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International Conference on Connected and Autonomous Driving

作者： Luodai Yang Qian Jia Ruijun Wang Jie Cao Weisong Shi College of Engineering and Technology Eastern Michigan University College of Arts and Science Eastern Michigan University Dept.of Computer Science Wayne State University School of Information Security and Applied Computing Eastern Michigan University

ISBN: (数字)9781728160597

ISBN: (纸本)9781728160603

Today's autonomous vehicles will deploy multiple sensors to achieve safe and reliable navigation and precise perception of the environment. Although multiple sensors can be advantageous in terms of providing a robust and complete description of the surrounding area, the synchronization of multi-sensors in real-time processing is extremely important. When data is synchronized, primary functional systems such as localization, perception, planning, and control, will all benefit. In this paper, we proposed a synchronized data illustration and collection method to assist the data processing applications for autonomous driving. Our proposed solution among different sensors can be directly deployed on autonomous vehicles for data integration and environment analysis to support the driving model construction. The experimental results validate that our proposed method can present a 360° synchronized view while providing the capability of real-time scanning with up to 80% reduced latency.

关键词： autonomous vehicles multiple sensor synchronization camera LiDAR transformation matrix Autonomous Vehicles transformation matrix Lidar environmental perception Cameras Sensors multi sensor

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MACHINE LEARNING IN NETWORK security USING KNIME ANALYTICS

arXiv

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arXiv 2019年

作者： Abualkibash, Munther School of Information Security and Applied Computing College of Technology Eastern Michigan University YpsilantiMI United States

Machine learning has more and more effect on our every day’s life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Detection of Phishing Websites using Machine Learning

Detection of Phishing Websites using Machine Learning

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IEEE Cloud Summit

作者： Abdul Razaque Mohamed Ben Haj Frej Dauren Sabyrov Aidana Shaikhyn Fathi Amsaad Ahmed Oun International IT University Almaty Kazakhstan University of Bridgeport Bridgeport CT USA Computer Engineering and Telecommunication International IT University Almaty Kazakhstan School of Information Security and Applied Computing (SISAC) Eastern Michigan University Ypsilanti MI USA University of Toledo Toledo OH US

ISBN: (数字)9781728182667

ISBN: (纸本)9781728182674

Phishing sends malicious links or attachments through emails that can perform various functions, including capturing the victim's login credentials or account information. These emails harm the victims, cause money loss, and identity theft. In this paper, we contribute to solving the phishing problem by developing an extension for the Google Chrome web browser. In the development of this feature, we used JavaScript PL. To be able to identify and prevent the fishing attack, a combination of Blacklisting and semantic analysis methods was used. Furthermore, a database for phishing sites is generated, and the text, links, images, and other data on-site are analyzed for pattern recognition. Finally, our proposed solution was tested and compared to existing approaches. The results validate that our proposed method is capable of handling the phishing issue substantially.

关键词： Uniform resource locators Social networking (online) Phishing Semantics Browsers Electronic mail Videos

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Pair Data Division Algorithm For Handling Data Classification

Pair Data Division Algorithm For Handling Data Classificatio...

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

作者： Abdul Razaque A. Kanapina M. Sailaubek D. Tsoy Z. Turganov Muder Almiani Fathi Amsaad Mohammad Adnan Almahamed IITU Kazakhstan Al-Hussein Bin Talal University Jordan School of Information Security and Applied Computing Eastern Michigan University USA

Data heterogeneity creates the complexity for understanding the different domains. Most of domains require sophisticated algorithm to understand the complexity. To handle the data heterogeneity problem, several methods have been proposed (e.g., Support Vector Machine (SVM), Decision Tree, Random Forest, Liner regression etc.). However, these existing methods suffer due to accuracy, outlying. In this paper, we introduce Pair Data Division (PDD) with support of SMV for handling the data heterogeneity. PDD divides the data into the pairs then further pairs are processed using SVM. The PDD algorithm is implanted by using Python and PyCharm is used as Interface Development Environment (IDE). Furthermore, training datasets will be given to PDD for testing purpose. Based on the testing results, we confirm that our proposed algorithm provides better accuracy as compared to known algorithms: Random Forest and Linear regression when handling the data heterogeneity.

