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A review on bystanders terminologies in cyberbullying

AIP Conference Proceedings 2022年第1期2617卷

作者： Rina Md Anwar Fiza Abdul Rahim Puven Alvin Victor 1Institute of Informatics and Computing Energy Universiti Tenaga Nasional (UNITEN) 43000 Kajang Selangor Malaysia 2Advanced Informatics Department Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia 54100 Kuala Lumpur Malaysia

Most of the communication today takes place online, thereby reflecting a significant part of our lives. Commuting from offline to online contact causes many challenges, misunderstandings, and differences, not knowing how words are interpreted in various terminologies. In terms of individuals virtually harming another individual-bully-victim scenario when online communication goes wrong, there are witnesses or bystanders who passively involved and do nothing. An upstander is a person who actively involved in the cyberbullying incident, opposes any unfair or intolerant acts, and intervenes on behalf the victims or bullies. While the research on programs to address cyberbullying is keep on increasing, only few studies focused on witness terminologies in cyberbullying. Our study aims to deeper understand the terminologies used in existing studies related to bystander behavior in cyberbullying. A systematic literature review approach was used using three databases. A total of 220 articles were extracted using a predefined search string. A specific criterion was applied to the extracted articles. A total of 99 articles were selected for further analysis. Based on the review, the bystander terminologies can be divided into Neutral, Positive and Negative.

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An Adaptive Digital Twin-Driven Model Based on Consumer Internet of Things To Ensure the Safe Operation of Aero-Engine

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IEEE Transactions on Consumer Electronics 2024年

作者： Gou, Ruijia Wang, Wei Liu, Zongyao Gadekallu, Thippa Reddy Amoon, Mohammed Lin, Chuan Xiamen University School of Informatics Xiamen361005 China Beijing Institute of Technology School of Medical Technology Beijing100081 China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Guangdong Shenzhen518172 China Northwestern Polytechnical University School of Power and Energy Xian710072 China Lovely Professional University Division of Research and Development Phagwara India Chitkara University Center of Research Impact and Outcome Punjab Rajpura140401 India King Saud University Department of Computer Science Community College Riyadh Saudi Arabia Northeastern University School of Software Shenyang110819 China

The aero-engine, often hailed as the manufacturing's 'crown jewel' presents considerable complexity in its design, manufacturing, and maintenance. To enhance safety and efficacy of the aero-engine especially through comprehensive monitoring and systematic testing, in this paper, we propose an Adaptive Cycle Engine (ACE) model, i.e., a digital twin-driven aero-engine model based on the Consumer Internet of Things (CIoT), to safely model the aero-engine. The proposed ACE model can simulate all categories of the aero-engine's working modes by setting the regulating components that control the opening and closing of bypass ducts, including mode selection valve and variable area bypass injector. Thus, each category of the working modes can be adaptively switched to enable aero-engine flexible adjustment and control. Furthermore, in the proposed ACE model, the common working conditions and the models of each mode are determined by the corresponding analysis of iterative variables and constraint equations for achieving the comprehensive performance analysis of steady state and transient conditions. Evaluation results demonstrate that the proposed ACE model can accurately simulate the operating state of the actual aero-engine, especially when it is compared with some of the famous commercial models. This work aim at promoting the development of consumer aero-engine. © 1975-2011 IEEE.

关键词： adaptive cycle engine (ACE) Consumer Internet of Things Digital twin variable area bypass injector

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Theoretical Bases of Identifying Determinants of Protection Intentions towards Bring-Your-Own-Device (BYOD) Protection Behaviors 1

Theoretical Bases of Identifying Determinants of Protection ...

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1st International Conference of Intelligent computing and Engineering, ICOICE 2019

作者： Al-Harthy, Ibrahim Mohammed Rahim, Fiza Abdul Ali, Nor"Ashikin Singun, Amando P. Computer Services Educational Technologies Centre Higher College of Technology Muscat Oman Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia College of Computing and Informatics Universiti Tenaga Nasional Malaysia Information Technology Department Higher College of Technology Muscat Oman

ISBN: (数字)9781728144870

ISBN: (纸本)9781728144870

Bring-Your-Own-Device (BYOD) has been acknowledged as a very good practice in the workplace. With its increasing popularity where personal as well as organizational data are accessed using BYOD, there have been raising concerns on privacy and security. Most BYOD risks are related to unauthorized access to policy changes and information, leaking sensitive information to people, breach of organization data and privacy, access control, misuse, and even stolen devices. Privacy violations are of widely security breaches in business organizations when users are using their own personal devices. In this paper, it presents the appropriate theories and models to derive a set of validated determinants of protection intentions towards the protection behaviors of BYOD. The introduction of BYOD calls for a thorough investigation of whether or not such a practice poses vulnerabilities and threats to organizations. © 2019 IEEE.

关键词： Sensitive data

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Performance Analysis of Deep Neural Networks for Object Classification with Edge TPU

Performance Analysis of Deep Neural Networks for Object Clas...

