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IT and Industrial Technologies (ICIT), International Conference on

作者： Attia Shabbir Raja Hashim Ali Muhammad Zeeshan Shabbir Zain Ul Abideen Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Muhammad Imad Muhammad Abu Bakar Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Department of Electrical Engineering University of Engineering & Technology (UET) Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models.

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EdgeMatrix: A Resources Redefined Edge-Cloud System for Prioritized Services

arXiv

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arXiv 2022年

作者： Ren, Yuanming Shen, Shihao Ju, Yanli Wang, Xiaofei Wang, Wenyu Leung, Victor C.M. TANKLab College of Intelligence and Computing Tianjin University Tianjin China Shanghai Zhuichu Networking Technologies Co. Ltd. China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Dept. of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

The edge-cloud system has the potential to combine the advantages of heterogeneous devices and truly realize ubiquitous computing. However, for service providers to guarantee the Service-Level-Agreement (SLA) priorities, the complex networked environment brings inherent challenges such as multi-resource heterogeneity, resource competition, and networked system dynamics. In this paper, we design a framework for the edge-cloud system, namely EdgeMatrix, to maximize the throughput while guaranteeing various SLA priorities. First, EdgeMatrix introduces Networked Multi-agent Actor-Critic (NMAC) algorithm to redefine physical resources as logically isolated resource combinations, i.e., resource cells. Then, we use a clustering algorithm to group the cells with similar characteristics into various sets, i.e., resource channels, for different channels can offer different SLA guarantees. Besides, we design a multi-task mechanism to solve the problem of joint service orchestration and request dispatch (JSORD) among edge-cloud clusters, significantly reducing the runtime than traditional methods. To ensure stability, EdgeMatrix adopts a two-time-scale framework, i.e., coordinating resources and services at the large time scale and dispatching requests at the small time scale. The real trace-based experimental results verify that EdgeMatrix can improve system throughput in complex networked environments, reduce SLA violations, and significantly reduce the runtime than traditional methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Multi agent systems

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Efficient Models for Detecting Monkeypox using Skin Lesion Images

Efficient Models for Detecting Monkeypox using Skin Lesion I...

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International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Neelam Sunda Divya Sharma Sujith Battu Rewanth Nayak Banoth Rahul Monika Department of Computer Science Kanoria PG Mahila Mahavidyalaya Jaipur India Department of Computer Science Kanoria PG Mahila Mahavidhyalaya jaipur India Dept. of Software Engineering Delhi Technological University New Delhi India Department of Applied Physics Delhi Technological University New Delhi India Department of Computer Science Shaheed Rajguru College of Applied Sciences for Women University of Delhi

Monkeypox is predicted to be the next pandemic and is spreading over African countries like water under a mat. The study basically investigates three deep learning imaging models for the identification of monkeypox from skin lesion photos from the MSID dataset. The motto is to define a computer-based technique to identify monkeypox from just its image, making it cheaper and faster with accurate results. The study achieved the highest accuracy of 99.37% as compared to previous works related to Resnet 50. The limits and complexity of the procedures in real-world situations are also clearly addressed.

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Text-to-Image Diffusion Models can be Easily Backdoored through Multimodal Data Poisoning

arXiv

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arXiv 2023年

作者： Zhai, Shengfang Dong, Yinpeng Shen, Qingni Pu, Shi Fang, Yuejian Su, Hang School of Software and Microelectronics Peking University China Department of Computer Science & Technology Tsinghua University China ShengShu Beijing China School of Software and Microelectronics National Engineering Research Center for Software Engineering PKU-OCTA Laboratory for Blockchain and Privacy Computing Peking University Beijing100871 China Dept. of Comp. Sci. and Tech. Institute for AI Tsinghua-Bosch Joint ML Center THBI Lab BNRist Center Tsinghua University Beijing100084 China

