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22nd European Conference on Cyber Warfare and Security, ECCWS 2023

作者： Barman, Fouz Alkaabi, Nora Almenhali, Hamda Alshedi, Mahra Ikuesan, Richard Computing and Applied Technology Department College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates

ISBN: (纸本)9781914587702

Reconnaissance and enumeration are both equally significant phases of the penetration testing lifecycle. In hindsight, both reconnaissance and enumeration seem to be very similar as the pair involve information gathering. Whilst reconnaissance leverages passive approaches without direct interaction with the target, enumeration exploits susceptibilities and vulnerabilities in direct client-server communication. Both phases involve gathering information and pinpointing the attack surface within the network of the target. To do so, powerful tools such as Nmap and Netcat are utilized by ethical hackers and penetration testers to identify and resolve security vulnerabilities and weaknesses. Nmap is an open-source command-line tool used for information gathering, network discovery, and security auditing. Whereas Netcat is a back-end tool that manages networks, monitors traffic flow between systems, as well as allows port scanning and listening. However, the plethora of tools and approaches available for these two phases often introduce inconsistencies and time wastage, which can lead to frustration and poor outcome for inexperienced penetration testers. Additionally, not all commands found online are relevant and applicable. In such situations, there is a high probability that the user will feel overwhelmed and exasperated with the overflow of new and foreign information. To address this daunting challenge, this study developed a methodical framework that can provide a technical guide for the reconnaissance and enumeration phases of the penetration testing lifecycle. Furthermore, a clear and thorough step-by-step procedure and detailed explanations of each stage and commands initiated using Nmap and Netcat are provided. The output of this study will be extremely beneficial and informative to a vast group of audience, ranging from university students majoring in security to individuals interested in ethical hacking, and even someone looking for a job with a position of a p

关键词： Personal computing

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Digital forensic in a virtual world: A case of metaverse and vr 22

Digital forensic in a virtual world: A case of metaverse and...

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22nd European Conference on Cyber Warfare and Security, ECCWS 2023

作者： Ali, Tayba Al Alfulaiti, Sara Abuzour, Manal Almaqahami, Sheikha Ikuesan, Richard Computing and Applied Technology Department College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates

ISBN: (纸本)9781914587702

Metaverse is a virtual space where users can interact with each other. It is a combination of virtual reality, augmented reality, and mixed reality. This evolving technology can offer many exciting opportunities that can be used for individuals and businesses. Although this technology has many advantages, people are misusing it for their benefit. Many cyberattacks are occurring in the metaverse world because it has various vulnerabilities and privacy issues. This paper explains four cyberattacks and a case scenario of each attack as it relates to the metaverse. Additionally, this study developed a metaverse forensic framework that can be used to investigate cyberattacks in the metaverse world. Furthermore, this study describes how forensic examiners can conduct a forensic investigation using state-of-The-Art forensic solutions and tools. The developed framework can be used by forensic examiners, security researchers, as well as the general scientific community for the security of the metaverse. © 2023 Curran Associates Inc.. All rights reserved.

关键词： Mixed reality

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Towards the development of indicators of fake websites for digital investigation 22

Towards the development of indicators of fake websites for d...

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22nd European Conference on Cyber Warfare and Security, ECCWS 2023

作者： Alremeithi, Mera Alkuwaiti, Aysha Alobeidli, Haya Ikuesan, Richard Computing and Applied Technology College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates

ISBN: (纸本)9781914587702

A fake website is considered a website that is intended to cause harm and manipulate users, especially novice users without some knowledge of indicators of fakeness. Understanding the indicators of fake websites is thus considered an important concept to avoid being a victim of malicious attacks in online engagements. In some cases, such knowledge is required to reduce the potential attack surface of cyber criminals. However, the increasing rate of website diversity and complexities makes it difficult for an individual to distinguish between a fake and a real website while compounding the investigation process of a website. Also, the growing rate of website imitation technology and website domain closure presents a veritable platform for the development of fake websites. As a step towards determining the genuineness of a website, this study developed a forensic framework based on an exploratory analysis of different genres of fake websites. To achieve this, forensic methodologies and processes were applied to methodically selected samples of known fake websites based on three fakeness categories: Hoaxes, Cybersquatting, and Sweepstakes. The result revealed the existence of salient markers which can be used as indicators of fakeness and can be applied across a wide genre of websites. Furthermore, the resultant observation was used to develop a digital forensic framework for website fakeness evaluation. The developed framework was benchmarked to the ISO 27043/2015 and the NIST SP800-86 standard for completeness and relevance to forensic investigation processes. By leveraging the proposed digital forensic framework, an investigation can develop a reliable pointer to evaluate the genuineness of any website, which can significantly reduce the investigation time. For a non-forensic individual, the developed framework can be leveraged to identify, at first glance, the degree of fakeness of a website. Such a mechanism can therefore provide a useful tool to reduce the potent

