal SCMA techniques deep learning (artificial intelligence) massive antennas extended message passing algorithm channel estimation detection complexity MMPA Multiple access communication base station low-complexity detection sparse code multiple access system spectrally-efficient access next-generation wireless network telecommunication computing message passing M-SCMA system antenna arrays Antenna arrays detection algorithms uplink massive MIMO SCMA systems EMPA-based detection next generation networks Radio links and equipment Communication channel equalisation and identification M-SCMA parameters massive antenna system MIMO communication deep learning-based detection scheme

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：