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作者： Duan, Lijuan Lian, Zhaoyang Chen, Juncheng Qiao, Yuanhua Miao, Jun Li, Mingai Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Trusted Computing Beijing University of Technology Beijing China National Engineering Laboratory for Key Technologies of Information Security Level Protection Beijing University of Technology Beijing China Applied Sciences Beijing University of Technology Beijing China School of Computer Science Beijing Information Science and Technology University Beijing China

Epilepsy seriously damages the physical and mental health of patients. Detection of epileptic EEG signals in different periods can help doctors diagnose the disease. The change of frequency components during epilepsy seizures is obvious, and there may be noises in epilepsy EEG signals. Moreover, epileptic seizures are closely related to the release of neuronal spiking in the brain. In this paper, we propose an approach for epilepsy period classification based on combination feature extraction methods and spiking swarm intelligent optimization classification algorithm. First, combination feature extraction methods take in account both the time-frequency features and principal component features of epilepsy. The time-frequency features are obtained by WPT or STFT-PSD, and noises are removed while extracting principal component features by PCA. Second, spiking swarm intelligent optimization classification algorithm takes advantage of individual cooperation and information interaction with strong robustness. Its simulated neurons are closer to reality, which consider more information and obtain stronger computing power. The experimental results show that the average classification accuracy of the proposed method can reach 98.95% and the highest classification accuracy can reach 100%. Compared with other methods, the proposed method has the best classification performance. © 2020 John Wiley & Sons, Ltd.

关键词： Classification (of information)

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Classifying Arabian Gulf Tweets to Detect People's Trends: A case study

Classifying Arabian Gulf Tweets to Detect People's Trends: A...

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International Conference on Social Networks Analysis, Management and security (SNAMS)

作者： Khaled Balhaf Omar Darwish Emad Rawashdeh Mohammad Abu Awad Dirar Darweesh Yahya Tashtoush Saif Rawashdeh Department of Computer Science Jordan University of Science and Technology Irbid Jordan Information Security and Applied Computing Eastern Michigan University Ypsilanti USA

Recently, media and business companies are utilizing social media to reach a large set of users to maximize the amount of gained profit. Actually, these companies are looking for the best ways to satisfy their user's requirements. It is very difficult to understand these requirements because of the large set of users on social media like Twitter. For this reason, the goal of our research project is to build a classifier that can detect Arabian trends among Gulf area Twitter users. The new built classifier can assist these companies to deliver the convenient products and media contents like photos and videos according to users' trends. By using our own designed Java-based tool, we have collected a significant dataset of tweets. Also, two experiments of tweet classification have been implemented to compare the effects of balanced and imbalanced training data and to measure the effect of data size on the accuracy of classifiers. In both experiments, Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Naïve Bayes algorithms are used as classifiers. The first experiment uses small, imbalanced data sets and four classes of data, which are Sport, Politics, Islam and Culture. The Light and Root Stemmers were used with each classifier. The best outcome achieved in our research project by utilizing a Naïve Bayes algorithm with the Light Stemmer technique. It achieved an accuracy reaching 76.27%. In the second experiment, we used a balanced large data set with the same classifiers. In addition, we have added one more class to the new data set which is Economics. The experimental results showed that the best accuracy (81.17%) is obtained by using SVM with the Light Stemmer method. The Light Stemmer achieved the best outcomes for all classifiers since almost all of the tweets were written in dialects.

关键词： Support vector machines Social networking (online) Blogs Training data Companies Media Market research

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A Trusted Authentication Scheme for IoT-based Smart Grid Applications

A Trusted Authentication Scheme for IoT-based Smart Grid App...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Fathi Amsaad Selçuk Köse School of Information Security and Applied Computing Eastern Michigan University Ypsilanti Michigan Electrical and Computer Engineering University of Rochester Rochester New York

ISBN: (数字)9781728155036

ISBN: (纸本)9781728155043

Advanced metering infrastructure (AMI) network, which is an example of the benefits of IoT technology in the power grid system, offers two-way communication between various power smart grid nodes (i.e., smart meters, aggregator, and the utility company) to enable efficient and reliable power delivery. This type of communication within a smart grid, however, brings several challenges in protecting these nodes from various types of cyber attacks. An efficient and dynamic hardware-oriented authentication and key management scheme for AMI networks is proposed in this paper as a proof of concept, to demonstrate the potential benefits of IoT technology in smart cities. The proposed scheme is based on a dynamic physical unclonable function (PUF) architecture that extracts a large set of reliable secret keys to authenticate AMI nodes over their corresponding lifetimes provided by the manufacturer. For a secure exchange of the secret keys while preserving privacy, a lightweight encryption scheme is utilized to authenticate AMI smart meters without disclosing the the keys that are exchanged and/or the identity of the meter. Furthermore, the proposed framework will enable different security levels defined by the standards of the National Institute of Science and Technology (NIST) to ensure a secure communication between AMI constituents. The proposed scheme is implemented on 10 FPGA boards (Nexys4 DDR Artix-7 with 28 nm). The experimental results demonstrate that the proposed scheme is highly secure and efficient in terms of authentication time and data storage requirements.