关键词： Websites

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Infeasibility of Sequitur-Based Motif for Mouse Dynamics in Digital Forensics 12

Infeasibility of Sequitur-Based Motif for Mouse Dynamics in ...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Ikuesan, Richard Iqbal, Farkhund Hayawi, Abdul Kadhim College of Technological Innovation Zayed University Computing and Applied Technology Abu Dhabi United Arab Emirates College of Interdisciplinary Studies Zayed University Computational Systems Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350330366

Utilizing and deploying behavioral biometric modalities (BBM), specifically mouse dynamics for user attribution in a digital investigation, has seen a rapid upsurge. However, as asserted in a recent study, the current reliability threshold of BBM falls short of the required standard for forensic attributes. This poor reliability can be attributed, in part, to the low signal-to-noise ratio in a typical behavioral dataset. This study proposed a context-free signature identification and extraction technique for BBM to extract a unique mouse dynamics signature suitable for a forensic process. A Re-Pair Grammar induction approach, which identifies and extracts unique Grammar sequences, was used to achieve this proposition. The grammar generation leverages symbolic aggregate approximation techniques to generate behavioral string subsequences from the mouse dataset. The Re-Pair approach was then used to develop a user attribution mechanism, which can be deployed for digital forensic analysis. The outcome of the implementation of the proposition, however, shows a poor performance relative to existing studies, hence its infeasibility as a benchmark for forensic science. However, it shows promising potential to reveal the inherent noise in mouse dynamics data, which can provide further insight into digital forensic science. This result further extends the literature on establishing digital forensic science, a significant requirement for any forensic discipline. © 2024 IEEE.

关键词： Digital forensics

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Functional architectural design of a digital forensic readiness cybercrime language as a service 22

Functional architectural design of a digital forensic readin...

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22nd European Conference on Cyber Warfare and Security, ECCWS 2023

作者： Baror, Stacey Omeleze Ikuesan, Richard Adeyemi Venter, Hein S. DigiForS Research Group University of Pretoria South Africa Computing and Applied Technology Department College of Technological Innovation Zayed University United Arab Emirates

ISBN: (纸本)9781914587702

Developing a generic digital forensic solution in a cloud computing platform that can address the functional requirements of digital forensic stakeholders is a complex process. The solution would require a technology-independent architectural design that addresses the challenges of incident threat identification, triggering, incident threat isolation and investigation. Existing approaches are limited to the functionality that treats these four challenges individually without the due diligence to consider their interoperability. This study proposes a context-independent and technology-neutral architecture to address these issues by developing a digital forensic readiness (DFR) based on a human language communication interaction (HLI) system that could create a cybercrime language as a service (DFClaaS). The functional architectural design of the proposed DFR HLI DFClaaS system comprises microservices, layered and event/component-based architectural patterns on top of cloud architectural patterns. The DFR HLI DFClaaS system integrates flexibility and other quality requirements to separate concerns while accommodating rigid requirements like security and reliability. The developed architecture is essential for any human-centred digital forensic solution. Therefore, integrating the developed architecture presents a reliable baseline for the digital forensic community. © 2023 Curran Associates Inc.. All rights reserved.

