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Penetration Testing Overview-Opportunities and Ethical Considerations: Literature Notes

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Penetration Testing Overview-Opportunities and Ethical Consi...

Jordanian Cybersecurity Conference (IJCC), International

作者： Mahmoud Asassfeh Ghassan Samara Ali Abu Zaid Dena Abu Laila Shouq Al-Anzi Abdullah Alqammaz Ahmad Al Smadi Ala'a Al-Shaikh Mohammad Rasmi Al-Mousa Department of Cybersecurity Zarqa University Zarqa Jordan Department of Computer Science Zarqa University Zarqa Jordan Department of Cyber Security Faculty of Information Technology Zarqa Technical Intermediate College Zarqa Jordan Department of Data Science and AI Zarqa University Zarqa Jordan

ISBN: (数字)9798331518462

ISBN: (纸本)9798331518479

The paper relates to how Large Language Models and penetration testing practices interlink, providing a critical outlook on the potential of LLMs, along with their ethical ramifications. It performs an overview of the state-of-art in LLM-driven penetration testing through the analysis of three different studies. The first introduces PENTESTGPT, an LLM specifically designed for the purpose of penetration testing. This is indeed an LLM that proves to be quite efficient in performing tasks and giving logical reasoning. This study, however, also underlines several challenges on the narrow scope and generalizability of this model and questions its greater applicability. The second involves research into LLMs as AI sparring partners for the simulation of real-world cyberattacks that target vulnerabilities in systems. In the interest of finding flaws in security, the study exposes some strong ethical concerns-particularly on adversarial uses of AI in that direction. This third study shall explore the role of penetration testing in cloud security frameworks and help indicate how such practice is indispensable in maintaining security in cloud infrastructures. The paper further outlines the shared responsibility that exists in the maintenance of security between the cloud service providers and users besides highlighting what LLMs can do in this area. Aggregately, these studies help to outline the transformative power of LLMs in improving pen-testing. Yet, they also at the same time require great attention to ethical considerations and further research to make sure that deployment of LLMs really is responsible within the evolving cybersecurity landscape.

关键词： Ethics Cloud computing security Large language models Cognition Maintenance computer crime Standards Penetration testing

Multiple Teachers-Meticulous Student: A Domain Adaptive Meta-Knowledge Distillation Model for Medical Image Classification

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arXiv 2024年

作者： Nabavi, Shahabedin Hamedani, Kian Anvari Moghaddam, Mohsen Ebrahimi Abin, Ahmad Ali Frangi, Alejandro F. Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran Division of Informatics Imaging and Data Sciences Schools of Computer Science and Health Sciences The University of Manchester Manchester United Kingdom Electrical Engineering and Cardiovascular Sciences Departments KU Leuven Leuven Belgium Alan Turing Institute London United Kingdom

Background: Image classification can be considered one of the key pillars of medical image analysis. Deep learning (DL) faces challenges that prevent its practical applications despite the remarkable improvement in medical image classification. The data distribution differences can lead to a drop in the efficiency of DL, known as the domain shift problem. Besides, requiring bulk annotated data for model training, the large size of models, and the privacy-preserving of patients are other challenges of using DL in medical image classification. This study presents a strategy that can address the mentioned issues simultaneously. Method: The proposed domain adaptive model based on knowledge distillation can classify images by receiving limited annotated data of different distributions. The designed multiple teachers-meticulous student model trains a student network that tries to solve the challenges by receiving the parameters of several teacher networks. The proposed model was evaluated using six available datasets of different distributions by defining the respiratory motion artefact detection task. Results: The results of extensive experiments using several datasets show the superiority of the proposed model in addressing the domain shift problem and lack of access to bulk annotated data. Besides, the privacy preservation of patients by receiving only the teacher network parameters instead of the original data and consolidating the knowledge of several DL models into a model with almost similar performance are other advantages of the proposed model. Conclusions: The proposed model can pave the way for practical clinical applications of deep classification methods by achieving the mentioned objectives simultaneously. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

Quantitative asymptotic regularity of the VAM iteration with error terms for m-accretive operators in Banach spaces

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arXiv 2024年

作者： Firmino, Paulo Leuştean, Laurentiu Departamento de Matemática Faculdade de Ciências Universidade de Lisboa Portugal LOS Faculty of Mathematics and Computer Science University of Bucharest Romania Simion Stoilow Institute of Mathematics the Romanian Academy Romania Institute for Logic and Data Science Bucharest Romania

In this paper we obtain, by using proof mining methods, quantitative results on the asymptotic regularity of the viscosity approximation method (VAM) with error terms for m-accretive operators in Banach spaces. For concrete instances of the parameter sequences, linear rates are computed by applying a lemma due to Sabach and *** Codes 47H05, 47H09, 47J25, 03F10 © 2024, CC BY.