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International Conference on Information Technology and Multimedia (ICIM)

作者： Ahmad Ammar Asyraaf Jainuddin Yew Cheong Hou Mohd Zafri Baharuddin Salman Yussof College of Engineering Universiti Tenaga Nasional Kajang Selangor Malaysia Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Kajang Selangor Malaysia

ISBN: (数字)9781728173108

ISBN: (纸本)9781728173115

Deep learning becomes a more popular, widespread, and common tool in almost any task that requires information extraction from a large dataset. Hence, the data transmission speed between the data-gathering devices and processing units can be crucial in hardware selection depending on the machine learning application. Generally, the processing unit is usually centralized, and the data transferring time will increase when the data-gathering devices were installed further away from the processing unit. The work aims to provide the performance analysis on Google's new machine learning hardware called Edge TPU that was created specifically for edge devices. Furthermore, the work also reviewed the different types of deep neural network models as current benchmarks in deep learning were tested with different hardware used in edge applications. The review also discussed the comparison of the performance of the edge device using the deep neural networks in Tensorflow. From the results obtained, the performance of the edge device with the Edge TPU is faster than the device without it.

关键词： Performance evaluation Deep learning Neural networks Benchmark testing Hardware Performance analysis Internet

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Risk-ranking matrix for security patching of exploitable vulnerabilities

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AIP Conference Proceedings 2023年第1期2808卷

作者： Mohammad Shamsul Hoque Norziana Jamil Nowshad Amin Muhamad Mansor College of Computing and Informatics Universiti Tenaga Nasional Kajang Selangor Malaysia Institute of Sustainable Energy Universiti Tenaga Nasional Kajang Selangor Malaysia Institute of Power Engineering Universiti Tenaga Nasional Kajang Selangor Malaysia

A vulnerability in cybersecurity can be any weakness within a software or hardware of any information systems, internal controls, network or system processes that can be exploited to cause damage, or allow an attacker to manipulate the system in some way. Since the late 1980s cyberattacks through exploiting vulnerabilities started to evolve and increasingly becoming sophisticated and dangerous. Successful cyber-attacks are primarily takes place through the exploitation of vulnerabilities. Although thousands of vulnerabilities are being detected and registered each year it has been observed that only few of them get exploited by threat actors. Hence, there is a need to utilize machine learning to develop a model to predict the highly exploitable vulnerabilities by the threat actors and a model to predict the number of future vulnerability to support a cost-effective cyber security management. Subsequently, the predicted exploitable vulnerabilities need to be ranked to understand their severity impact, if the exploitation is realized. The literature reviews show that all the existing machine learning models primarily have utilized United States (U.S) vulnerability database, the largest in its kind, as the source of vulnerability data. The literature review shows that there are existing research works with machine learning approaches to forecast the number of future vulnerabilities and to predict the highly exploitable vulnerabilities, but the literature shows that a risk ranking matrix is missing in this domain. Hence, the need to fill up this gap is of urgent need. The aim of this research is to develop a novel risk matrix that ranks the severity impact of highly exploitable vulnerabilities. To achieve this scope we have developed machine learning based model to predict the highly exploitable vulnerabilities to work as background engines to find the most exploitable vulnerabilities out of published known vulnerabilities. Unlike few existing research works, our propos

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An Anomaly Detection Technique for Deception Attacks in Industrial Control Systems

An Anomaly Detection Technique for Deception Attacks in Indu...

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IEEE International Conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart computing (HPSC) and Intelligent Data and Security (IDS)

作者： Q.S. Qassim A.R. Ahmad R. Ismail A. Abu Bakar F. Abdul Rahim M.Z. Mokhtar R. Ramli B. Mohd Yusof Mohammed Najah Mahdi Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia College of Computing & Informatics Universiti Tenaga Nasional Malaysia Institute of Informatics and Computing in Energy Universiti Tenaga Nasional 43000 Malaysia

The increasing interaction of modern industrial control systems (ICS) to the outside Internet world influences making these systems vulnerable to a wide range of cyber-attacks. Moreover, the utilisation of Commercial-off-the-Shelf (COTS) products, as well as open communication protocols, made them attractive targets to various threat agents including cyber-criminals, national-state, and cyber-terrorists. Given that, today's ICSs are deriving the most critical national infrastructures. Therefore, this raises tremendous needs to secure these systems against cyber-attacks. Intrusion detection technology has been considered as one of the most essential security precautions for ICS networks. It can effectively detect potential cyber-attacks and malicious activities and prevent catastrophic consequences. This paper puts forward a new method to detect malicious activities at the ICS net-works.