关键词： Semantics

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Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

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Computers, Materials & Continua 2024年第6期79卷 3585-3629页

作者： Imtiaz Ahmad Tariq Hussain Babar Shah Altaf Hussain Iqtidar Ali Farman Ali Institute of Computer Sciences and Information Technology The University of AgriculturePeshawarPakistan School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhouChina College of Technological Innovation Zayed UniversityDubaiUnited Arab Emirates School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Department of Applied AI School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063South Korea

Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and *** of them is a sensor network with embedded sensors working as the primary nodes,termed Wireless Sensor Networks(WSNs),in which numerous sensors are connected to at least one Base Station(BS).These sensors gather information from the environment and transmit it to a BS or gathering *** have several challenges,including throughput,energy usage,and network lifetime *** strategies have been applied to get over these *** may,therefore,be thought of as the best way to solve such ***,it is crucial to analyze effective Cluster Head(CH)selection to maximize efficiency throughput,extend the network lifetime,and minimize energy *** paper proposed an Accelerated Particle Swarm Optimization(APSO)algorithm based on the Low Energy Adaptive Clustering Hierarchy(LEACH),Neighboring Based Energy Efficient Routing(NBEER),Cooperative Energy Efficient Routing(CEER),and Cooperative Relay Neighboring Based Energy Efficient Routing(CR-NBEER)*** the help of APSO in the implementation of the WSN,the main methodology of this article has taken *** simulation findings in this study demonstrated that the suggested approach uses less energy,with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH,1.003 to 0.0521 for 10 CH,and 0.1734 to 0.0911 for 15 *** sending packets ratio was also raised for all three CH selection scenarios,increasing from 659 to *** number of dead nodes likewise dropped for the given combination,falling between 71 and *** network lifetime was deemed to have risen based on the results found.A hybrid with a few valuable parameters can further improve the suggested APSO-based *** to underwater,WSN can make use of the proposed *** overall results have been evaluated and

关键词： Wireless sensor network cluster head selection low energy adaptive clustering hierarchy accelerated particle swarm optimization

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Classification Framework for Autoimmune Liver Disease using Machine Learning and Deep Learning Techniques 5

Classification Framework for Autoimmune Liver Disease using ...

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5th technology Innovation Management and Engineering Science International Conference, TIMES-iCON 2024

作者： George, E. Ben Rosline, G. Jeba Varghese, Abraham Rajesh, Gnana D. Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Muscat Oman Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Al Mussanah Oman

ISBN: (纸本)9798350362602

—Autoimmune diseases affect people of all ages, genders, and ethnic backgrounds, making it one of the most widespread health issues we face today. These disorders occur when the immune system mistakenly targets healthy cells within the body and causing a wide range of potentially life-threatening diseases. There are over 80 recognized autoimmune disorders, including type-1 diabetes, rheumatoid arthritis, Graves' disease, alopecia, celiac disease, and autoimmune liver disease(AiLD). Autoimmune liver disease is one such chronic disorder characterized by liver inflammation and damage. If not detected on time and untreated within the given time, it might lead to liver cirrhosis or liver cancer. Identifying and treating autoimmune liver disease is a complex and time consuming process which involves symptom analysis, laboratory tests, and clinical assessments. Detecting the disease can be more challenging because many symptoms of autoimmune liver diseases overlap with those of other autoimmune conditions. As a result, there is a need for an optimized, accurate, efficient, and cost-effective methodology to identify the disease without extensive and invasive procedures such as organ transplants. This paper introduces an innovative approach that employs the power of supervised machine learning and deep learning techniques to predict the onset of autoimmune liver diseases in advance. The machine learning model is constructed using a comprehensive dataset comprising the complete medical history of patients, physical examination records, histopathology and genetic data. Along with the machine learning model, the deep learning model is also developed for liver MRI images of various patients to make accurate prediction. This system provides a second opinion for medical professionals that may avoid the need for extensive and invasive medical procedures, including biopsies, and ultimately improve patient care and outcomes. ©2024 IEEE.

关键词： Autoimmune Liver Disease Classification Deep Learning Machine Learning

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Efficient Diagnoses of Breast Cancer Disease Using Deep Learning Technique 24

Efficient Diagnoses of Breast Cancer Disease Using Deep Lear...

