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2022 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2022

作者： Sudheer, Devulapalli Jothiaruna, N. Potti, Anupama Gangappa, M. Ramasubbareddy, Somula Computer Science and Engineering Vnr Vjiet Hyderabad India Vellore. Institute of Technology Schoolf Computer Science and Enginerring Tamilnadu Vellore India Sree Dattha Institute of Engineering and Science Computer Science and Engineering Hyderabad India Vnr Vjiet Dept. Information Technology Hyderabad India

ISBN: (纸本)9781665454995

Brain computer interface (BCI) is used to identify electrical activity in human brain using the electroencephalog-raphy (EEG). EEG records the electrical activity by placing the electrodes on the scalp. By using the recorded information it will able to classify the different types of abnormalities happening in brain. For extracting the information from signal without losing any information, some feature extraction methods have been used using deep learning concepts. The methods are Time Frequency distribution, Fast Fourier Transform, Eigen Vector, Wavelet Transform, Auto Regressive, Independent Component Analysis, Principal Component Analysis, Empirical Method Decomposition, Hilbert Huang Transform and Local Discriminant bases. Comparison have been done between the methods to show how effectively methods without losing an information. The classification accuracies of the feature methods are compared with each other. The study concluded the hybrid methods such as domain specific and automated features togeather shown better performance. © 2022 IEEE.

关键词： Feature extraction

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Revolutionizing Requirements Elicitation: Deep Learning-Based Classification of Functional and Non-Functional Requirements

Revolutionizing Requirements Elicitation: Deep Learning-Base...

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Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Grace Hanna Nathan Boyar Nathan Garay Mina Maleki Dept. of Electrical & Computer Engineering & Computer Science University of Detroit Mercy Detroit MI United States

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

The requirements elicitation phase in the software development life cycle (SDLC) is both critical and challenging, especially in the context of big data and rapid technological advancement. Traditional approaches like workshops and proto-typing, while useful, often struggle to keep pace with the massive data volumes and rapidly changing user demands characteristic of modern technology. This paper introduces a data-driven approach that utilizes deep learning (DL) and natural language processing (NLP) to enhance the requirements elicitation process by extracting requirements and classifying them into functional and non-functional categories. Our research involves a deep neural network (DNN) trained on a large dataset of transcriptions from client/user stories. This DNN can identify whether a specific text represents a functional requirement, a non-functional requirement, or neither. Our approach shows a marked improvement over previous methods, with a 33% increase in accuracy and an 18% increase in the F1 score. These results indicate the capability for deep learning techniques to play a vital role in elicitation.

关键词： Deep learning Accuracy Conferences Artificial neural networks Big Data Natural language processing Real-time systems Pattern recognition Software development management

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Exploration and Evaluation of Machine Learning Methods for Detecting Oil Spills in Open Seas

Exploration and Evaluation of Machine Learning Methods for D...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Sibai, Fadi N. Sibai, Ahmad El-Moursy, Ali GUST Engineering & Applied Innovation Research Center Gulf Univ. for Science and Tech. West Mishref Hawalli Kuwait Univ. of South Florida Computer Science & Engg. Dept. TampaFL United States Univ. of Sharjah Comp. Engg. Dept. Sharjah United Arab Emirates

ISBN: (纸本)9798350369106

Detecting oil spills in open seas is important for the early containment of the pollution diffusion and avoidance of further ecological catastrophes. Herein, we explore and assess the application of several machine learning (ML) methods and image processing techniques for the detection of oil spills in images of open seas captured at the elevation of planes, helicopters, or drones. The image dataset used contains 24 clean sea images and 24 dirty sea images with oil slicks. The machine learning methods experimented with include neural networks, SVM, random forest, decision tree, naïve Bayes, AdaBoost, and logistic regression. We also evaluated these ML methods with and without the application of Sobel Feldman edge detection and Vintage filtering image processing techniques. Results indicate the superiority of the in and neural networks methods over the other explored ML methods, achieving perfect and near-perfect classification, respectively. © 2024 IEEE.

关键词： Oil spills

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Efficient Host Intrusion Detection using Hyperdimensional Computing

Efficient Host Intrusion Detection using Hyperdimensional Co...

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IEEE International Conference on Big Data

作者： Yujin Nam Rachel King Quinn Burke Minxuan Zhou Patrick McDaniel Tajana Rosing Dept. of Computer Science and Engineering UC San Diego USA Dept. of Computer Sciences University of Wisconsin-Madison Madison USA Computer Science Department Illinois Institute of Technology Chicago USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Modern host-based intrusion detection systems (HIDS) rely on querying provenance graphs—graph representations of activity history on a system—to detect and respond to security threats present on a system. However, as the complexity and number of applications running on a system increase, the size of provenance graphs also increase, and thus the latency to query them. State-of-the-art designs deliver query latencies that are impractical for modern threat detection. In this paper, we introduce a hyper-dimensional computing (HDC) approach to querying provenance graphs for HIDS. By encoding provenance graphs and attack patterns/signatures into hyper-dimensional vectors, we can implement a query engine using simple vector operations. Our approach is hardware accelerator compatible, providing further speedups under resource-constrained environments. Our evaluation on a real-world dataset shows that our approach achieves > 90% detection accuracy and up to 4, 242× speedups over the state-of-the-art. This shows that HDC-based approaches can effectively deal with scaling issues in modern HIDS.