关键词： Banach spaces

Joint Energy Harvesting, Semantic Transmission Selection, Channel Allocation and Power Control for Resource-Constrained IoT Networks

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Joint Energy Harvesting, Semantic Transmission Selection, Ch...

IEEE Conference on Vehicular Technology (VTC)

作者： Nguyen Huu Sang Nguyen Duc Hai Nguyen Duc Duy Anh Nguyen Cong Luong Shimin Gong Dusit Niyato Faculty of Computer Science PHENIKAA University Hanoi Vietnam School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China College of Computing and Data Sciene Nanyang Technological University Singapore

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

In this work, we propose the use of energy harvesting and semantic communication for Internet of Things (IoT) systems. The system allows IoT devices to harvest energy from a base station and then uses the harvested energy for extracting and transmitting semantic information, i.e., scene graphs, to the base station. The proposed network thus copes with the energy and network resource constraints of the IoT devices. To maximize the total image data or scene graph transmitted to the base station, we formulate a problem that optimizes the energy harvesting duration, the selection of original image or portions of scene graphs, transmit power, and channel allocation to the IoT devices. Under the high dynamics and uncertainty of the context and size of the collected images as well as the wireless channels and computing resources, we propose two advanced deep reinforcement learning (DRL) algorithms, i.e., advantage Actor-Critic (A2C) and proximal policy optimization (PPO), to solve the problem. Simulation results are implemented on the real dataset clearly showing that the performance achieved by the proposed algorithms is much higher than that achieved by the baseline scheme. This implies that more original images or triplets are transmitted.

关键词： Wireless communication Base stations Uncertainty Heuristic algorithms Simulation Semantics Channel allocation

Statistical Distance-Guided Unsupervised Domain Adaptation for Automated Multi-Class Cardiovascular Magnetic Resonance Image Quality Assessment

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arXiv 2024年

This study proposes an attention-based statistical distance-guided unsupervised domain adaptation model for multi-class cardiovascular magnetic resonance (CMR) image quality assessment. The proposed model consists of a feature extractor, a label predictor and a statistical distance estimator. An annotated dataset as the source set and an unlabeled dataset as the target set with different statistical distributions are considered inputs. The statistical distance estimator approximates the Wasserstein distance between the extracted feature vectors from the source and target data in a mini-batch. The label predictor predicts data labels of source data and uses a combinational loss function for training, which includes cross entropy and centre loss functions plus the estimated value of the distance estimator. Four datasets, including imaging and k-space data, were used to evaluate the proposed model in identifying four common CMR imaging artefacts: respiratory and cardiac motions, Gibbs ringing and Aliasing. The results of the extensive experiments showed that the proposed model, both in image and k-space analysis, has an acceptable performance in covering the domain shift between the source and target sets. The model explainability evaluations and the ablation studies confirmed the proper functioning and effectiveness of all the model's modules. The proposed model outperformed the previous studies regarding performance and the number of examined artefacts. The proposed model can be used for CMR post-imaging quality control or in large-scale cohort studies for image and k-space quality assessment due to the appropriate performance in domain shift coverage without a tedious data-labelling process. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Using data Mining Techniques to Analyze Facial Expression Motion Vectors 6th

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Using Data Mining Techniques to Analyze Facial Expression Mo...

6th International Conference on Dynamics of Information Systems, DIS 2023

作者： Roshanzamir, Mohamad Alizadehsani, Roohallah Roshanzamir, Mahdi Shoeibi, Afshin Gorriz, Juan M. Khosravi, Abbas Nahavandi, Saeid Acharya, U. Rajendra Department of Computer Engineering Faculty of Engineering Fasa University Fasa74617-81189 Iran Deakin University GeelongVIC Australia Department of Electrical and Computer Engineering University of Tabriz Tabriz Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain Department of Signal Theory Networking and Communications Universidad de Granada Granada Spain Swinburne University of Technology HawthornVIC3122 Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

