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2023 IEEE International Conference on Communications, computing, Cybersecurity and Informatics, CCCI 2023

作者： Kumar, Pankaj Obaidat, Mohammad S. Pandey, Piyush Wazid, Mohammad Das, Ashok Kumar Singh, Devesh Pratap Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India King Abdullah Ii School of Information Technology The University of Jordan Amman11942 Jordan School of Computing Srm University Jordan University of Science and Technology Beijing Beijing100083 China School of Computing Srm University TN Kattankulathur603203 India Algorithmic Research International Institute of Information Technology Center for Security Theory Hyderabad India

ISBN: (纸本)9798350302561

Today's world is rapidly growing towards the advanced concept of technology. technology nowadays is at its peak of development and has great advantages in all fields of work, whether it is in industrial automation, farming, weather forecasting, or disease detection and control. Despite its great advantages, it does have its negative side because a major population is using technology and general-purpose computing devices (i.e., desktops, laptops, smartphones, tablets, etc.). Therefore, most of their personal data is available online. The data is constantly in danger of getting stolen by malicious actors (i.e., hackers), who can find a way to get their hands on this data and then use it for their personal gain (i.e., money-making). The best-known technique to get this information is by using malware exploitation on the machine of the target. Malware is an executable or a binary that is malicious in nature. It may have various devastating effects, and in order to detect and make the system defensive, we need to propose a scheme to detect the malware in the system, analyze the particular file, and then mitigate it. In this paper, we propose a secure machine learning-based malware detection and analysis scheme (in short, SML-MDA). The conducted security analysis and formal security verification of the proposed SMLMDA prove its security against various types of potential attacks. The proposed SML-MDA is then compared with the other similar competing schemes, and its performance has been found to be better in terms of various performance metrics like accuracy and F1-score. © 2023 IEEE.

关键词： Machine learning

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Energy-Aware Compression and Consumption Algorithms for Efficient TinyML Model Using Aquila Optimization in Industrial IoT

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 334-342页

作者： Thuppari, Chaitanya Jannu, Srikanth Edla, Damodar Reddy Vidyarthi, Ankit Agarwal, Krishna Kant Alkhayyat, Ahmed Vaagdevi Engineering College Department of CSE Bollikunta Warangal506 005 India National Institute of Technology Department of CSE Goa 403 703 India Jaypee Institute of Information Technology Noida Department of CSE and IT Noida India Galgotias University School of Computing Science and Engineering Greater Noida India Islamic University College of Technical Engineering Najaf Iraq

With the proliferation of Industrial Internet of Things (IIoT) applications, the demand for energy efficient compression and clustering algorithms has escalated along with reliable routing algorithms. This paper proposes a novel approach: an energy aware compression based cluster and routing algorithms leveraging efficient tiny machine learning (TinyML) model using Aquila optimization for enhanced performance in IIoT environments. This approach aims to mitigate the challenges of energy and quality aware compression and energy consumption often encountered in IIoTs within underground coal mines. By dynamically forming clusters of IoT devices based on their proximity and data compression, our approach distributes the data traffic evenly across the network, thus preventing congestion and prolonging network lifetime. The integration of Aquila optimization further refines the routing decisions, optimizing the network's overall performance. Through extensive simulations and comparative analysis, proposed method demonstrates superior performance metrics, including network lifetime by 62.3%, a 66% reduction in energy consumption and a 41% improvement in live nodes compared to existing methods. Additionally, our algorithms show 21% improvement in packet delivery ratio and increase network stability, making them particularly effective in challenging environments such as underground coal mines. This research contributes to the advancement of efficient routing techniques adapted for the demanding requirements of IIoT deployments, promising a more robust and sustainable IIoT. © 1975-2011 IEEE.

