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Classification of Adversarial Attacks Using Ensemble Clustering Approach

computers, Materials & Continua 2023年第2期74卷 2479-2498页

作者： Pongsakorn Tatongjai Tossapon Boongoen Natthakan Iam-On Nitin Naik Longzhi Yang Center of Excellence in AI&Emerging Technologies School of ITMae Fah Luang UniversityChiang RaiThailand Department of Computer Science Aberystwyth UniversityAberystwythUnited Kingdom School of Informatics and Digital Engineering Aston UniversityBirminghamUnited Kingdom Department of Computer and Information Sciences Northumbria UniversityNewcastleUnited Kingdom

As more business transactions and information services have been implemented via communication networks,both personal and organization assets encounter a higher risk of *** safeguard these,a perimeter defence likeNIDS(network-based intrusion detection system)can be effective for known *** has been a great deal of attention within the joint community of security and data science to improve machine-learning based NIDS such that it becomes more accurate for adversarial attacks,where obfuscation techniques are applied to disguise patterns of intrusive *** current research focuses on non-payload connections at the TCP(transmission control protocol)stack level that is applicable to different network *** contrary to the wrapper method introduced with the benchmark dataset,three new filter models are proposed to transform the feature space without knowledge of class *** ECT(ensemble clustering based transformation)techniques,i.e.,ECT-Subspace,ECT-Noise and ECT-Combined,are developed using the concept of ensemble clustering and three different ensemble generation strategies,i.e.,random feature subspace,feature noise injection and their *** on the empirical study with published dataset and four classification algorithms,new models usually outperform that original wrapper and other filter alternatives found in the *** is similarly summarized from the first experiment with basic classification of legitimate and direct attacks,and the second that focuses on recognizing obfuscated *** addition,analysis of algorithmic parameters,i.e.,ensemble size and level of noise,is provided as a guideline for a practical use.

关键词： Intrusion detection adversarial attack machine learning feature transformation ensemble clustering

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Context transfer and Q-Transferable tasks 29

Context transfer and Q-Transferable tasks

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29th AAAI Conference on Artificial Intelligence, AAAI 2015

作者： Mousavi, Amin Araabi, Babak Nadjar Ahmadabadi, Majid Nili Robotics and AI Lab Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Iran School of Cognitive Science Tehran Iran

ISBN: (纸本)9781577357209

This article discusses the notion of context transfer in reinforcement learning tasks. Context transfer, as defined in this article, implies knowledge transfer between tasks that share the same environment's dynamics and reward function, but have different state and action spaces. For example, we have a working mobile robot in an environment. At some point, we decide to upgrade its sensors and/or actuators. Any change in these modules will result in a different description of the agent- environment model, and the trained knowledge is no longer applicable. We consider the tasks of the old and new robots, as the source and target tasks, respectively. The Markov decision process (MDP) of these tasks, under certain conditions, are called Q-transferable tasks, and the problem of knowledge transfer between them is called context transfer. We investigate the relation of the MDPs of these tasks. Copyright © 2015, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Markov processes

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Design of Provably Secure Authentication Protocol for Edge-Centric Maritime Transportation System

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IEEE Transactions on Intelligent Transportation Systems 2023年第12期24卷 14536-14545页

作者： Mahmood, Khalid Shamshad, Salman Ayub, Muhammad Faizan Ghaffar, Zahid Khan, Muhammad Khurram Das, Ashok Kumar National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610054 China National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan King Saud University Center of Excellence in Information Assurance Riyadh11653 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

The epidemic growth of the Internet of Things (IoT) objects have revolutionized Maritime Transportation Systems (MTS). Though, it becomes challenging for the centralized cloud-centric framework to fulfil the application requirements such as low latency and power utilization. The introduction of the distributed edge-centric framework has recently helped the IoT-enabled MTS to meet these requirements by manipulating the tasks at the edge of the networks. Despite the fact that MTS leverages mobile subscribers by overcoming inherent cloud computing limitations, data security and user privacy requirements in establishing the MTS setup are still non-trivial challenges. In this article, we develop a key agreement solution for mobile users to realize mutual authentication in a single round. Our protocol offers user anonymity to maintain user privacy, and it can prevent physical attacks by physically unclonable functions. Initially, the security analysis is conferred to substantiate our protocol's security persistence or strength. Later, its performance correlation is observed under the assumption of diverse metrics in a predefined empirical setup. The meticulous performance correlation endorses the precedence of our protocol over specified related protocols. © 2000-2011 IEEE.

