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4th International Conference on Information Systems and computer Networks, ISCON 2019

作者： Nautiyal, Swati Wadhwa, Shruti NITTTR Department of Computer Science and Engineering Chandigarh India Post Graduate Govt. College Department of Computer Science Chandigarh India

ISBN: (数字)9781728136516

ISBN: (纸本)9781728136516

In cloud computing, a new threat is introduced namely Economic Denial of Sustainability (EDoS) which is similar to Distributed Denial of Services (DDoS) attack that targets the vulnerabilities of the cloud server user along with its price model. EDoS attackers continually request bulk resources, such as virtual security devices and databases, virtual machines, virtual networking devices, that targets the auto scaling feature of cloud. As a result, there is a huge increase in consumer bill due to the illegitimate requests which results in bankruptcy. In this paper, various EDoS mitigation techniques are discussed with their different approaches. This paper proposed a new approach that uses Artificial Neural Network along with Genetic Algorithm. The classification of is done using Artificial Neural Network that that classify the cloud server consumer and may lessen the EDoS attacks in the cloud environment whereas Genetic Algorithm (GA) is deployed for optimization of the properties of each server using pertinent fitness function. The proposed technique's comparison with existing technique is taken under the considerations and expectations. © 2019 IEEE.

关键词： Neural networks

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Hoax Classification in Imbalanced Datasets Based on Indonesian News Title using RoBERTa 3

Hoax Classification in Imbalanced Datasets Based on Indonesi...

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3rd International Conference on Artificial Intelligence and Data sciences, AiDAS 2022

作者： Muliono, Yohan Gaol, Ford Lumban Soewito, Benfano Warnars, Harco Leslie Hendric Spits School of Computer Science Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665491648

ISBN: (纸本)9781665491648

Hoaxes are something that can not be avoided, especially in Indonesia, where the literacy rate in Indonesia is quite low, they are easy to believe in news without doing fact check. The worst thing is that news that is trusted by the public may not be read completely through the entire content. They believe, from the title alone could already covers the entire content of the news. Media in the other side, are also competing to make controversial titles so that their traffic is improved. The research that will be carried out by us is where we can classify a news whether it is a fact or a hoax from the title alone. The RoBERTa (A Robustly Optimized BERT Pretraining Approach) model will be used in this study, because in several previous studies RoBERTa has proven to be good for classification. The accuracy achieved in this study also reached 99.52% with an accuracy validation of 93.84% which shows that even with an imbalanced dataset the classification shows a promising result by using the RoBERTa model which data is balanced using the undersampling method. © 2022 IEEE.

关键词： Classification (of information)

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Ransomware Resilience: Investigating Organizational Security Culture and Its Impact on Cybersecurity Practices against Ransomware Threats

Ransomware Resilience: Investigating Organizational Security...

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2023 International Conference on Informatics Engineering, science and Technology, INCITEST 2023

作者： Yulianto, Semi Soewito, Benfano Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350317862

The increasing threat of ransomware attacks in an interconnected and digital world presents important hurdles for cybersecurity, prompting further examination into how organizational security culture affects their ability to withstand these malicious threats. This study examines the impact and importance of organizational security culture in ransomware threat mitigation. We have surveyed five organizations, giving points from 1 to 5 for each question on cultural factors, leadership support, employee awareness and involvement (compliance), communication, behavior change or BBS practice incorporation, learning/training delivery methodology, and Size and industry impact on their security culture. The results indicate that a robust security culture, with leadership and effective communication, builds stronger resilience to ransomware. Organizations with jointly accountable cybersecurity and training practices are highly efficient. Suggestions range from establishing a secure environment to increasing management buy-in and integrating compliance. This work helps advance our understanding of ransomware resilience and highlights the key role of organizational security culture. The resulting learnings can help to inform organizations' security posture, ultimately helping them better protect their defenses against ransomware threats. © 2023 IEEE.

关键词： Cybersecurity

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Integrated Framework for IT Asset Physical Security: A Risk Management Approach Using NIST and ISO Standards

Integrated Framework for IT Asset Physical Security: A Risk ...

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2024 International Conference on Information Technology Systems and Innovation, ICITSI 2024

作者： Alamsyah, Ricky Arifin, Yulyani Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331511470

This study presents a comprehensive approach to IT asset security risk management in physical access control systems, integrating NIST SP 800-116, 800-53, 800-30, ISO 31000, and 27002:2022 standards. Using a mixed-method approach, we conducted interviews, discussion forums, and observations with IT and security professionals in manufacturing and insurance sectors in Indonesia. Our findings reveal seven key factors crucial for physical security of IT assets, including policies, access authorization, and environmental controls. The study proposes a risk assessment model that determines authentication factor levels based on risk severity. Results indicate discrepancies between current practices and recommended security levels, particularly in server rooms and production areas. We provide a set of control recommendations aligned with ISO 27002:2022 to enhance physical security measures. This research contributes to both theory and practice by offering a holistic framework for managing physical security risks in IT environments, particularly relevant for organizations in emerging economies. © 2024 IEEE.