With the help of conditioning mechanisms, the state-of-the-art diffusion models have achieved tremendous success in guided image generation, particularly in text-to-image synthesis. To gain a better understanding of the training process and potential risks of text-to-image synthesis, we perform a systematic investigation of backdoor attack on text-to-image diffusion models and propose BadT2I, a general multimodal backdoor attack framework that tampers with image synthesis in diverse semantic levels. Specifically, we perform backdoor attacks on three levels of the vision semantics: Pixel-Backdoor, Object-Backdoor and Style-Backdoor. By utilizing a regularization loss, our methods efficiently inject backdoors into a large-scale text-to-image diffusion model while preserving its utility with benign inputs. We conduct empirical experiments on Stable Diffusion, the widely-used text-to-image diffusion model, demonstrating that the large-scale diffusion model can be easily backdoored within a few fine-tuning steps. We conduct additional experiments to explore the impact of different types of textual triggers, as well as the backdoor persistence during further training, providing insights for the development of backdoor defense methods. Besides, our investigation may contribute to the copyright protection of text-to-image models in the future. Our Code: https://***/sf-zhai/BadT2I. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Network Intrusion Detection System Using Deep Learning Method with KDD Cup'99 Dataset

Network Intrusion Detection System Using Deep Learning Metho...

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IEEE International Symposium on Embedded Multicore Socs (MCSoC)

作者： Jesse Jeremiah Tanimu Mohamed Hamada Patience Robert Anand Mahendran Dept. Of Computer Science Bayero University Kano Kano Nigeria Software Engineering Lab. University of Aizu Aizu Japan Department of Computer Science Federal Polytechnic Bali Bali Nigeria Theoretical Computer Science Lab. Higher School of Economics Moscow Moscow Russia

ISBN: (纸本)9781665465007

This work is a deep sparse autoencoder network intrusion detection system which addresses the issue of interpretability of L2 regularization technique used in other works. The proposed model was trained using a mini-batch gradient descent technique, L1 regularization technique and ReLU activation function to arrive at a better performance. Results based on the KDDCUP'99 dataset show that our approach provides significant performance improvements over other deep sparse autoencoder Network Intrusion Detection Systems.

关键词： Deep learning Multicore processing Network intrusion detection

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Information Leakage from Optical Emanations

arXiv

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arXiv 2023年

作者： Loughry, Joe Umphress, David A. Lockheed Martin Space Systems Dept. 3740 Mail Stop X3741 P.O. Box 179 DenverCO80201 United States Auburn University Department of Computer Science and Software Engineering 215 Dunstan Hall AL36849 United States

A previously unknown form of compromising emanations has been discovered. LED status indicators on data communication equipment, under certain conditions, are shown to carry a modulated optical signal that is significantly correlated with information being processed by the device. Physical access is not required;the attacker gains access to all data going through the device, including plaintext in the case of data encryption systems. Experiments show that it is possible to intercept data under realistic conditions at a considerable distance. Many different sorts of devices, including modems and Internet Protocol routers, were found to be vulnerable. A taxonomy of compromising optical emanations is developed, and design changes are described that will successfully block this kind of "Optical Tempest" attack. Categories and Subject Descriptors: C.2.0 [computer Systems Organization]: computer-COMMUNICATION NETWORKS—General, Security and protection (e.g., firewalls);D.4.6 [software]: OPERATING SYSTEMS—Security and Protection, Invasive software (e.g., viruses, worms, Trojan horses);E.3 [Data]: DATA ENCRYPTION—Code breaking;K.6.5 [Computing Milieux]: MANAGEMENT OF COMPUTING AND INFORMATION SYSTEMS—Security and Protection, Unauthorized Access (e.g., hacking, phreaking) Copyright © 2023, The Authors. All rights reserved.

关键词： Light emitting diodes

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TIMBERT: Toponym Identifier For The Medical Domain Based on BERT 28

TIMBERT: Toponym Identifier For The Medical Domain Based on ...

引用

28th International Conference on Computational Linguistics, COLING 2020

作者： Davari, MohammadReza Kosseim, Leila Bui, Tien D. Dept. Computer Science and Software Engineering Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9781952148279

In this paper, we propose an approach to automate the process of place name detection in the medical domain to enable epidemiologists to better study and model the spread of viruses. We created a family of Toponym Identification Models based on BERT (TIMBERT), in order to learn in an end-to-end fashion the mapping from an input sentence to the associated sentence labeled with toponyms. When evaluated with the SemEval 2019 task 12 test set (Weissenbacher et al., 2019), our best TIMBERT model achieves an F1 score of 90.85%, a significant improvement compared to the state-of-the-art of 89.13% (Wang et al., 2019). © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Viruses

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Explaining Agent Behavior in Agent-Based Modeling Using Subgroup Discovery