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10th International Conference on computing and Artificial Intelligence, ICCAI 2024

作者： Raza, Muhammad Ali Khattak, Asad Masood Abbas, Wasim Asghar, Muhammad Zubair Gomal Research Institute of Computing Faculty of Computing Gomal University D.I.Khan Pakistan Department of Computing and Applied Technology College of Technological Innovation Zayed University Abu Dhabi Campus 144534 United Arab Emirates CEO TECHSACARE PTE. LTD Singapore

ISBN: (纸本)9798400717055

According to WHO 2023 survey, each year more than 2.3 million breast cancer cases are reported. Breast cancer is the either first or second biggest disease in females that is the cause of death in almost 95% of countries. Diagnosing breast cancer in its early stage can be helpful to overcome this disease and result in an increase in the survival chance of the patient. Machine learning (ML) models and well-established methods for encoding categorical data have produced a wide variety of surprising outcomes when used to diagnose breast cancer using datasets that are imbalanced from testing. Early experiments also used an artificial neural network(ANN) to extract characteristics without understanding the sequencing data. In this study, we present a hybrid deep learning (DL) BiLSTM-CNN model, in order to diagnose breast cancer efficiently from patient data. The BiLSTM-CNN model was applied after dataset balancing. Contrasting to previous investigations, the experimental results of our suggested hybrid DL model were outstanding, with an accuracy of 99.3%, a precision of 99%, a recall of 99%, and an F1-score of 99%. © 2024 ACM.

关键词： Lung cancer

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The effect of deep learning methods on deepfake audio detection for digital investigation

The effect of deep learning methods on deepfake audio detect...

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2022 International Conference on ENTERprise Information Systems, CENTERIS 2022 - International Conference on Project MANagement, ProjMAN 2022 and International Conference on Health and Social Care Information Systems and Technologies, HCist 2022

作者： McUba, Mvelo Singh, Avinash Ikuesan, Richard Adeyemi Venter, Hein Department of Computer Science University of Pretoria Pretoria South Africa Computing and Applied Technology College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates

Voice cloning methods have been used in a range of ways, from customized speech interfaces for marketing to video games. Current voice cloning systems are smart enough to learn speech characteristics from a few samples and produce perceptually unrecognizable speech. These systems pose new protection and privacy risks to voice-driven interfaces. Fake audio has been used for malicious purposes and is difficult to classify what is real and fake during a digital forensic investigation. This paper reviews the issue of deep-fake audio classification and evaluates the current methods of deep-fake audio detection for forensic investigation. Audio file features were extracted and visually presented using MFCC, Mel-spectrum, Chromagram, and spectrogram representations to further study the differences. Harnessing the different deep learning techniques from existing literature were compared using five iterative tests to determine the mean accuracy and the effects thereof. The results showed a Custom Architecture gave better results for the Chromagram, Spectrogram, and Me-Spectrum images and the VGG-16 architecture gave the best results for the MFCC image feature. This paper contributes to further assisting forensic investigators in differentiating between synthetic and real voices. © 2023 The Authors. Published by Elsevier B.V.

关键词： Cloning

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Posture Monitoring and Classification for Health Care of Bedridden Patients Using IoT and AI Algorithms 10

Posture Monitoring and Classification for Health Care of Bed...

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10th International Conference on Engineering and Emerging Technologies, ICEET 2024

作者： Cheriyan, Sunitha Sakthivel, Sudha Regula, Thirupathi Kiran Kumar, S.G. Riyami, Said Al College of Computing and Information Sciences University of Technology and Applied Sciences Department of It Muscat Oman

ISBN: (纸本)9798331532895

Posture monitoring has become a crucial area of research and application, particularly considering the increasing concerns about musculoskeletal disorders and their association with prolonged lying and poor ergonomic practices. To solve the crucial problem of bedridden patient care, this study investigates the categorization of lying postures using four different deep learning models: The models used are Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), VGG16, and ResNe$t$50. Due to bedridden patients' difficulty in developing pressure ulcers, respiratory disorders, and other complications, the classification of laying positions which includes supine, prone, and lateral is essential. Some possible assistive devices that can inform caregivers or initiate some techniques to improve the patient's comfort and eliminate adverse effects of immobility can benefit from the identification of some postures in real-time. The models were trained using both balanced and unbalanced data sets from a set of labeled images covering different laying postures. Their performance was then determined by criteria including accuracy, RMSE, Kappa, recall, and precision. As for the performance, the proposed VGG model achieved better classification accuracy compared to the CNN, RNN, and ResNet models. In light of enhancing patient care as well as monitoring within the health care facilities, it is suggested from our result that these models could suggest potential solutions to posture identification for bedridden patients. By illustrating how AI posture classification systems can be implemented in IoT-based applications, the study also shows how future healthcare delivery can be improved and the problems caused by long periods of inactivity can be prevented. © 2024 IEEE.

关键词： Convolutional neural networks

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