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Web Attack Detection Using the Input Validation Method:DPDA Theory

computers, Materials & Continua 2021年第9期68卷 3167-3184页

作者： Osamah Ibrahim Khalaf Munsif Sokiyna Youseef Alotaibi Abdulmajeed Alsufyani Saleh Alghamdi Al-Nahrain University Al-Nahrain Nano Renewable Energy Research CenterBaghdadIraq Department of Management Information Systems Faculty of Science&Information TechnologyCyprus International UniversityNicosiaCyprus Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif UniversityTaif21944Saudi Arabia

A major issue while building web applications is proper input validation and *** can quickly exploit errors and vulnerabilities that lead to malicious behavior in web application validation *** are rapidly improving their capabilities and technologies and now focus on exploiting vulnerabilities in web applications and compromising ***-site scripting(XSS)and SQL injection attack(SQLIA)are attacks in which a hacker sends malicious inputs(cheat codes)to confuse a web application,to access or disable the application’s back-end without user *** this paper,we explore the problem of detecting and removing bugs from both client-side and server-side code.A new idea that allows assault detection and prevention using the input validation mechanism is *** addition,the project supports web security tests by providing easy-to-use and accurate models of vulnerability prediction and methods for *** these attributes imply a program statement that is vulnerable in an SQLIA,this can be evaluated and checked for a set of static code ***,we provide a script whitelisting interception layer built into the browser’s JavaScript engine,where the SQLIA is eventually detected and the XSS attack resolved using the method of input validation and script whitelisting under pushdown *** framework was tested under a scenario of an SQL attack and *** is demonstrated to offer an extensive improvement over the current *** framework’s main ability lies in the decrease of bogus *** has been demonstrated utilizing new methodologies,nevertheless giving unique access to sites dependent on the peculiarity score related to web *** proposed input validation framework is shown to identify all anomalies and delivers better execution in contrast with the current program.

关键词： Static dynamic detection prevention input validation deterministic push down automata

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Classification of Alzheimer’s Disease using Random Oversampling and Albumentations on Convolutional Neural Network

Classification of Alzheimer’s Disease using Random Oversamp...

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International Conference on Informatics and Computing (ICIC)

作者： Calvin Kamardi I Kadek Perry Bagus Laksana Maria Susan Anggreainy Nora Fitriawati Michiko Amaya Yonatan Tesalonika Abigail Eikwine Mangkang Vianny Pengesa Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia

Alzheimer’s Disease (AD) is a brain disorder that causes dementia and affects the memory, cognitive, and behavioral function. Early detection for AD can help to reduce the symptoms and slow down AD progression. Deep learning, particularly Convolutional Neural Network (CNN), a component of artificial intelligence, has demonstrated efficacy in resolving image-related issues and has gained widespread use in the analysis of medical images. CNN works well on large datasets, however, in imbalanced datasets, it can perform poorly by misclassifying minority classes. In this study, the random over-sampling technique is used to address the imbalance in the dataset, while Albumentations is used for image augmentation to prevent overfitting. The results demonstrate that the combination of these techniques leads to improved classification performance compared to traditional approaches. The proposed method has the potential to improve the accuracy of AD diagnosis and contribute to the development of effective treatments for this debilitating condition.

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ECG signals-based security and steganography approaches in WBANs: A comprehensive survey and taxonomy

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Sustainable Computing: Informatics and Systems 2024年 41卷

作者： Masdari, Mohammad Band, Shahab S. Qasem, Sultan Noman Sayed, Biju Theruvil Pai, Hao-Ting Department of Computer Engineering Urmia Branch Islamic Azad University Urmia Iran Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Riyadh11432 Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz6803 Yemen Department of Computer Science Dhofar University PO Box 2509 SalalahPCode 211 Oman Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan

Wireless Body Area Networks (WBANs) are integral components of e-healthcare systems, responsible for monitoring patients' physiological states through intelligent implantable or wearable sensor nodes. These nodes collect medical data, which is then transmitted to remote healthcare facilities for thorough evaluation. Securing medical data within WBANs is paramount due to its central role in preserving patient privacy and confidentiality. Notably, Electrocardiogram (ECG) signals have recently gained prominence as pivotal elements within diverse security frameworks. Incorporating ECG signals strategically enhances the security and reliability of WBANs and broader e-healthcare systems, instilling greater trustworthiness. This survey article provides an in-depth exploration of contemporary ECG-based security schemes, adding to the scholarly discourse. The imperative to categorize these security paradigms revolves around their use of ECG signals. This categorization identifies three key domains: the first involves schemes that utilize ECG signals for cryptographic operations, encompassing key generation, agreement, management, and authentication. The second category employs steganography-based techniques, using ECG signals to conceal patients' sensitive medical data. The third category focuses on enhancing ECG signal security during data transmission. Each category is meticulously elaborated, detailing architectural foundations, notable contributions, and intrinsic security services. Furthermore, each section presents a comprehensive overview of the attributes characterizing ECG-based security frameworks. This includes insights into employed datasets, simulation environments, evaluation metrics, and inherent advantages and limitations. Expanding on this, a thorough analysis of distinctive attributes underpinning these security frameworks concludes by shedding light on potential directions for future research. © 2023 Elsevier Inc.

关键词： Authentication

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Drone Application Model for Image Acquisition of Plantation Areas and Oil Palm Trees Counting 7

Drone Application Model for Image Acquisition of Plantation ...

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7th International Conference on information Management and technology, ICIMTech 2022

作者： Sastrohartono, Hermantoro Suryotomo, Andiko Putro Saifullah, Shoffan Suparyanto, Teddy Perbangsa, Anzaludin Samsinga Pardamean, Bens Instiper Yogyakarta Faculty of Agricultural Technology Department of Agricultural Engineering Yogyakarta55281 Indonesia Universitas Pembangunan Nasional Veteran Yogyakarta Department of Informatics Yogyakarta55283 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

The area of oil palm plantations in Indonesia increased by 7% from 14 million ha in 2017 to 15 million ha in 2021. The vast land requires the support of effective and efficient management techniques to maintain sustainable productivity. The high-performance computing technologies, Internet of Things (IoT), Big Data, Artificial Intelligence, spatial modeling, and drones are the answers to these needs. This study aims to design an application that can obtain plantation image data and analyze the calculation of the number of oil palm trees. The image of oil palm plantations is obtained from processing photo data from drones through a mosaic and composite process. This study also employed Scrum and UML as a system model development method. The image of the oil palm plantation area is used to build a tree counting model using the Viola-Jones algorithm. The oil palm tree count information generated by this system can then be used by management for fertilizing, harvesting, and monitoring the condition of oil palm trees. © 2022 IEEE.

关键词： Drones

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Hausa Visual Genome: A Dataset for Multi-Modal English to Hausa Machine Translation 13

Hausa Visual Genome: A Dataset for Multi-Modal English to Ha...

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13th International Conference on Language Resources and Evaluation Conference, LREC 2022

作者： Abdulmumin, Idris Dash, Satya Ranjan Dawud, Musa Abdullahi Parida, Shantipriya Muhammad, Shamsuddeen Hassan Sa'id Ahmad, Ibrahim Panda, Subhadarshi Bojar, Ondřej Galadanci, Bashir Shehu Bello, Bello Shehu Department of Computer Science Ahmadu Bello University Zaria Nigeria School of Computer Applications KIIT University Bhubaneswar India Silo AI Helsinki Finland LIAAD INESC TEC Faculty of Sciences University of Porto Portugal Faculty of Computer Science and Information Technology Bayero University Kano Nigeria Graduate Center City University of New York United States Charles University Faculty of Mathematics and Physics ÚFAL Prague Czech Republic