关键词： Clustering algorithms

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Investigating Naive Bayes Algorithms for Network Time Series Analysis 5th

Investigating Naive Bayes Algorithms for Network Time Series...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Nidhya, M.S. Verma, Sunny Mohamed, H. B. Asif Agarwal, Trapty Karnataka Bangalore India Department of Electronic & Communication Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Naive Bayes algorithms are a class of supervised gadget mastering models which are typically used for a variety of obligations, together with statistics mining, textual content category, and regression. This studies explores the ability of applying these algorithms to the evaluation of community time series statistics. Thru empirical experimentation, the effectiveness of diverse Naive Bayes algorithms is as compared the usage of empirical performance metrics. The results show that Naive Bayes algorithms can successfully be used to identify patterns in community time series, carry out regression responsibilities, and offer feature representations. The results spotlight the capability of Naive Bayes algorithms as a tool for the evaluation of network time collection statistics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Time series

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HOFD: An Outdated Fact Detector for Knowledge Bases

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IEEE Transactions on Knowledge and Data Engineering 2023年第10期35卷 10775-10789页

作者： Hao, Shuang Chai, Chengliang Li, Guoliang Tang, Nan Wang, Ning Yu, Xiang Beijing Jiaotong University School of Computer and Information Technology Beijing100044 China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing100044 China Beijing Institute of Technology School of Computer Science and Technology Beijing100811 China Beijing100190 China Qatar Computing Research Institute Hbku Doha34110 Qatar Huawei Company Gauss Department Beijing100031 China

Knowledge bases (KBs), which store high-quality information, are crucial for many applications, such as enhancing search results and serving as external sources for data cleaning. Not surprisingly, there exist outdated facts in most KBs due to the rapid change of information. Naturally, it is important to keep KBs up-to-date. Traditional wisdom has investigated the problem of using reference data (such as new facts extracted from the news) to detect outdated facts in KBs. However, existing approaches can only cover a small percentage of facts in KBs. In this paper, we propose HOFD, a novel human-in-the-loop approach for outdated fact detection in KBs. HOFD trains a binary classifier using features such as historical update frequency and update time of a fact to compute the likelihood of a fact in a KB to be outdated. Then, HOFD interacts with humans to verify whether a fact with high likelihood is indeed outdated. In addition, HOFD also uses logical rules to detect more outdated facts based on human feedback. The outdated facts detected by the logical rules will also be fed back to train the ML model further for data augmentation. Extensive experiments on real-world KBs, such as Yago and DBpedia, show the effectiveness of our solution. © 1989-2012 IEEE.

关键词： Knowledge based systems

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Multi-features fusion for short-term photovoltaic power prediction

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Intelligent and Converged Networks 2022年第4期3卷 311-324页

作者： Ming Ma Xiaorun Tang Qingquan Lv Jun Shen Baixue Zhu Jinqiang Wang Binbin Yong State Grid Gansu Electric Power Research Institute Lanzhou 730000China School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China School of Computing and Information Technology University of WollongongWollongong 2522Australia

In recent years,in order to achieve the goal of“carbon peaking and carbon neutralization”,many countries have focused on the development of clean energy,and the prediction of photovoltaic power generation has become a hot research ***,many traditional methods only use meteorological factors such as temperature and irradiance as the features of photovoltaic power generation,and they rarely consider the multi-features fusion methods for power *** paper first preprocesses abnormal data points and missing values in the data from 18 power stations in Northwest China,and then carries out correlation analysis to screen out 8 meteorological features as the most relevant to power ***,the historical generating power and 8 meteorological features are fused in different ways to construct three types of experimental ***,traditional time series prediction methods,such as Recurrent Neural Network(RNN),Convolution Neural Network(CNN)combined with eXtreme Gradient Boosting(XGBoost),are applied to study the impact of different feature fusion methods on power *** results show that the prediction accuracy of Long Short-Term Memory(LSTM),stacked Long Short-Term Memory(stacked LSTM),Bi-directional LSTM(Bi-LSTM),Temporal Convolutional Network(TCN),and XGBoost algorithms can be greatly improved by the method of integrating historical generation power and meteorological ***,the feature fusion based photovoltaic power prediction method proposed in this paper is of great significance to the development of the photovoltaic power generation industry.