关键词： Support vector machines Classification algorithms Decision trees Data processing Prediction algorithms Social networking (online) security

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Analysis of variance of graph-clique mining for scalable proof of work 14th

Analysis of variance of graph-clique mining for scalable pro...

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14th International Conference on information security and Cryptology, Inscrypt 2018

作者： Anada, Hiroaki Matsushima, Tomohiro Su, Chunhua Meng, Weizhi Kawamoto, Junpei Bag, Samiran Sakurai, Kouichi Department of Information Security University of Nagasaki Nagasaki Japan Department of Informatics Graduate School and Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan Division of Computer Science University of Aizu Aizuwakamatsu Japan Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark School of Computing Science Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9783030142339

Recently, Bitcoin is becoming one of the most popular decentralized cryptographic currency technologies, and Bitcoin mining is a process of adding transaction records to Bitcoin’s public ledger of past transactions or blockchain. To obtain a bitcoin, the mining process involves compiling recent transactions into blocks and trying to solve a computationally difficult puzzle, e.g., proof of work puzzle. A proof of work allows miners the ability to quantify how much work a given proof contains. Basically, the required time for mining is decided in advance, but problems will occur if the value is large for dispersion. In this paper, we first accept that the required time between consecutive blocks follows the exponential distribution. That is, the variance is stable as long as the expected time is fixed. Then, we focus on the graph clique mining technique proposed by the literature, like Tromp (BITCOIN 2015) and Bag-Ruj-Sakurai (Inscrypt 2015), which is based on a computational difficulty problem of searching cliques of undirected graphs, where a clique is a subset of vertices. In particular, when the clique size is two, graph clique mining can be used to gain Bitcoins. The previous work also claimed that if the clique size is parameterized and increased, even if the expected time is fixed, the variance would not be stable. However, no qualitative or quantitative results were given to support their claim. Motivated by this issue, in this work, we propose a simple search algorithm for graph cliques mining, and perform a small scale evaluation on Bitcoin and Graph cliques’s solo mining to investigate the variance issue. © 2019, Springer Nature Switzerland AG.

关键词： Blockchain

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An Ultra-Scalable Blockchain Platform for Universal Asset Tokenization: Design and Implementation

TechRxiv

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TechRxiv 2022年

作者： Buldas, Ahto Draheim, Dirk Gault, Mike Laanoja, Risto Nagumo, Takehiko Saarepera, Märt Shah, Syed Attique Simm, Joosep Steiner, Jamie Tammet, Tanel Truu, Ahto Centre for Digital Forensics and Cyber Security Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia Information Systems Group Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia Guardtime Avenue d’Ouchy 4 Lausanne1006 Switzerland Guardtime A. H. Tammsaare tee 60 Tallinn11316 Estonia Mitsubishi UFJ Research and Consulting 5-11-2 Toranomon Minato City Tokyo105-8501 Japan Graduate School of Management Kyoto University Yoshida Honmachi Sakyo-ku Kyoto-shi Kyoto606-8501 Japan Martest Research Pärnu mnt 160a Tallinn11317 Estonia School of Computing and Digital Technology Birmingham City University Millennium Point BirminghamB4 7XG United Kingdom Applied Artificial Intelligence Group Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia

Since its introduction with Bitcoin in 2009, blockchain technology has received tremendous attention by academia, industry, politics and media alike, in particular, through extended blockchain-based visions such as smart contracts, decentralized finance, and, most recently, Web3. The critical prerequisite for any such blockchain-based vision to be turned into reality is uncapped scalability. Furthermore, and equally important, blockchain technology needs to transcend the stage of specialized tokens into an adaptive, heterogeneous tokenization platform. In this paper, we explain the Alphabill family of technologies that addresses both unlimited scalability and unrestricted adaptivity. We deliver a sharded blockchain technology with unlimited scalability and performance, called KSI Cash, which is based on a new form of electronic money scheme, the bill scheme. We present performance tests of KSI Cash that we have conducted with the European Central Bank and a group of eight national central banks from the Eurosystem in order to assess the technological feasibility of a digital euro, showing the system operating with 100 million wallets and 15 thousand transactions per second (under simulation of realistic usage), having an estimated carbon footprint of 0.0001g CO2 per transaction (Bitcoin = 100 kg and more);furthermore, showing the system operating with up to 2 million payment orders per second, an equivalent of more than 300,000 transactions per second (in a laboratory setting with the central components of KSI Cash), scaling linearly in terms of the number of deployed shards. We explain, in detail, the key concepts that unlock this performance (i.e., the concepts of the bill money scheme). The results provide evidence that the scalability of our technology is unlimited in both permissioned and permissionless scenarios, resulting into the Alphabill Money technology. Next, we contribute the architecture of a universal tokenization platform that allows for universal as

关键词： Blockchain

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A Survey of Traffic Sign Recognition Systems Based on Convolutional Neural Networks

A Survey of Traffic Sign Recognition Systems Based on Convol...

