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Detection and Prevention of Jellyfish Attacks Using kNN Algorithm and Trusted Routing Scheme in MANET

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International Journal of Network Security 2021年第1期23卷 77-87页

作者： Zardari, Zulfiqar Ali He, Jingsha Pathan, Muhammad Salman Qureshi, Sirajuddin Hussain, Muhammad Iftikhar Razaque, Fahad He, Peng Zhu, Nafei Faculty of Information Technology Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing100124 China College of Computer and Information Science China Three Gorges University YichangHubei443002 China

Mobile ad hoc networks (MANETs) are surrounded by various vulnerabilities and attacks due to open medium, dynamic topology, limited energy, and absence of central control. In MANET, each attack has different behavior and aftermaths. DoS attack is one of the serious attacks in MANET, which disturbs the normal routing process. Jellyfish (JF) attack is a type of DoS attack in MANET, whose dynamic behavior makes it quite difficult to detect such attacks. In this paper, we propose a technique to detect the jellyfish attack in MANET. The proposed technique combines the authentication and trustworthiness of nodes and the kNN algorithm for the identification of jellyfish attacks in which each and every node calculates the primary and secondary trust values to detect the attacking node by the recommendation of neighboring nodes and trust metrics. The kNN algorithm separates the jellyfish nodes from other legitimate nodes based on the differences in their behavior. The proposed technique would then pick reliable nodes by the hierarchical trust assessment property of nodes to perform packet routing. The experiment shows that the proposed technique could decrease delay and increase the throughput of the network by avoiding jellyfish nodes. © 2021

关键词： Mobile ad hoc networks

Russian natural language generation: Creation of a language modeling dataset and evaluation with modern neural architectures

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arXiv 2020年

作者： Shaheen, Z. Wohlgenannt, G. Zaity, B. Mouromtsev, D.I. Pak, V.G. Faculty of Software Engineering and Computer Systems Itmo University St. Petersburg Russia St. Petersburg Russia

Generating coherent, grammatically correct, and meaningful text is very challenging, however, it is crucial to many modern NLP systems. So far, research has mostly focused on English language, for other languages both standardized datasets, as well as experiments with stateof-the-Art models, are rare. In this work, we i) provide a novel reference dataset for Russian language modeling, ii) experiment with popular modern methods for text generation, namely variational autoencoders, and generative adversarial networks, which we trained on the new dataset. We evaluate the generated text regarding metrics such as perplexity, grammatical correctness and lexical diversity. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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T

arXiv 2020年

作者： Aloufi, Khalid S. Alsewari, Abdulrahman A. Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan Mala sia26300 Department of Computer Engineering College of Computer Science and Engineering Taibah University Medina Saudi Arabia

This research presents a methodology for trusting the provenance of data on the web. The implication is that data does not change after publication and the source of the data is stable. There are different data that should not change over time, such as published information in books and similar documents as well as news or events reported on the web. If the data change after publication on the web, the web pages that reference the unstable data will lose points of interest or link to different resources. With the current move to linked data and the semantic web, this is becoming a greater obstacle to be solved. This research presents a methodology for establishing trusted information using an encoded reference of the data embedded in its URI, which creates a stable reference of the data and a method for ensuring its provenance stability. After applying the methodology, the results showed that the methodology is highly applicable and has no overhead cost over the loading time. The novel solution can be applied directly to any data portals or web content management systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Websites

An Improved Image Compression Technique using Large Adaptive DCT Psychovisual Thresholds 2

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An Improved Image Compression Technique using Large Adaptive...