关键词： Accuracy Costs Intrusion detection Vectors Encoding Threat assessment Security History Hardware acceleration Engines

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Onboard Near-Real-Time Detection of Harmful Algal Blooms Using a Convolutional Neural Network and Multispectral Imagery from a Satellite in Low Earth Orbit

Onboard Near-Real-Time Detection of Harmful Algal Blooms Usi...

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OCEANS

作者： Jonathan D. Parsons Mae L. Seto Computer Science Dept. Dalhousie University Halifax Canada Mechanical Engineering Dept. Dalhousie University Halifax Canada

ISBN: (数字)9798331540081

ISBN: (纸本)9798331540098

Freshwater harmful algal blooms (HABs) pose significant ecological and public health risks worldwide. Detecting HABs soon after they form is critical to managing the damage they cause. While in-situ measurements are more accurate at detecting and measuring their toxicity levels, satellite imagery is more adept.at capturing the spatial and temporal dynamics of these blooms over large geographic regions. Satellites can also more persistently monitor for HABs. In the past, empirical methods and machine learning methods have used multispectral satellite imagery to estimate HAB biomass. To build upon the current body of research, this paper investigates an approach to expedite HAB detection by utilizing a convolutional neural network (CNN) deployed onboard a CubeSat in low Earth orbit to detect HABs in near-real-time. The CNN is trained with multispectral imagery from the Sentinel-2 satellite constellation aggregated with in-situ cyanobacteria cell counts from the Seabass CAML dataset. The results successfully demonstrated the capability of a CNN to detect cyanobacterial blooms using multispectral imagery. After classifying HAB predictions into 5 severity classes, the best performing model achieved a RMSE of 1.33 between HAB severity levels. Training the CNN on 30m GSD imagery with RGB and red edge (B05) bands achieved a RMSE of 1.83 between HAB severity levels, which was inadequate for detecting HABs in small inland water bodies. Improved performance was observed with 10m ground sample distance (GSD) band combinations. The best performing networks utilized all of Sentinel-2's 10m and 20m spectral bands.

关键词： Satellites Image edge detection Low earth orbit satellites Cyanobacteria Predictive models Extraterrestrial measurements Data models Satellite images Convolutional neural networks Spatial resolution

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Minimize Web Applications vulnerabilities through the early Detection of CRLF Injection

arXiv

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arXiv 2023年

作者： Hasan, M.D. Asibul Rahman, Md. Mijanur Dept. of Computer Science and Engineering Southeast University Bangladesh

Carriage return (CR) and line feed (LF), also known as CRLF injection is a type of vulnerability that allows a hacker to enter special characters into a web application, altering its operation or confusing the administrator. Log poisoning and HTTP response splitting are two prominent harmful uses of this technique. Additionally, CRLF injection can be used by an attacker to exploit other vulnerabilities, such as cross-site scripting (XSS). Email injection, also known as email header injection, is another way that can be used to modify the behavior of emails. The Open Web Application Security Project (OWASP) is an organization that studies vulnerabilities and ranks them based on their level of risk. According to OWASP, CRLF vulnerabilities are among the top 10 vulnerabilities and are a type of injection attack. However, CRLF vulnerabilities can also lead to the discovery of other high-risk vulnerabilities, and it fosters a better approach to mitigate CRLF vulnerabilities in the early stage and help secure applications against known vulnerabilities. Although there has been a significant amount of research on other types of injection attacks, such as Structure Query Language Injection (SQL Injection). There has been less research on CRLF vulnerabilities and how to detect them with automated testing. There is room for further research to be done on this subject matter in order to develop creative solutions to problems. It will also help to reduce false positive alerts by checking the header response of each request. Automated alerts from security systems can provide a quicker and more accurate understanding of potential vulnerabilities and can help to reduce false positive alerts. Despite the extensive research on various types of vulnerabilities in web applications, CRLF vulnerabilities have only recently been included in the research. Utilizing automated testing as a recurring task can assist companies in receiving consistent updates about their systems and enhance the

关键词： Cybersecurity

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Smart Cities: Transforming Urban Living with IoT

Smart Cities: Transforming Urban Living with IoT

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Niveditha J D R Kumar Raja UM Ashwin Kumar Post Graduate Student dept of Computer Science and Engineering Reva University Bangalore India