关键词： Hash functions

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A Constant Factor Approximation Algorithm for Reordering Buffer Management 13

A Constant Factor Approximation Algorithm for Reordering Buf...

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Annual ACM-Society for Industrial and Applied Mathmatics Symposium on Discrete algorithms

作者： Noa Avigdor-Elgrabli Yuval Rabani Computer Science Department Technion-Israel Institute of Technology The Rachel and Selim Benin School of Computer Science and Engineering and the Center of Excellence on Algorithms The Hebrew University of Jerusalem

ISBN: (纸本)9781611972511

In the reordering buffer management problem (RBM) a sequence of n colored items enters a buffer with limited capacity k. When the buffer is full, one item is removed to the output sequence, making room for the next input item. This step is repeated until the input sequence is exhausted and the buffer is empty. The objective is to find a sequence of removals that minimizes the total number of color changes in the output sequence. The problem formalizes numerous applications in computer and production systems, and is known to be NP-hard. We give the first constant factor approximation guarantee for RBM. Our algorithm is based on an intricate "rounding" of the solution to an LP relaxation for RBM, so it also establishes a constant upper bound on the integrality gap of this relaxation. Our results improve upon the best previous bound of O({the square root of}log k) of Adamaszek et al. (STOC 2011) that used different methods and gave an online algorithm. Our constant factor approximation beats the super-constant lower bounds on the competitive ratio given by Adamaszek et al. This is the first demonstration of a polynomial time of-fine algorithm for RBM that is provably better than any online algorithm.

关键词： Approximation Algorithm Reordering Buffer Management first demonstration Approximation algorithms buffers Input sequence Constants Sequences

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A method to generate context information sets from analysis results with a unified abstraction model based on an extension of data enrichment scheme

A method to generate context information sets from analysis ...

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作者： Park, Yoosang Mun, Jonghyeok Choi, Jongsun Choi, Jaeyoung Kim, Hoda School of Computer Science and Engineering Soongsil University Seoul Korea Republic of National Program of Excellence in Software center Chosun University Gwangju Korea Republic of

Context-awareness techniques generally deal with steps for processing information that can be gathered from perspectives of objects in real-world scenes in accordance with resolving heterogeneous devices and providing meaningful context information in recent computing domains such as Internet of Things, cloud computing, edge computing, and big data analysis. However, analysis applications are necessary to be improved in a way of cooperative approaches because they are independently executed far away from context-aware systems. This means that the stages are applied to process the data or the derived results, are isolated. That leads difficulties of management and integration for other software modules. As a result, a computing paradigm called edge computing has been suggested to compromise such isolations. For processing context information, it is necessary to establish knowledge parts with validation processes against the isolations. In this paper, we suggest a method for processing datasets in a view of building knowledge parts. The suggested method uses an extensible data enrichment scheme, which represents the relations for the object. In the experiment section, applications of the proposed method are shown in one of the healthcare community centers, and the data abstraction with the analysis jobs can be processed for each real-world object. © 2020 John Wiley & Sons, Ltd.

关键词： Knowledge representation

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A Deep Learning Framework for Enhancing Recommender Systems With Dual-Feedback Integration

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Applied Computational Intelligence and Soft Computing 2025年第1期2025卷

作者： V. Lakshmi Chetana Hari Seetha Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Amaravati Campus Kuragallu Village Mangalagiri (M) Guntur Andhra Pradesh India amrita.edu Center of Excellence AI and Robotics VIT-AP University Near Vijayawada Amaravati Andhra Pradesh India vitap.ac.in

Collaborative filtering (CF)-based personalized recommendation systems are among the most widely adopted strategies for addressing information overload in modern markets. However, a significant challenge that hinders the effectiveness of these systems is data sparsity. To address this limitation, advanced methodologies such as matrix factorization and deep learning models have been developed to improve CF system performance in sparse data scenarios. Despite their advancements, most existing models rely solely on either implicit or explicit user–item interactions to generate recommendations. Traditional CF approaches often struggle with sparsity, necessitating the development of hybrid methods that integrate both explicit and implicit feedback to enhance performance. To address this gap, this study introduces DeepBlendRec, an innovative deep learning-based framework that leverages both explicit user ratings and implicit user behaviour. By utilizing this dual-input methodology, the model captures richer information regarding user–item interactions, thereby significantly improving recommendation accuracy. DeepBlendRec employs an enhanced autoencoder with a constrained decoder to process implicit ratings, thereby improving reconstruction quality and facilitating the creation of robust latent space representations. Simultaneously, explicit ratings are processed through a multilayer perceptron. The reconstructed outputs are then fused to generate a Top-N recommendation list. Experimental evaluations conducted on the MovieLens datasets demonstrate that DeepBlendRec consistently outperforms existing models across several key performance metrics, including mean-squared error (MSE), root-mean-squared error (RMSE), mean absolute error (MAE), precision, recall, and F1-score. These results highlight the potential of DeepBlendRec to advance the capabilities of recommendation systems in handling data sparsity and improving predictive accuracy.