关键词： meteorological factors multi-features fusion time series prediction photovoltaic power prediction

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Implicit Supervision-Assisted Graph Collaborative Filtering for Third-Party Library Recommendation

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IEEE Transactions on Services computing 2025年第3期18卷 1459-1471页

作者： Lianrong Chen Mingdong Tang Naidan Mei Fenfang Xie Guo Zhong Qiang He School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China Unicom (Guangdong) Industry Internet Company Guangzhou China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computing Technologies Swinburne University of Technology Melbourne VIC Australia

Third-party libraries (TPLs) play a crucial role in software development. Utilizing TPL recommender systems can aid software developers in promptly finding useful TPLs. A number of TPL recommendation approaches have been proposed and among them graph neural network (GNN)-based recommendation is attracting the most attention. However, GNN-based approaches generate node representations through multiple convolutional aggregations, which is prone to introducing noise, resulting in the over-smoothing issue. In addition, due to the high sparsity of labelled data, node representations may be biased in real-world scenarios. To address these issues, this paper presents a TPL recommendation method named Implicit Supervision-assisted Graph Collaborative Filtering (ISGCF). Specifically, it takes the App-TPL interaction relationships as input and employs a popularity-debiased method to generate denoised App and TPL graphs. This reduces the noise introduced during graph convolution and alleviates the over-smoothing issue. It also employs a novel implicitly-supervised loss function to exploit the labelled data to learn enhanced node representations. Extensive experiments on a large-scale real-world dataset demonstrate that ISGCF achieves a significant performance advantage over other state-of-the-art TPL recommendation methods in Recall, NDCG and MAP. The experiments also validate the superiority of ISGCF in mitigating the over-smoothing problem.

关键词： Vectors Noise Training Collaborative filtering Recommender systems Libraries Predictive models Graph neural networks Electronic mail Convolution

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Multi-Target Distributed Maximum Correntropy Kalman Filter 24

Multi-Target Distributed Maximum Correntropy Kalman Filter

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12th International Conference on Communications and Broadband Networking, ICCBN 2024

作者： Deng, Xingyu Han, Hongyu Zhang, Sheng Xu, Yin College of Computer Science and Visual Computing Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal University Chengdu610066 China The School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)9798400717109

Multi-target tracking in sensor networks is a challenging problem, especially in scenarios where sensor observations are limited. Conventional centralized Kalman filters and distributed Kalman filters (DKFs) require each sensor to observe all targets, which is often difficult to achieve in practical distributed applications. With this in mind, and inspired by the promising robustness of the maximum correntropy criterion in non-Gaussian noise environments, this paper proposes a novel multi-target distributed maximum correntropy Kalman filter, named MT-DMCKF. The proposed algorithm can operate effectively in distributed multi-target scenarios and provide accurate and consistent tracking performance even when the number of sensors mounted on targets is limited. Within a single period, the algorithm requires only one exchange of information among sensors, thus reducing communication overhead compared to existing distributed Kalman filters. Simulation experiments validate the effectiveness and robustness of the proposed MT-DMCKF. © 2024 ACM.

关键词： Wiener filtering

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Double Cancelable Hashing for Protecting Biometrics of Users in Crowd 18

Double Cancelable Hashing for Protecting Biometrics of Users...