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A Classifier to Detect Stream Applications Based on Network Traffic Statistical Metrics 23

A Classifier to Detect Stream Applications Based on Network ...

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23rd International Arab Conference on information Technology, ACIT 2022

作者： Darwish, Omar Herzallah, Sarah Maabreh, Majdi Al-Eidi, Shouroq Al-Maani, Mahmoud Tashtoush, Yahya Eastern Michigan University Information Security and Applied Computing YpsilantiMI United States Eastern Michigan University Electrical and Computer Engineering YpsilantiMI United States The Hashemite University Department of Information Technology Zarqa Jordan Tafila Technical University Computer Science Department Tafila Jordan Jordan University of Science and Technology Department of Computer Science Irbid Jordan

ISBN: (纸本)9798350320244

One of the most crucial considerations, when considering security vulnerabilities, is network traffic. There is still potential for more research on the inter-arrival time side, even though some studies concentrate on network traffic from the perspective of the packet fields such as packet length and packet number. Inter-arrival timings are crucial to investigate because there are numerous attacks, such as Covert Timing Channels attacks, that heavily rely on them. In this article, we conduct a statistical analysis of the TCP inter-arrival times of two major key streaming programs (Zoom and Skype), which are frequently used, particularly during and following the COVID-19 pandemic. Using two internet-connected devices and the statistical measures of TCP, a dataset of 18,371 instances is created for this use. Five machine learning algorithms are evaluated on balanced and imbalanced forms of the dataset. The results revealed that the traffic of Zoom and Skype calls can be identified by machine learning algorithms with an accuracy of up to 99% by random forest. © 2022 IEEE.

关键词： Transmission control protocol

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Practical synchronization on complex dynamical networks via optimal pinning control

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Physical Review E 2015年第1期92卷 010903(R)-010903(R)页

作者： Kezan Li Weigang Sun Michael Small Xinchu Fu School of Mathematics and Computing Science Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin 541004 People's Republic of China Institute of Applied Mathematics and Engineering Computations Hangzhou Dianzi University Hangzhou 310018 People's Republic of China School of Mathematics and Statistics The University of Western Australia Crawley Western Australia 6009 Australia Department of Mathematics Shanghai University Shanghai 200444 People's Republic of China

We consider practical synchronization on complex dynamical networks under linear feedback control designed by optimal control theory. The control goal is to minimize global synchronization error and control strength over a given finite time interval, and synchronization error at terminal time. By utilizing the Pontryagin's minimum principle, and based on a general complex dynamical network, we obtain an optimal system to achieve the control goal. The result is verified by performing some numerical simulations on Star networks, Watts-Strogatz networks, and Barabási-Albert networks. Moreover, by combining optimal control and traditional pinning control, we propose an optimal pinning control strategy which depends on the network's topological structure. Obtained results show that optimal pinning control is very effective for synchronization control in real applications.

关键词： OPTIMAL control theory SYNCHRONIZATION TOPOLOGY FEEDBACK control systems PONTRYAGIN'S minimum principle

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A Novel 3D Medical Image Segmentation Model Using Improved SAM

A Novel 3D Medical Image Segmentation Model Using Improved S...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Kuang, Yuansen Ma, Xitong Zhao, Jing Wang, Guangchen Zeng, Yijie Liu, Song Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

3D medical image segmentation is an essential task in the medical image field, which aims to segment organs or tumours into different labels. A number of issues exist with the current 3D medical image segmentation task: existing models cannot simultaneously obtain the space correlation and depth correlation of 3D slices;previous models suffer from local detail loss of positional embedding in 3D images;previous approaches often have blurring of boundaries in segmenting 3D images. To solve these shortcomings, we propose a 3D medical image segmentation model named TPM-SAM. In our model, we design a twinchannel image encoder to simultaneously capture the space correlation and depth correlation of 3D slices through a multi-head attention mechanism and improved adapters. Furthermore, we design a prompt encoding generator, which divides the volumetric image into small blocks and better captures the local detail information. In addition, we introduce a multi-layer aggregation decoder by employing U-Net with multi-level skip connection to solve the blurring of boundaries in processing 3D images. Finally, we experimented and evaluated our model on KiTS21 and LiTS17 datasets to compare with other baseline models. © 2024 IEEE.

关键词： Image segmentation

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Earthquake Damage Detection Using Before and After Earthquake Satellite Images

Earthquake Damage Detection Using Before and After Earthquak...

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IEEE International Conference on Electro information Technology

作者： Kasim A. Korkmaz Munther Abualkibash Eastern Michigan University School of Visual and Built Environments Ypsilanti MI Eastem Michigan University School of Information Security and Applied Computing Ypsilanti MI

After experiencing many earthquakes all around the world, earthquake damage assessment has attracted a great deal of attention from both researchers and practitioners. Complexity and uncertainty in many practical problems require new methods and tools. Image processing is one of these tools that can be used as an efficient process for greater rewards. Implementation of image processing in earthquake damage detection after an earthquake allows rapid and accurate response. Using before and after event images makes damage inspection possible right after an earthquake occurs. In this paper, image processing and computer vision technologies were applied for earthquake damage detection. A city model was used for a representative damage detection process. In addition, satellite images before and after the March 11, 2011 Miyagi earthquake in Japan were used to detect earthquake damage. Visual damage inspection of structures was carried out considering urban areas comparing pre and post event images. Damage assessment using image differences gives a damage profile in the structural areas of residential buildings, highway bridges, and infrastructures. Such assessment can be used for immediate response by the rescue teams after an earthquake to locate the damaged urban surfaces.