The Internet of Things (IoT) is being used by smart cities to harness the power of the IoT and build more efficient and sustainable urban settings as they quickly adapt to the challenges of urbanization. This study examines the idea of “smart cities” and the different fields where Internet of Things-based technologies are being used to improve urban living. The article discusses several IoT use cases and applications for smart cities, including smart energy management, smart transportation, smart healthcare, and smart public safety. Discussions also cover the advantages and difficulties of IoT-based smart city initiatives, such as the necessity for reliable infrastructure, data privacy and security concerns, and cooperative strategies. The essay also emphasizes how crucial a favorable policy and regulatory framework is to the deployment of IoT-based smart city projects. The article's conclusion discusses potential paths for further research as well as how the Internet of Things may change urban living in the years to come

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Harmonizing open banking in the European Union: an analysis of PSD2 compliance and interrelation with cybersecurity frameworks and standards

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International Cybersecurity Law Review 2024年第1期5卷 79-120页

作者： Gounari, Marianna Stergiopoulos, George Pipyros, Kosmas Gritzalis, Dimitris Dept. of Information & Communication Systems Engineering University of the Aegean Samos Greece Dept. of Computer Science Philips University Nicosia Cyprus Dept. of Informatics Athens University of Economics & Business Athens Greece

This paper focuses on the security protocols enacted in banking transactions across the European Economic Area (EEA), as stipulated by the Second or Revised Payment Service Directive (commonly referred to as ‘PSD2’ or simply ‘the Directive’). The study aims to comprehensively analyse the implementation and efficacy of these security measures within the specified jurisdiction. The Directive incorporates fundamental rights and obligations that all stakeholders are compelled to adhere to and delineates specific security measures and standards that both traditional banking institutions and third-party providers (TPP) are mandated to implement. In particular, one of the cardinal mandates for banking and financial institutions under PSD2 is the obligation to facilitate third-party access to customer data via open application programming interfaces (API). While this open banking paradigm and the consequent proliferation of data sharing unquestionably bring about various advantages, such as enhanced consumer choice and market competition, they concurrently expose the financial ecosystem to a slew of potential security vulnerabilities and privacy risks. Upon conducting a comprehensive review of the security requirements and measures stipulated under PSD2 and a comparative analysis with essential cybersecurity frameworks and standards (NIS2, Cybersecurity Act, GDPR, ISO 27001:22 and PCI DSS), we have ascertained a discernible lack of harmonisation and clarity concerning the technical security specifications for its effective implementation. This lacuna substantiates the challenges banks face in fully grasping the extensive spectrum of compliance obligations mandated by PSD2. The aim of this research is to offer a valuable contribution to both the comprehension and the pragmatic deployment of security standards in the context of banking transactions, as regulated by the PSD2. The paper serves as a valuable resource for traditional banking institut

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Blockchain-based certificate authentication system with enabling correction

arXiv

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arXiv 2023年

作者： Rahman, Md. Mijanur Tonmoy, Md Tanzinul Kabir Shihab, Saifur Rahman Farhana, Riya Dept. of Computer Science and Engineering Southeast University Bangladesh

Blockchain has proven to be an emerging technology in the digital world, changing the way everyone thinks about data security and bringing efficiency to several industries. It has already been applied to a wide range of applications, from financial services and supply chain management to voting systems and identity verification. An organization must verify its candidates before selecting them. Choosing an unqualified candidate can ruin an organization’s reputation. In this digital era, many key fraudulent schemes are rampant in many companies and one of them is certificate fraud. It is possible to validate a candidate’s qualifications using traditional methods, but there are drawbacks such as security issues and time consumption. In this paper, a blockchain-based academic certificate authentication system will be used to ensure authenticity and make the assertion of the decentralized system secure. However, the system will generate, authenticate and make corrections on academic certificates. Ultimately, some blockchain-based authentication systems already exist, they can’t correct any errors that occur during generation. The proposed system will help in many ways, such as providing a user-friendly university admission, and smooth job hiring process, etc. In conclusion, our proposed system can permanently eradicate certificate forgeries and create and promote trust in society. © 2023, CC BY.

关键词： Authentication

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High Throughput and Energy Efficient SHA-2 ASIC Design for Continuous Integrity Checking Applications

High Throughput and Energy Efficient SHA-2 ASIC Design for C...

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IEEE European Test Symposium (ETS)

作者： Asimina Koutra Vasileios Tenentes Dept. of Computer Science and Engineering University of Ioannina Greece

High throughput and energy efficient integrated cryptographic hash primitives are important for the continuous integrity checking and tampering detection in secure access management mechanisms of on-chip instrumentation, such as the IJTAG. However, previous SHA-256 cores focus only on throughput. In this paper, we synthesize with a 32 nm CMOS Technology SHA-256 cores that can be integrated in ASICs, and we present insights on their achieved throughput and energy efficiency. Moreover, we present a novel clock-gated design for reducing dynamic power dissipation of SHA-256 cores; and a novel Multi-Vt design for reducing static power dissipation of SHA-256 cores. The proposed designs can achieve upto 25.9% improvement of the energy efficiency of existing SHA-256 designs, without impacting their performed throughput. To the best of our knowledge, this is the first work that applies low power design techniques on SHA-256 cores.

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