关键词： Integrated circuits Telecommunication traffic Industrial control Monitoring Feature extraction Intrusion detection

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A new model to analyze power and communication system intra-and-inter dependencies

arXiv

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arXiv 2020年

作者： Roy, Sohini Chandrasekaran, Harish Pal, Anamitra Sen, Arunabha School of Computing Informatics and Decision System Engineering School of Electrical Computer and Energy Engineering Arizona State University Tempe- AZ85281 United States

—The reliable and resilient operation of the smart grid necessitates a clear understanding of the intra-and-inter dependencies of its power and communication systems. This understanding can only be achieved by accurately depicting the interactions between the different components of these two systems. This paper presents a model, called modified implicative interdependency model (MIIM), for capturing these interactions. Data obtained from a power utility in the U.S. Southwest is used to ensure the validity of the model. The performance of the model for a specific power system application namely, state estimation, is demonstrated using the IEEE 118-bus system. The results indicate that the proposed model is more accurate than its predecessor, the implicative interdependency model (IIM) [1], in predicting the system state in case of failures in the power and/or communication systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Phasor measurement units

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Advancing Thyroid Pathologies Detection with Recurrent Neural Networks and Micro-FTIR Hyperspectral Imaging

Advancing Thyroid Pathologies Detection with Recurrent Neura...

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Annual IEEE Symposium on Computer-Based Medical Systems

作者： Matheus De Freitas Oliveira Baffa Luciano Bachmann Denise Maria Zezell Thiago Martini Pereira Thomas Martin Deserno Joaquim Cezar Felipe Dept. of Computing and Mathematics University of São Paulo (USP) Ribeirao Preto SP Brazil Dept. of Physics University of São Paulo (USP) Ribeirao Preto SP Brazil Nuclear and Energy Research Institute São Paulo SP Brazil Dept. of Science and Technology Federal University of são Paulo (UNIFESP) São José dos Campos SP Brazil Peter L. Reichertz Institute for Medical Informatics Technische Universität Braunschweig Braunschweig Germany

Thyroid disorders are a complex group of diseases that require an accurate diagnosis for effective treatment. Fine-needle aspiration biopsies can assist in detecting many thyroid diseases. These materials can be analyzed visually using traditional computer vision methods, despite the limitations of complex samples. To address this problem, we propose a novel approach that uses hyperspectral imaging (HSI) to analyze thyroid biological samples. HSI measures the absorbance of infrared light by biological samples using a micro Fourier transform infrared spectroscopy (micro-FTIR) and converts this data into hyperspectral images. In this study, we used HSI to train and validate a recurrent neural network to classify thyroid samples as healthy, cancerous, or goiter. Our experiments, based on the k-fold cross-validation, achieved an overall accuracy of 96.88%, a sensitivity of 96.87%, and a specificity of 98.45%. These results demonstrate the potential of hyperspectral imaging as a tool to assist pathologists in the diagnosis of thyroid disease.

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Decentralized attribution of generative models

arXiv

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arXiv 2020年

作者： Kim, Changhoon Ren, Yi Yang, Yezhou School of Computing Informatics and Decision Systems Engineering Arizona State University United States School for Engineering of Matter Transport and Energy Arizona State University United States

Growing applications of generative models have led to new threats such as malicious personation and digital copyright infringement. One solution to these threats is model attribution, i.e., the identification of user-end models where the contents under question are generated from. Existing studies showed empirical feasibility of attribution through a centralized classifier trained on all user-end models. However, this approach is not scalable in reality as the number of models ever grows. Neither does it provide an attributability guarantee. To this end, this paper studies decentralized attribution, which relies on binary classifiers associated with each user-end model. Each binary classifier is parameterized by a user-specific key and distinguishes its associated model distribution from the authentic data distribution. We develop sufficient conditions of the keys that guarantee an attributability lower bound. Our method is validated on MNIST, CelebA, and FFHQ datasets. We also examine the trade-off between generation quality and robustness of attribution against adversarial post-processes. Copyright © 2020, The Authors. All rights reserved.

关键词： Economic and social effects

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Towards SCADA Threat Intelligence based on Intrusion Detection Systems - A Short Review

Towards SCADA Threat Intelligence based on Intrusion Detecti...

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International Conference on Information Technology and Multimedia (ICIM)

作者： Qais Saif Qassim Norziana Jamil Mohammed Najah Mahdi Azril Azam Abdul Rahim Ibri College of Technology Ibri Sultanate of Oman Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Selangor Malaysia Tenaga Nasional Berhad Kuala Lumpur Malaysia

ISBN: (数字)9781728173108

ISBN: (纸本)9781728173115

Securing and protecting Supervisory Control and Data Acquisition (SCADA) systems have been an active topic of research for the past decades due to the catastrophic and disastrous consequences when these systems are breached or compromised. Therefore, possible cyberattacks and malicious behaviours must be addressed instantly to prevent catastrophic and disastrous consequences on the national critical infrastructures. To this end, intrusion detection systems are considered as an essential security defence mechanism for SCADA networks. It can effectively detect potential cyberattacks and malicious activities and prevent catastrophic consequences. However, zero-day, deception and stealth attacks require a special type of detection methods in which it should be able to identify anomalies and detect possible malicious activities. There are a handful number of studies that have been carried out previously in this regard. However, this area of research is still immature and emerging. Therefore, this research is intended to investigate the merits, limitations and drawbacks of the existing detection methods, investigates the cyberattacks on SCADA systems, identify key features that can be used to discover irregular activities and to put forward the requirements and recommendations for detect cyberattacks and malicious activities in the SCADA networks.

关键词： Protocols SCADA systems Intrusion detection Software Security Reliability Computer crime

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