ISBN: (纸本)9791095546726

Multi-modal Machine Translation (MMT) enables the use of visual information to enhance the quality of translations. The visual information can serve as a valuable piece of context information to decrease the ambiguity of input sentences. Despite the increasing popularity of such a technique, good and sizeable datasets are scarce, limiting the full extent of their potential. Hausa, a Chadic language, is a member of the Afro-Asiatic language family. It is estimated that about 100 to 150 million people speak the language, with more than 80 million indigenous speakers. This is more than any of the other Chadic languages. Despite a large number of speakers, the Hausa language is considered low-resource in natural language processing (NLP). This is due to the absence of sufficient resources to implement most NLP tasks. While some datasets exist, they are either scarce, machine-generated, or in the religious domain. Therefore, there is a need to create training and evaluation data for implementing machine learning tasks and bridging the research gap in the language. This work presents the Hausa Visual Genome (HaVG), a dataset that contains the description of an image or a section within the image in Hausa and its equivalent in English. To prepare the dataset, we started by translating the English description of the images in the Hindi Visual Genome (HVG) into Hausa automatically. Afterward, the synthetic Hausa data was carefully post-edited considering the respective images. The dataset comprises 32,923 images and their descriptions that are divided into training, development, test, and challenge test set. The Hausa Visual Genome is the first dataset of its kind and can be used for Hausa-English machine translation, multi-modal research, and image description, among various other natural language processing and generation tasks. © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.

关键词： Computational linguistics

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WIT-Greedy: Hardware System Design of Weighted ITerative Greedy Decoder for Surface Code

WIT-Greedy: Hardware System Design of Weighted ITerative Gre...

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Asia and South Pacific Design Automation Conference

作者： Wang Liao Yasunari Suzuki Teruo Tanimoto Yosuke Ueno Yuuki Tokunaga Photon Science Center the University of Tokyo Japan NTT Computer and Data Science Laboratories Japan Faculty of Information Science and Electrical Engineering Kyushu University Japan Graduate School of Information Science and Technology the University of Tokyo Japan

Large error rates of quantum bits (qubits) are one of the main difficulties in the development of quantum computing. Performing quantum error correction (QEC) with surface codes is considered the most promising approach to reduce the error rates of qubits effectively. To perform error correction, we need an error-decoding unit, which estimates errors in the noisy physical qubits repetitively, to create a robust logical qubit. While complicated graph-matching problems must be solved within a strict time restriction for the error decoding, several hardware implementations that satisfy the restriction at a large code distance have been proposed. However, the existing decoder designs are still challenging in reducing the logical error rate. This is because they assume that the error rates of physical qubits are uniform while they have large variations in practice. According to our numerical simulation based on the quantum chip with the largest qubit number, neglecting the non-uniform error properties of a real quantum chip in the decoding process induces significant degradation of the logical error rate and spoils the benefit of QEC. To take the non-uniformity into account, decoders need to solve matching problems on a weighted graph, but they are difficult to solve using the existing designs without exceeding the time limit of decoding. Therefore, a decoder that can treat both the non-uniform physical error rates and the large surface code is strongly demanded. In this paper, we propose a hardware design of decoding units for the surface code that can treat the non-identical error properties with small latency at a large code distance. The key idea of our design is 1) constructing a look-up table for calculating the shortest paths between nodes in a weighted graph and 2) enabling parallel processing during decoding. The implementation results in field programmable gate array (FPGA) indicate that our design scales up to code distance 11 within a microsecond-level delay,

关键词： Codes Error analysis Qubit Hardware Decoding Delays Table lookup

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Apache Spark Powered: Enhancing Network Intrusion Detection System Using Random Forest

Apache Spark Powered: Enhancing Network Intrusion Detection ...

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Hesham Mamdouh Mohamed Tarek Abdelrahman Radwan Adel Saeed Ahmed Abdeen Monier Ashraf Khaled M. Fouad Ibrahim Abdelbaky School of Engineering and Applied Science (EAS) Nile University Giza Egypt CS program School of Information Technology and Computer Science (ITCS) Nile University Giza Egypt Faculty of Computer Science and Engineering New Mansoura University Artificial Intelligence Department Faculty of Computers and Artificial Intelligence Benha University Banha Egypt

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

The increasing sophistication of cyber attacks necessitates effective intrusion detection systems. We propose a novel intrusion detection method integrating deep learning with big data management using Apache Spark. Leveraging the comprehensive CSE-CIC-IDS2018 dataset, we apply extensive data preprocessing, including handling missing and unreliable values, duplicates, and redundant columns. In addition, implementation of a Random Forest based feature importance approach is derived to prioritize the most impactful Features. Furthermore, stratified k-fold cross-validation is used for a model selection process on a class-imbalanced dataset. Our weighted Random Forest classifier achieves a remarkable weighted average F1-score of 0.999 and a test inference time of 0.673 seconds using only the top 34 features, outperforming previous studies without sampling techniques. The proposed architecture offers a scalable and accurate solution for intrusion detection in cloud architectures, demonstrating the effectiveness of combining deep learning and big data technologies for cybersecurity.