2nd IEEE International Conference on Knowledge Innovation and Invention, ICKII 2019

作者： Ernawan, Ferda Kabir, Muhammad Nomani Mustaffa, Zuriani Moorthy, Kohbalan Ramalingam, Mritha Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak Gambang Pahang26300 Malaysia

ISBN: (纸本)9781728101101

High quality multimedia requires high bandwidth and data transfer rate to transmit multimedia data in communication networks. Image compression is one of solutions to reduce the storage of multimedia data which in turn allows an efficient transmission through networks. An adaptive image compression technique through customized quantization tables based on user preference has been widely used in many applications. Scaling quantization table can significantly influence the reconstruction error and compression rate. This paper proposes an adaptive psychovisual threshold for customizing large quantization tables to improve image compression. An adaptive psychovisual threshold is computed based on a smooth curve of the absolute reconstruction error by incrementing the DCT frequency order. Experimental results show that the performance of adaptive large DCT psychovisual threshold achieves high image quality and minimum average bit length of Huffman code. The proposed method also demonstrates that boundary effects do not appear when the compressed image is zoomed in to 400%. © 2019 IEEE.

关键词： Data transfer

A Brief Review of Explainable Artificial Intelligence in Healthcare

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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

PhenoProfiler: Advancing Phenotypic Learning for Image-based Drug Discovery

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arXiv 2025年

作者： Li, Bo Zhang, Bob Zhang, Chengyang Zhou, Minghao Huang, Weiliang Wang, Shihang Wang, Qing Li, Mengran Zhang, Yong Song, Qianqian PAMI Research Group Department of Computer and Information Science University of Macau Taipa China Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing China Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Florida United States Faculty of Applied Sciences Macao Polytechnic University Taipa China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong China Department of Cancer Biology Wake Forest School of Medicine Winston SalemNC United States

In the field of image-based drug discovery, capturing the phenotypic response of cells to various drug treatments and perturbations is a crucial step. This process involves transforming high-throughput cellular images into quantitative representations for downstream analysis. However, existing methods require computationally extensive and complex multi-step procedures, which can introduce inefficiencies, limit generalizability, and increase potential errors. To address these challenges, we present PhenoProfiler, an innovative model designed to efficiently and effectively extract morphological representations, enabling the elucidation of phenotypic changes induced by treatments. PhenoProfiler is designed as an end-to-end tool that processes whole-slide multi-channel images directly into low-dimensional quantitative representations, eliminating the extensive computational steps required by existing methods. It also includes a multi-objective learning module to enhance robustness, accuracy, and generalization in morphological representation learning. PhenoProfiler is rigorously evaluated on large-scale publicly available datasets, including over 230,000 whole-slide multi-channel images in end-to-end scenarios and more than 8.42 million single-cell images in non-end-to-end settings. Across these benchmarks, PhenoProfiler consistently outperforms state-of-the-art methods by up to 20%, demonstrating substantial improvements in both accuracy and robustness. Furthermore, PhenoProfiler uses a tailored phenotype correction strategy to emphasize relative phenotypic changes under treatments, facilitating the detection of biologically meaningful signals. UMAP visualizations of treatment profiles demonstrate PhenoProfiler’s ability to effectively cluster treatments with similar biological annotations, thereby enhancing interpretability. These findings establish PhenoProfiler as a scalable, generalizable, and robust tool for phenotypic learning, offering transformative advancement

关键词： Benchmarking

AN IMPROVED GENERIC ER SCHEMA FOR CONCEPTUAL MODELLING OF INFORMATION systems

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arXiv 2020年

作者： Pieris, D. Wijegunasekera, M.C. Dias, N.G.J. Department of HRM Faculty of Commerce and Management Studies Department of Software Engineering Faculty of Computing and Technology Department of Computer Systems Engineering Faculty of Computing and Technology University of Kelaniya Sri Lanka