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18th INDIAcom;11th International Conference on computing for Sustainable Global Development, INDIACom 2024

作者： Almutairi, Mishaal M. Apostolopoulou, Dimitra Stupples, David Sen, Adnan Ahmed Abi Yamin, Mohammad Halikias, George City University of London School of Science and Technology Dept. of Electrical and Electronic Engg London United Kingdom Islamic University Deanship of Information Technology Madinah Saudi Arabia The Australian National University School of Computing College of Engineering and Computer Science Canberra Australia University of Athens Department of Mathematics Greece

ISBN: (纸本)9789380544519

Smart gates are considered one of the most important methods for managing crowds. Often, methods of authentication and verification of users depend on the user's biometric fingerprints, such as hand or eye prints. Sending a picture of the fingerprint or eye to the server for verification is deemed unacceptable, especially in crowds, as it will significantly impact service performance in addition to imposing a heavy load on the network. Another, more serious challenge is related to the security and privacy of this data. If a user's fingerprint is stolen, it implies that all systems and services related to it, whether financial, medical, or otherwise, will be vulnerable to hacking. Unfortunately, the use of the traditional hash function is not sufficiently strong against a Replay Attack, and integration with blockchain requires substantial resources and suffers from delays that may impact the quality of sensitive services. In this paper, a Hash Function is proposed to enhance the level of privacy and security of biometric information. The hashing process will be doubled on one hand, and fog computing will be employed as an intermediary and additional protection on the other hand. Finally, the proposed technology will provide a solution to the problem of Relay Attack. The effectiveness and applicability of the proposed approach will be implemented and assessed. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Crowd management data privacy data security encryption hackers

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Developing Algorithms for Adaptive Network Coding and Its Impact on Wireless Network Performance 5th

Developing Algorithms for Adaptive Network Coding and Its Im...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Garg, Aanchal Poornima, Selvaraj Savita Nachappa, M.N. Department of Electronic & Communication Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Adaptive network coding (ANC) is a recent technique used in Wi-Fi networks that utilizes coded transmissions to boost community performance. The concept at the back of ANC is to find the ideal combination of coded packets, considering channel conditions and network topology. By adjusting encoding techniques in response to converting environmental situations, ANC helps to maximize throughput and reduce overhead and latency. This paper proposes a set of rules for ANC that considers the evolution of channel situations to alter its encoding strategy dynamically. The proposed set of rules works by using remarks from previously transmitted packets to determine whether modifications to the current transmission scheme want to be made, which will improve community performance. The algorithm's effectiveness is then tested through simulations wherein the network's overall performance is compared to widespread Wi-Fi structures. Outcomes show that the set of rules can achieve sizable upgrades in network performance… © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wireless local area networks (WLAN)

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Remaining Useful Life Prediction of Degraded Systems Based on Multiple Performance Indicators Constructed by Multiple Features

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IEEE Sensors Journal 2025年第11期25卷 19613-19631页

作者： Wu, Bin Shi, Hui Zeng, Jianchao Zhang, Xiaohong Wang, Zhijian Wang, Zuolu Taiyuan University of Science and Technology School of Electronic Information Engineering Taiyuan030024 China North University of China institute for Big Data and Visual Computing Taiyuan030051 China University of Huddersfield Centre for Efficiency and Performance Engineering HuddersfieldHD1 3DH United Kingdom

The health status assessment of industrial equipment is critical to fault prediction and health management technologies. To fully reflect system degradation and accurately predict the remaining useful life (RUL) of a system, the construction of multiple performance indicators is important. Thus, a new state-space model and RUL prediction method is proposed herein based on multiple indicators, considering the three correlation relationships among features and degradation indicators. First, according to the extracted and selected feature signals, various relationships such as the correlations among multiple features, multiple features and indicators, and multiple indicators are analyzed. Then an intuitive multi-indicator state-space model is established considering the three correlations and the identified degradation patterns. On this basis, three failure modes of the system are defined according to the multi-indicator complex correlation conditions, and the corresponding probability density function of the system RUL is calculated. Finally, the PHM2012 Challenge and XJTU-SY datasets are used as examples for experimental verification. Compared with other prediction methods, the proposed method can intuitively reflect the correlation between features and indicators, to solve the problem caused by the hidden random correlation. © 2001-2012 IEEE.

关键词： Prediction models

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