关键词： Deep learning Cloud computing Data preprocessing Network intrusion detection Cluster computing Forestry Big Data Random forests Cyberattack Testing

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Society with Trust: A Scientometrics Review of Zero-Knowledge Proof Advanced Applications in Preserving Digital Privacy for Society 5.0 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Dominic, Nicholas Pratama, Naufal Rizqi Cornelius, Kenny Senewe, Shavellin Herliman Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Department of Computer Science Universitas Multimedia Nusantara Tangerang15111 Indonesia Psychology Department Faculty of Humanities Bina Nusantara University Jakarta11480 Indonesia BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Society 5.0 focuses on human productivity in the midst of advanced technological services. While the concept has human trust at its core, technology development is now leading to zero-trust architecture. In this scientometrics review, 107 selected papers were analyzed to obtain the nature of the Zero-knowledge Proof (ZKP) theory and its advanced applications to favor digital privacy in greater society. With the common literature review strategy, it was found that the most citations in this field appeared in 2018, with IEEE, Springer, and Elsevier as the top three publishers. By affiliations, China and United States are both leading countries where the researcher comes from. And as the positive trend emerged, the ZKP mechanism will keep being leveraged as one of the cryptographic toolsets in the cloud services, Internet of Things (IoT), smart contracts, healthcare, electronic systems, and many other industrial needs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Smart contract

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An Analysis of Training Artificial Intelligence Techniques into Eco Sounder Machine to Identify Fish 1

An Analysis of Training Artificial Intelligence Techniques i...

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2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Hadlee, R. Richards Gudadhe, Amit A. Kumar, J. Pretheep Puri, Chetan G. Verma, Prateek School of Engineering and Technology Shiv Nadar University Chennai India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Wardha India School of Computer Science and Information Technology St. John. the Baptist University Blantyre Malawi

ISBN: (纸本)9798350338423

The acoustic backscatter coefficient values obtained from Echosounders provides important information about the presence of fishes in the water. There are several developments in underwater acoustic technology such as single beam echo sounder, multi frequency echo sounder, side scan radar. But there are several challenges associated with this technology such as interpretation of echograms generated in these devices are time consuming, there is requirement of technical experts to understand these echograms and detection of fish species is still a challenge etc. The recent advancement in this field is the integration of echo signal with the Artificial Intelligence algorithms. Machine Learning, Deep Learning, Fuzzy Logic are some of the recent advanced algorithms which are used for automatic classification of fishes that aids the fishermen to identify the locations of the fishes. Hence, this review article focusses on the role of these advance algorithms to identify fishes in the sea that can help fishermen in saving their time by precisely locating the fishes. A trained AI program can locate areas in a scene and recognize fish feature patterns. Fish recognition and species categorization on 3D photos have both been successfully accomplished using the Echo sounder object detection framework. © 2023 IEEE.

关键词： Fish

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Slice-Fusion: Reducing False Positives in Liver Tumor Detection for Mask R-CNN

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2023年第5期20卷 3267-3277页

作者： Tu, Deng-Yao Lin, Peng-Chan Chou, Hsin-Hung Shen, Meng-Ru Hsieh, Sun-Yuan National Cheng Kung University Master Degree Program on Artificial Intelligence Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Informatics Department of Oncology Department of Genomic Medicine National Cheng Kung University Hospital College of Medicine Department of Computer Science and Information Engineering Tainan City70101 Taiwan National Chi Nan University Department of Computer Science and Information Engineering Nantou County54561 Taiwan National Cheng Kung University Graduate Institute of Clinical Medicine Department of Obstetrics and Gynecology Department of Pharmacology National Cheng Kung University Hospital College of Medicine Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Information Institute of Manufacturing Information and Systems Center for Innovative FinTech Business Models International Center for the Scientific Development of Shrimp Aquaculture Department of Computer Science and Information Engineering Tainan City70101 Taiwan

Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models. © 2004-2012 IEEE.

关键词： Tumors

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