The Entity Relationship (ER) model is widely used for creating ER schemas for modelling application domains in the field of Information systems development. However, when an ER schema is transformed to a Relational Database Schema (RDS), some important information on the ER schema may not be represented meaningfully on the RDS. This causes loss of information during the transformation process. Although, several previous researches have proposed solutions to remedy the situation, the problem still exists. Thus, in this on-going research we wish to improve the proposed solutions and maximize information preservation in the ER to relational transformation process. Cardinality ratio constraints, role names, composite attributes, and certain relationship types are among the information frequently lost in the transformation process. Deficiencies in the ER model and the transformation method seems causing this situation. We take the view that if the information lost is resolved;a one-to-one mapping should exist from the ER schema to its RDS. We modified the ER model and the transformation algorithm following a heuristic research method with a view to eliminating the deficiencies and thereby achieving a one-to-one mapping. We should show that the mapping exists for any real world application. We create a generic ER schema - an ER schema that represents any phenomena in symbolic form - and use it to show that a one-to-one mapping exists for any real world application. In this paper, we explore our generic ER schema and its advantages over its predecessors in view of representing any real world application. Copyright © 2020, The Authors. All rights reserved.

关键词： Information use

A framework for managing enterprise architecture debts - Outline and research directions 10

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A framework for managing enterprise architecture debts - Out...

10th International Workshop on Enterprise Modeling and Information systems Architectures, EMISA 2020

作者： Alexander, Peter Hacks, Simon Jung, Jürgen Lichter, Horst Steffens, Ulrike Uludağ, Ömer RWTH Aachen University Research Group Software Construction Aachen Germany KTH Royal Institute of Technology Network and Systems Engineering Stockholm Sweden Frankfurt University of Applied Sciences Faculty of Computer Science and Engineering Frankfurt Germany Hamburg University of Applied Sciences Department of Computer Science Hamburg Germany Technical University of Munich Department of Informatics Munich Germany

Even though enterprise architecture management (EAM) offers a wide range of methods and tools for aligning business with IT, an architect’s work is challenged by reality. The evolution of enterprise architecture (EA) and given constraints (e.g. legacy systems and processes) lead to debts which may complicate and hinder opportunities;however, the management of such debts has not been considered in EAM research. This paper presents a framework for strategically managing EA-debt-related issues and propose open questions as well as future research directions in this field. Copyright © 2020 for this paper by its authors.

关键词： Legacy systems

Natidroid: Cross-language android permission specification

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arXiv 2021年

作者： Li, Chaoran Chen, Xiao Sun, Ruoxi Xue, Jason Wen, Sheng Ahmed, Muhammad Ejaz Camtepe, Seyit Xiang, Yang School of Software and Electrical Engineering Swinburne University of Technology HawthornVIC3122 Australia Department of Software Systems and Cybersecurity Faculty of IT Monash University ClaytonVIC3800 Australia School of Computer Science University of Adelaide North TerraceSA5005 Australia CSIRO Data61 Australia

The Android system manages access to sensitive APIs by permission enforcement. An application (app) must declare proper permissions before invoking specific Android APIs. However, there is no official documentation providing the complete list of permission-protected APIs and the corresponding permissions to date. Researchers have spent significant efforts extracting such API protection mapping from the Android API framework, which leverages static code analysis to determine if specific permissions are required before accessing an API. Nevertheless, none of them has attempted to analyze the protection mapping in the native library (i.e., code written in C and C++), an essential component of the Android framework that handles communication with the lower-level hardware, such as cameras and sensors. While the protection mapping can be utilized to detect various security vulnerabilities in Android apps, such as permission over-privilege and component hijacking, imprecise mapping will lead to false results in detecting such security vulnerabilities. To fill this gap, we thereby propose to construct the protection mapping involved in the native libraries of the Android framework to present a complete and accurate specification of Android API protection. We develop a prototype system, named NATIDROID, to facilitate the cross-language static analysis to benchmark against two state-of-the-art tools, termed AXPLORER [1] and ARCADE [2]. We evaluate NATIDROID on more than 11,000 Android apps, including system apps from custom Android ROMs and third-party apps from the Google Play. Our NATIDROID can identify up to 464 new API-permission mappings, in contrast to the worst-case results derived from both AXPLORER and ARCADE, where approximately 71% apps have at least one false positive in permission over-privilege and up to 3.6% apps have at least one false negative in component hijacking. Additionally, we identify that 24 components with at least one Native-triggered component hij

关键词： Mobile security