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IEEE International Conference on Electro information Technology

作者： Syed Hussain Munther Abualkibash Samir Tout School of Information Security and Applied Computing Eastern Michigan University

In this paper, we briefly discuss the applications of Convolutional Neural Networks (CNNs) model to traffic sign recognition (TSR) systems. Traditionally, the TSRs have used different techniques to detect and classify visual data. The CNNs have been used separately to extract features and train the classifier as well as simultaneously for detection and classification tasks. One model that has been successful is the Fast Branch CNN model, which imitates biological mechanisms to become more efficient. While it is not the most accurate of the ones presented in this paper, the efficiency it exhibits under time-sensitive conditions is worth exploring because of the potential applications of such technology. The Fast Branch CNN model challenged the assumptions of past models, and this technology can only advance further if new models attempt to do the same.

关键词： TSR (Traffic Sign Recognition) CNN (Convolutional Neural Network) Detection Classification

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Emerging Research in the security of Modern and Autonomous Vehicles

Emerging Research in the Security of Modern and Autonomous V...

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IEEE International Conference on Electro information Technology

作者： Samir Tout Munther Abualkibash Pooja Patil School of Information Security & Applied Computing Department of Computer Science Eastern Michigan University

The last decade has witnessed considerable advancements in automating modern "connected" and Autonomous Vehicles (AVs), which are becoming an integral part of the modern transportation systems. Such advancements have been attributed to major strides in Machine Learning, Artificial Intelligence and processing technology. More importantly, the complexity of such systems and their lack of built-in security have brought to the spotlight the need for extensive research to understand the associated risks and propose mitigations for them. In this paper we briefly discuss the security aspects of modern vehicles and provide a concise literature review of the work that has been done in this domain. We focus on specific areas that are of interest to us and conclude by providing a plan for future related endeavors.

关键词： Vehicle Autonomous security Networks Hacking

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Two Factor Authentication using Twofish Encryption and Visual Cryptography Algorithms for Secure Data Communication

Two Factor Authentication using Twofish Encryption and Visua...

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International Conference on Internet of Things: Systems, Management and security

作者： Duisen Gulsezim Seiitkaliyeva Zhansaya Abdul Razaque Yestayeva Ramina Fathi Amsaad Muder Almiani Raouf Ganda Ahmed Oun Computer Eng. And Security International Inf. Tech. Univ Almaty Kazakhstan Dept. of Computer Sci NYIT Nanjing China School of Information Security and Applied Computing Eastern Michigan University USA Computer Information Sys Al-Hussein Bin Talal University Jordan Yale School of Management Yale University USA EECS Department University of Toledo OH

Dependence of the individuals on the Internet for performing the several actions require secure data communication. Thus, the reliable data communication improves the confidentiality. As, enhanced security leads to reliable and faster communication. To improve the reliability and confidentiality, there is dire need of fully secured authentication method. There are several methods of password protections were introduced to protect the confidentiality and reliability. Most of the existing methods are based on alphanumeric approaches, but few methods provide the dual authentication process. In this paper, we introduce improved graphical password authentication using Twofish Encryption and Visual Cryptography (TEVC) method. Our proposed TEVC is unpredictably organized as predicting the correct graphical password and arranging its particles in the proper order is harder as compared to traditional alphanumeric password system. TEVC is tested by using JAVA platform. Based on the testing results, we confirm that proposed TEVC provides secure authentication. TEVC encryption algorithm detected as more prudent and possessing lower time complexity as compared to other known existing algorithms message code confirmation and fingerprint scan with password.

关键词： Conferences Internet security

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Earthquake Damage Detection Using Before and After Earthquake Satellite Images

Earthquake Damage Detection Using Before and After Earthquak...

引用

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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