关键词： autoencoder collaborative filtering deep learning explicit feedback hybrid feedback implicit feedback MLP

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QoS Prediction Model of Cloud Services Based on Deep Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 564-566页

作者： WenJun Huang PeiYun Zhang YuTong Chen MengChu Zhou Yusuf Al-Turki Abdullah Abusorrah Nanjing University of Information Science&Technology School of Computer and SoftwareNanjingChina IEEE New Jersey Institute of Technology Helen and John C.Hartmann Department of Electrical and Computer EngineeringNewarkUSA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Dear editor,This letter presents a deep learning-based prediction model for the quality-of-service(QoS)of cloud ***,to improve the QoS prediction accuracy of cloud services,a new QoS prediction model is proposed,which is based on multi-staged multi-metric feature fusion with individual *** multi-metric features include global,local,and individual *** results show that the proposed model can provide more accurate QoS prediction results of cloud services than several state-of-the-art methods.

关键词： services Services individual

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A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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Time-stepping finite-element analysis of dynamic eccentricity fault in a three-phase salient pole synchronous generator

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Progress In Electromagnetics Research B 2010年第20期20卷 263-284页

作者： Faiz, J. Babaei, M. Nazarzadeh, J. Ebrahimi, B.M. Amini, S. Science and Research Branch Islamic Azad University Tehran Iran Shahed University Tehran Iran Center of Excellence on Applied Electromagnetic Systems School of Electrical and Computer Engineering University of Tehran Tehran Iran Niroo Research Institute Tehran Iran

In this paper, two-dimensional time-stepping -nite-element (TSFE) method is performed for modeling and analyzing of a salient pole synchronous generator with different degree of dynamic eccentricity (DE) fault. TSFE analysis is used to describe the influence of DE fault on the flux distribution within the generator and no-load voltage pro-les at low and high -eld current is obtained for healthy and faulty cases. Comparing the magnetic flux distribution of healthy and faulty generators helps to detect the influence of DE fault. Also, it can be seen at no-load condition with low excitation current, the effect of the eccentricity is considerable compared to that of the rated excitation current. Since the calculation of inductances of the machine is the most important step for fault analysis and diagnosis, the self- and mutual- inductances of the stator phases and rotor windings are calculated in the eccentric generator. Double periodic phenomenon is observedin inductances profile of stator phases due to the DE fault. Finally, spectrum analysis of stator current of two generators with different design parameters is used to diagnosis the significant harmonics in the presence of DE fault.

关键词： Inductance

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ChartEye: A Deep Learning Framework for Chart Information Extraction

ChartEye: A Deep Learning Framework for Chart Information Ex...

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Mustafa, Osama Ali, Muhammad Khizer Moetesum, Momina Siddiqi, Imran Bahria University Center of Excellence in Ai Islamabad Pakistan School of Electrical Engineering and Computer Science Islamabad Pakistan Xynoptik Pty Limited MelbourneVIC Australia

ISBN: (纸本)9798350382204

The widespread use of charts and infographics as a means of data visualization in various domains has inspired recent research in automated chart understanding. However, information extraction from chart images is a complex multi-tasked process due to style variations and, as a consequence, it is challenging to design an end-to-end system. In this study, we propose a deep learning-based framework that provides a solution for key steps in the chart information extraction pipeline. The proposed framework utilizes hierarchal vision transformers for the tasks of chart-type and text-role classification, while YOLOv7 for text detection. The detected text is then enhanced using Super Resolution Generative Adversarial Networks to improve the recognition output of the OCR. Experimental results on a benchmark dataset show that our proposed framework achieves excellent performance at every stage with F1-scores of 0.97 for chart-type classification, 0.91 for text-role classification, and a mean Average Precision of 0.95 for text detection. © 2023 IEEE.

关键词： Classification (of information)

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