关键词： practitioners Complexity uncertainty

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A building block for awareness in technical systems: Online novelty detection and reaction with an application in intrusion detection

A building block for awareness in technical systems: Online ...

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International Conference on Awareness Science and Technology (iCAST)

作者： Christian Gruhl Bernhard Sick Arno Wacker Sven Tomforde Jörg Hähner Universitat Kassel Kassel Hessen DE Applied Information Security University of Kassel Organic-Computing Augsburg University Universitat Augsburg Augsburg Bayern DE

In this article we propose a new building block to realize awareness in technical systems, a two-stage algorithm for online novelty detection and reaction in a probabilistic framework. It uses a combination of parametric as well as nonparametric density modeling techniques. First, observed samples are categorized as potentially novel. Then, clusters of potentially novel samples are identified and finally probabilistic models of the observed environment are extended by adding new model components that describe the novel process. To demonstrate the applicability of the proposed algorithm in self-adapting technical systems, we investigate a case study in the field of intrusion detection, where new kinds of attacks have to be identified by an intrusion detection system. That is, the algorithm is used in this article to realize environment-awareness, but it could in principal be taken for self- or context-awareness mechanisms, too.

关键词： Clustering algorithms Training Noise Intrusion detection Adaptation models Probabilistic logic Kernel

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A Communication-efficient Conjunctive Query Scheme under Local Differential Privacy

A Communication-efficient Conjunctive Query Scheme under Loc...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Ellen Z. Zhang Yunguo Guan Rongxing Lu Harry Zhang Faculty of Computer Science University of New Brunswick Fredericton Canada School of Information Security and Applied Computing Eastern Michigan University Ypsilanti MI USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Crowdsourcing has become a widely used method for data collection and analysis, yet its privacy remains a challenge. In this paper, we present a new efficient and privacy-preserving conjunctive query scheme for crowdsourcing scenarios. The scheme employs the Local Differential Privacy (LDP) technique to ensure both query privacy and high communication efficiency. Specifically, when an aggregator launches a conjunctive query to a set of crowdsourcing users, the query condition will not be leaked. To respond the query, each user just needs to return one bit back to the aggregator. By integrating prefix encoding technique, our proposed scheme can also efficiently support conjunctive queries with one range query condition. Detailed security analysis shows our proposed scheme can achieve desirable security requirements. In addition, performance evaluations also indicate its efficiency. Furthermore, extensive experiments demonstrate our proposed scheme can achieve high accuracy while ensuring ε-LDP.

关键词： Crowdsourcing Performance evaluation Differential privacy Privacy Accuracy Data collection Encoding security Global communication

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CNNs in Crop Care: A Comparative Analysis of Tomato Disease Detection Models 2

CNNs in Crop Care: A Comparative Analysis of Tomato Disease ...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Ali, Ali M Nashwan, Shadi Al-Qerem, Ahmad Aldweesh, Amjad Alauthman, Mohammad Elgamal, Zenab Almomani, Ammar AlAhliyya Amman University Faculty Of Engineering Communications And Computer Engineering Department Amman19328 Jordan Faculty Of Information Technology Cybersecurity Department Amman11831 Jordan Zarqa University Faculty Of Information Technology Computer Science Department Zarqa13110 Jordan College Of Computing And It Shaqra University Shaqra Saudi Arabia University Of Petra Faculty Of Information Technology Department Of Information Security Amman Jordan School Of Computing Skyline University College University City Of Sharjah Sharjah United Arab Emirates School Of Computing Skyline University College Sharjah United Arab Emirates AlHuson University College AlBalqa Applied University I.T. Department Irbid Jordan

ISBN: (纸本)9798350394962

Timely and accurate detection of plant diseases is critical for sustainable crop production. This paper presents a comparative analysis of convolutional neural network (CNN) models for automated tomato leaf disease detection, including VGG16, VGG19, AlexNet and Google Net. A dataset of tomato leaf images with multiple disease types was utilized to train and test the performance of each CNN architecture. Our results demonstrate that the VGG16 model achieved the highest accuracy of 99.38% in classifying tomato leaf diseases. Compared to AlexNet and VGG19 models which obtained 98.67% and 99.29% accuracy respectively, the superior performance of VGG16 highlights its suitability for deploying robust tomato disease detection systems to enable prompt disease control interventions. Our work provides valuable insights on harnessing deep learning for agricultural disease surveillance, allowing farmers and agronomists to identify crop infections early and implement timely management practices to reduce yield losses. © 2024 IEEE.

关键词： Convolutional neural networks

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