关键词： ISO Standards

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Analysing the Impact of Rhythm Game in Enhancing Typing Performance 10

Analysing the Impact of Rhythm Game in Enhancing Typing Perf...

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10th International HCI and UX Conference in Indonesia, CHIuXiD 2024

作者： Lancaster, Lemuel Arifin, Yulyani Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350380439

The research was centered on adapting and assessing a new, unique game concept. The game onKeys presents a new approach and poses as an alternative method for improving typing skills. However, despite offering a promising avenue, the game still grapples with certain limitations. Utilizing the MDA Framework, the study seeks to integrate additional game components into onKeys and evaluate its effectiveness in improving typing skills. A paired T-test is used to assess the Pre-Test and post-Test data. The System Usability Scale gathers subjective feedback about the game design's usability. This study found that the onKeys game has little impact on players' typing speed and accuracy, which may be attributed to a higher average typing speed, short period of playtime or a small sample size. However, upon reviewing the record and survey data, particularly the System Usability Score, it becomes evident that the game design still has potential. Further research can delve deeper into this game concept and design. © 2024 IEEE.

关键词： Game design

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All-in-One Machine Learning Framework for Early Detection and Characterization of Sugar Beet Diseases Using Hyperspectral Imaging

SSRN

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SSRN 2025年

作者： Abdalla, Alwaseela Todd, Olivia E. Pennam, Sai V.P.K. Hoopes, Emma Nguyen, Nga T. Dorn, Kevin M. Dao, Phuong D. Department of Agricultural Biology Colorado State University Fort CollinsCO80523 United States Graduate Degree Program in Ecology Colorado State University Fort CollinsCO80523 United States School of Global Environmental Sustainability Colorado State University Fort CollinsCO80523 United States USDA-ARS Soil Management and Sugarbeet Research Unit Fort CollinsCO80626 United States Department of Computer Science Colorado State University Fort CollinsCO80523 United States

Soilborne diseases like Fusarium oxysporum and Rhizoctonia solani significantly impact sugar beet production, causing major yield losses. Accurate disease rating and characterization enhance disease management and breeding by tracking progression, assessing resistance, and guiding control strategies. Existing diagnostic approaches often focus on limited aspects of disease assessment, addressing only one or two ICQP objectives—identification, classification, quantification, and prediction—leaving gaps in comprehensive disease management. This study proposes an all-in-one framework that integrates hyperspectral imaging and machine learning to address all ICQP objectives. Hyperspectral data were collected from 122 plants inoculated with F. oxysporum and R. solani over 30 days using a Specim IQ hyperspectral sensor (400–1000 nm, 204 bands). To ensure accurate spectral data extraction, image segmentation was performed using a trained Deeplabv3+ model. Optimal wavelengths for each ICQP task were identified using the ANOVA algorithm and fed into three machine learning classifiers, including random forest (RF), multilayer perceptron (MLP), and support vector machine (SVM). The study revealed that no single spectral region or machine learning model was universally optimal across all ICQP objectives. Chlorophyll-sensitive wavelengths (670–700 nm) were optimal for both F. oxysporum and R. solani disease identification, while the near-infrared range (830–1000 nm) provided critical insights for disease type classification. RF achieved the highest accuracy (96%) in identifying healthy and infected plants and demonstrated strong performance in disease type classification. For disease quantification, MLP achieved superior results with 94% accuracy and an IoU of 88%, enabling detailed pixel-level mapping of disease severity with high confidence. This study demonstrates the importance of task-specific optimization in spectral analysis and machine learning, linking spectral features t

关键词： Image segmentation

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Unified algorithm to improve reinforcement learning in dynamic environments: An instance-based approach

Unified algorithm to improve reinforcement learning in dynam...