ISBN: (纸本)9783031503191

Automatic recognition of facial expressions is a common problem in human-computer interaction. While humans can recognize facial expressions very easily, machines cannot do it as easily as humans. Analyzing facial changes during facial expressions is one of the methods used for this purpose by the machines. In this research, facial deformation caused by facial expressions is considered for automatic facial expression recognition by machines. To achieve this goal, the motion vectors of facial deformations are captured during facial expression using an optical flow algorithm. These motion vectors are then used to analyze facial expressions using some data mining algorithms. This analysis not only determined how changes in the face occur during facial expressions but can also be used for facial expression recognition. The facial expressions investigated in this research are happiness, sadness, surprise, fear, anger, and disgust. According to our research, these facial expressions were classified into 12 classes of facial motion vectors. We applied our proposed analysis mechanism to the extended Cohen-Kanade facial expression dataset. Our developed automatic facial expression system achieved 95.3%, 92.8%, and 90.2% accuracy using Deep Learning (DL), Support Vector Machine (SVM), and C5.0 classifiers, respectively. In addition, based on this research, it was determined which parts of the face have a greater impact on facial expression recognition. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

TREE-WASSERSTEIN DISTANCE FOR HIGH DIMENSIONAL data WITH A LATENT FEATURE HIERARCHY

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arXiv 2024年

作者： Lin, Ya-Wei Eileen Coifman, Ronald R. Mishne, Gal Talmon, Ronen Viterbi Faculty of Electrical and Computer Engineering Technion United States Department of Mathematics Yale University United States Halicioǧlu Data Science Institute University of California San Diego United States

Finding meaningful distances between high-dimensional data samples is an important scientific task. To this end, we propose a new tree-Wasserstein distance (TWD) for high-dimensional data with two key aspects. First, our TWD is specifically designed for data with a latent feature hierarchy, i.e., the features lie in a hierarchical space, in contrast to the usual focus on embedding samples in hyperbolic space. Second, while the conventional use of TWD is to speed up the computation of the Wasserstein distance, we use its inherent tree as a means to learn the latent feature hierarchy. The key idea of our method is to embed the features into a multi-scale hyperbolic space using diffusion geometry and then present a new tree decoding method by establishing analogies between the hyperbolic embedding and trees. We show that our TWD computed based on data observations provably recovers the TWD defined with the latent feature hierarchy and that its computation is efficient and scalable. We showcase the usefulness of the proposed TWD in applications to word-document and single-cell RNA-sequencing datasets, demonstrating its advantages over existing TWDs and methods based on pre-trained models. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

A new approach to dealing with interval data in the TOPSIS method 26

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A new approach to dealing with interval data in the TOPSIS m...

26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Kaczynska, Aleksandra Gandotra, Neeraj Salabun, Wojciech Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland Yogananda School of AI Computers and Data Science Shoolini University HP Solan173229 India

Dealing with uncertainty is a significant direction of contemporary research on decision support systems. Despite many sophisticated methods of dealing with uncertain data, such as fuzzy sets and their numerous generalizations, methods based on interval numbers are still very popular. This is because interval data are much less complex and easier to process, and they are also easier to determine. However, there is a research gap regarding whether such a simple approach can adequately model uncertainty. This paper proposes a new approach to handling interval data in the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method based on the cartesian product of boundaries. This approach is compared with the most popular extension proposed by Jahanshahloo. In this work, two study cases taken from the literature were analyzed. Finally, the results of both approaches were compared, and conclusions were drawn. © 2022 The Authors. Published by Elsevier B.V.

关键词： Decision making

Unleashing the Potential of Decentralized Federated Learning in Healthcare: A Comprehensive Survey on Insights and Challenges

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Unleashing the Potential of Decentralized Federated Learning...

IEEE International Conference on data science in Cyberspace (DSC)

作者： Haibo Cheng Wenjian Liu Youyang Qu Kexue Fu Qinyuan Wang Faculty of Data Science City University of Macau Macau China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Federated learning (FL) is a distributed learning method that reduces data transmission and privacy risks. However, the existence of a central server may become a target of attackers, resulting in an increased risk of user privacy leakage. The emergence of Decentralized Federated Learning (DFL) is posited to enhance data privacy and system reliability by reducing reliance on central servers and enhancing local data control, especially in the medical field, where personal health data is protected. This research investigates the application of (DFL) in the medical domain, focusing on its limitations. This study employs a systematic literature review, adhering to PRISMA guidelines, to comprehensively understand DFL’s current landscape and challenges in healthcare. The key findings highlight four principal obstacles faced by DFL: security and privacy, communication efficiency, data and model heterogeneity, and incentive mechanisms. It also explores the potential for DFL to facilitate personalized healthcare treatments by large datasets distributed across various medical institutions. This research fills a significant void by systematically reviewing DFL technologies in healthcare. It provides insights into applications, challenges, and future directions for participants seeking secure and efficient healthcare data handling.

关键词： Surveys data privacy Systematics Federated learning Medical services data models Servers Security Reliability Systematic literature review