SSRN

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SSRN 2023年

作者： Romero, Elena Carmona del Jesús, Cristóbal José Cordón, Oscar del Jesus, María José Damas, Sergio Chica, Manuel Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Dept. of Computer Science and Artificial Intelligence DECSAI University of Granada Granada18071 Spain Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Jaen Jaen23071 Spain Leicester School of Pharmacy De Montfort University LeicesterLEI 9BH United Kingdom Dept. of Computer Science University of Jaen Jaen23071 Spain Dept. of Software Engineering University of Granada Granada18071 Spain School of Electrical Engineering and Computing The University of Newcastle CallaghanNSW2308 Australia

Agent-based modeling (ABM) is a popular tool for simulating complex systems. In addition to the key performance indicators ABM provides as main outputs of the modeling process, it also generates valuable information about the agents' roadmap, but the large volume of generated data is difficult to analyze and understand. Explainable Artificial Intelligence (XAI) techniques can improve the transparency and interpretability of these models. Subgroup Discovery (SD) is a useful tool for XAI that can facilitate the analysis of the data with respect to a variable of interest. In this contribution, we propose a methodology for applying SD to ABM for extracting useful knowledge of agents behavior from the model output. We compare the performance of different SD algorithms and show the effectiveness of our methodology through a case study in the marketing area. We analyze the obtained set of rules from the point of view of a marketing expert. The results show that our methodology can provide a deeper understanding of consumer behavior and market dynamics, providing potential areas of opportunity for marketers. © 2023, The Authors. All rights reserved.

关键词： Consumer behavior

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State of Security Awareness in the Additive Manufacturing Industry: 2020 Survey 6

State of Security Awareness in the Additive Manufacturing In...

引用

6th International Conference on Additive Manufacturing, ICAM 2021

作者： Yampolskiy, Mark Bates, Paul Seifi, Mohsen Shamsaei, Nima Dept. of Computer Science and Software Engineering Auburn Cyber Research Center 3127B Shelby Center 345 W. Magnolia Ave. AuburnAL36849 United States ASTM International 1850 M St. NW Suite 1030 WashingtonDC20036 United States Dept. of Mechanical Engineering Auburn University 1418 Wiggins Hall 354 War Eagle Way AuburnAL36849 United States National Center for Additive Manufacturing Excellence Auburn University 311 W. Magnolia Ave. AuburnAL36849 United States

ISBN: (纸本)9780803177352

The security of additive manufacturing (AM) gets increased attention due to the growing proliferation and adoption of AM in a variety of applications and business models. However, there is a significant disconnect between the AM community focused on manufacturing and the AM security community focused on securing this highly computerized manufacturing technology. To bridge this gap, we surveyed the America Makes AM community, asking in total 11 AM security-related questions aiming to discover the existing concerns, posture, and expectations. The first set of questions aimed to discover how many of these organizations use AM, outsource AM, or provide AM as a service. Then we asked about biggest security concerns as well as about assessment of who the potential adversaries might be and their motivation for attack. We then proceeded with questions on any experienced security incidents, if any security risk assessment was conducted, and if the participants' organizations were partnering with external experts to secure AM. Last, we asked whether security measures are implemented at all and, if so, whether they fall under the general cybersecurity category. Out of 69 participants affiliated with commercial industry, agencies, and academia, 53 have completed the entire survey. This paper presents the results of this survey, as well as our assessment of the AM security posture. The answers are a mixture of what we could label as expected, shocking but not surprising, and completely unexpected. Assuming that the provided answers are somewhat representative of the current state of the AM industry, we conclude that the industry is not ready to prevent or detect AM-specific attacks that have been demonstrated in the research literature. Copyright © 2022 by ASTM International.

关键词： 3D printing

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A Survey on Explainable Artificial Intelligence for Cybersecurity

arXiv

引用

arXiv 2023年

作者： Rjoub, Gaith Bentahar, Jamal Wahab, Omar Abdel Mizouni, Rabeb Song, Alyssa Cohen, Robin Otrok, Hadi Mourad, Azzam King Hussein School of Computing Sciences Princess Sumaya University for Technology Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates Department of EECS Khalifa University Abu Dhabi United Arab Emirates David R. Cheriton School of Computer Science University of Waterloo Canada Cyber Security Systems and Applied AI Research Center Dept. of CSM Lebanese American University Lebanon Division of Science New York University Abu Dhabi United Arab Emirates

The "black-box" nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of cybersecurity, XAI has the potential to revolutionize the way we approach network and system security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of cybersecurity threats and issues in networks and digital systems. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research. © 2023, CC BY.

关键词： Cybersecurity

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