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14th International Conference on Enterprise Information Systems, ICEIS 2012

作者： Ribeiro, Richardson Favarim, Fábio Barbosa, Marco A.C. Borges, André Pinz Dordal, Osmar Betazzi Koerich, Alessandro L. Enembreck, Fabrício Graduate Program in Computer Engineering Federal Technological University of Paraná Pato Branco Paraná Brazil Post-Graduate Program in Computer Science Pontificial Catholical University of Paraná Curitiba Paraná Brazil

ISBN: (纸本)9789898565105

This paper presents an approach for speeding up the convergence of adaptive intelligent agents using reinforcement learning algorithms. Speeding up the learning of an intelligent agent is a complex task since the choice of inadequate updating techniques may cause delays in the learning process or even induce an unexpected acceleration that causes the agent to converge to a non-satisfactory policy. We have developed a technique for estimating policies which combines instance-based learning and reinforcement learning algorithms in Markovian environments. Experimental results in dynamic environments of different dimensions have shown that the proposed technique is able to speed up the convergence of the agents while achieving optimal action policies, avoiding problems of classical reinforcement learning approaches.

关键词： Reinforcement learning

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Evaluation of desktop operating systems under thrashing conditions

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Journal of the Brazilian computer Society 2013年第1期19卷 29-42页

作者： Maziero, Carlos dos Santos, Douglas Santin, Altair Computer Science Department Federal University of Technology-Paraná Curitiba PR Brazil Graduate Program in Computer Science Pontifical Catholic University of Paraná State Curitiba PR Brazil

Virtual memory mechanisms allow offering more RAM memory space to processes than the amount of memory physically available in the system, using disk space as a memory extension. When there is not enough RAM memory to satisfy the active processes' working sets, the system throughput may be significantly reduced, due to the excessive paging activity. Such situation is known as memory thrashing. This paper presents an evaluation of some commodity desktop operating systems under thrashing conditions. We also identify and discuss the performance data about memory management available in each system and the mechanisms available to collect it. A portable benchmark tool was developed to bring each system to a thrashing situation and then pull it back to normality. The results observed from the systems under study are compared and discussed. © 2012 The Brazilian computer Society.

关键词： Random access storage

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Alphabet-Level Indian Sign Language Translation to Text Using Hybrid-AO Thresholding with CNN

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Intelligent Automation & Soft Computing 2023年第9期37卷 2567-2582页

作者： Seema Sabharwal Priti Singla Department of Computer Science and Engineering Baba Mastnath UniversityRohtak124001India Department of Computer Science Government Post Graduate College for WomenPanchkula134109India

Sign language is used as a communication medium in the field of trade,defence,and in deaf-mute communities *** the last few decades,research in the domain of translation of sign language has grown and become more *** necessitates the development of a Sign Language Translation System(SLTS)to provide effective communication in different research *** this paper,novel Hybrid Adaptive Gaussian Thresholding with Otsu Algorithm(Hybrid-AO)for image segmentation is proposed for the translation of alphabet-level Indian Sign Language(ISLTS)with a 5-layer Convolution Neural Network(CNN).The focus of this paper is to analyze various image segmentation(Canny Edge Detection,Simple Thresholding,and Hybrid-AO),pooling approaches(Max,Average,and Global Average Pooling),and activation functions(ReLU,Leaky ReLU,and ELU).5-layer CNN with Max pooling,Leaky ReLU activation function,and Hybrid-AO(5MXLR-HAO)have outperformed other *** open-access dataset of ISL alphabets with approx.31 K images of 26 classes have been used to train and test the *** proposed framework has been developed for translating alphabet-level Indian Sign Language into *** proposed framework attains 98.95%training accuracy,98.05%validation accuracy,and 0.0721 training loss and 0.1021 validation loss and the perfor-mance of the proposed system outperforms other existing systems.

关键词： Sign language translation CNN thresholding Indian sign language

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Software requirements selection and prioritization using SBSE approaches: A systematic review and mapping of the literature

Software requirements selection and prioritization using SBS...

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作者： Pitangueira, Antônio Mauricio Maciel, Rita Suzana P. Barros, Márcio Federal University of Bahia Computer Science Department Brazil Post-graduate Information Systems Program Unirio Brazil Federal Institute of Bahia Brazil

The selection and prioritization of software requirements represents an area of interest in Search-Based Software Engineering (SBSE) and its main focus is finding and selecting a set of requirements that may be part of a software release. This paper presents a systematic review and mapping that investigated, analyzed, categorized and classified the SBSE approaches that have been proposed to address software requirement selection and prioritization problems, reporting quantitative and qualitative assessment. Initially 39 papers returned from our search strategy in this area and they were analyzed by 18 previously established quality criteria. The results of this systematic review show which aspects of the requirements selection and prioritization problems were addressed by researchers, which approaches and search techniques are currently adopted to address these problems, as well as the strengths and weaknesses in this research area highlighted from the quality criteria. © 2014 Elsevier Inc. All rights reserved.

关键词： Requirements engineering

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