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Communications, Computing, Cybersecurity, and Informatics (CCCI), International Conference on

作者： Pragya Bhatt Mohammad S. Obaidat Gagan Dangwal Ashok Kumar Das Mohammad Wazid Balqies Sadoun Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India KASIT University of Jordan Amman Jordan University of Science and Technology Beijing Beijing China SRM University Kattankulathur India Amity University Noida India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India College of Engineering Al-Balqa’ Applied University Al-Salt Jordan

ISBN: (数字)9798350349832

ISBN: (纸本)9798350349849

Phishing attacks pose a serious threat to online users by impersonatina trustworthy entities and stealing sensitive data. This can result in reputational damage, financial loss, ransomware, or additional spyware outbreaks. Phishing scams take into consideration roughly 22% of all data violations, making it one of the most prominent cybercrimes, in accordance with the FBI's 2021 IC3 report. According to phishing email statistics, roughly 1.2% of all emails received are fraudulent, resulting in 3.4 billion phishing emails per day. One out of every 4,200 emails sent is almost certainly a phishing attempt. Cognitive methods, such as machine learning (ML) and deep learning (DL), are widely used in cybersecurity to extract information and detect possible intrusions and phishing attempts. This research investigates the procedure of employing machine learning methods for recognizing URLs that are used for phishing attacks. We have used the Pristine and Malicious URLs Dataset to train, test, and evaluate the performance of a set of algorithms to develop a robust binary class URL phishing attack detection mechanism. The obtained results show that the proposed mechanism outperforms the other existing competing mechanisms with 99.6% accuracy.

关键词： Uniform resource locators Training Accuracy Machine learning algorithms Phishing Electronic mail Classification algorithms Telecommunication computing computer crime Random forests

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Group Movable Antenna With Flexible Sparsity: Joint Array Position and Sparsity Optimization

arXiv

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arXiv 2024年

作者： Lu, Haiquan Zeng, Yong Jin, Shi Zhang, Rui The National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China The Purple Mountain Laboratories Nanjing211111 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Movable antenna (MA) is a promising technology to exploit the spatial variation of wireless channel for performance enhancement, by dynamically varying the antenna position within a certain region. However, for multi-antenna communication systems, moving each antenna independently not only requires prohibitive complexity to find the optimal antenna positions, but also incurs sophisticated movement control in practice. To address this issue, this letter proposes a new MA architecture termed group MA (GMA), enabling the group movement of all elements collectively in a continuous manner, and simultaneously achieving flexible array architecture by antenna selection (AS). In this letter, we focus on the uniform sparse array based GMA, where equally spaced antenna elements are selected to achieve desired array sparsity. The array position and sparsity level are jointly optimized to maximize the sum rate of the multi-user communication system. Numerical results verify the necessity to optimize the position and sparsity of GMA, and considerable performance gain is achieved as compared to the conventional fixed-position antenna (FPA). Copyright © 2024, The Authors. All rights reserved.

关键词： Antenna arrays

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Movable Antenna for Wireless Communications: Prototyping and Experimental Results

arXiv

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arXiv 2024年

作者： Dong, Zhenjun Zhou, Zhiwen Xiao, Zhiqiang Zhang, Chaoyue Li, Xinrui Min, Hongqi Zeng, Yong Jin, Shi Zhang, Rui The National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China The Purple Mountain Laboratories Nanjing211111 China The School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Movable antenna (MA), which can flexibly change the position of antenna in three-dimensional (3D) continuous space, is an emerging technology for achieving full spatial performance gains. In this paper, a prototype of MA communication system with ultra-accurate movement control is presented to verify the performance gain of MA in practical environments. The prototype utilizes the feedback control to ensure that each power measurement is performed after the MA moves to a designated position. The system operates at 3.5 GHz or 27.5 GHz, where the MA moves along a one-dimensional horizontal line with a step size of 0.01λ and in a two-dimensional square region with a step size of 0.05λ, respectively, with λ denoting the signal wavelength. The scenario with mixed line-of-sight (LoS) and non-LoS (NLoS) links is considered. Extensive experimental results are obtained with the designed prototype and compared with the simulation results, which validate the great potential of MA technology in improving wireless communication performance. For example, the maximum variation of measured power reaches over 40 dB and 23 dB at 3.5 GHz and 27.5 GHz, respectively, thanks to the flexible antenna movement. In addition, experimental results indicate that the power gain of MA system relies on the estimated path state information (PSI), including the number of paths, their delays, elevation and azimuth angles of arrival (AoAs), as well as the power ratio of each path. Copyright © 2024, The Authors. All rights reserved.

关键词： Feedback control

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Hybrid Data-Free Knowledge Distillation 39

Hybrid Data-Free Knowledge Distillation

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Tang, Jialiang Chen, Shuo Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Center for Advanced Intelligence Project RIKEN Japan Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)157735897X

Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. Existing collection-based and generation-based methods train student networks by collecting massive real examples and generating synthetic examples, respectively. However, they inevitably become weak in practical scenarios due to the difficulties in gathering or emulating sufficient real-world data. To solve this problem, we propose a novel method called Hybrid Data-Free Distillation (HiDFD), which leverages only a small amount of collected data as well as generates sufficient examples for training student networks. Our HiDFD comprises two primary modules, i.e., the teacher-guided generation and student distillation. The teacher-guided generation module guides a Generative Adversarial Network (GAN) by the teacher network to produce high-quality synthetic examples from very few real-world collected examples. Specifically, we design a feature integration mechanism to prevent the GAN from overfitting and facilitate the reliable representation learning from the teacher network. Meanwhile, we drive a category frequency smoothing technique via the teacher network to balance the generative training of each category. In the student distillation module, we explore a data inflation strategy to properly utilize a blend of real and synthetic data to train the student network via a classifier-sharing-based feature alignment technique. Intensive experiments across multiple benchmarks demonstrate that our HiDFD can achieve state-of-the-art performance using 120 times less collected data than existing methods. Copyright © 2025, Association for the Advancement of Artificia Intelligence (***). All rights reserved.

关键词： Personnel training

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Generative Federated Learning with Small and Large Models In Consumer Electronics for Privacy preserving Data Fusion in Healthcare Internet of Things

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Ghazal, Taher M. Islam, Shayla Hasan, Mohammad Kamrul Abu-Shareha, Ahmad A. Mokhtar, Umi A. Khan, M. Attique Baili, Jamel Saeed, Ali Q. Bhattt, Mohammed Wasim Ahmad, Munir Department of Networks and Cybersecurity Hourani Center for Applied Scientific Research Amman Jordan UCSI University Institute of Computer Science and Digital Innovation Kuala Lumpur56000 Malaysia Center for Cyber Security Faculty of Information Science and Technology Malaysia Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Department of Data Science and Artificial Intelligence Amman Jordan Prince Mohammad bin Fahd University Saudi Arabia King Khalid University College of Computer Science Department of Computer Engineering Abha61413 Saudi Arabia Northern Technical University Computer Center Ninevah Iraq Korea University College of Informatics Seoul02841 Korea Republic of

Healthcare Internet of Things (HIoT) requires large-scale privacy features to ensure maximum security in sharing sensitive physiological data in consumer electronics. Recent approaches utilize the fusion concept to provide maximum privacy in health data sharing. Embedded signing data fusion with the health observed data ensures privacy preserved sharing across heterogeneous medical consumer devices for diagnosis. This article proposes a Dependency-correlated Data Fusion Scheme (DcDFS) to maximize the privacy of the health data-sharing process. The proposed scheme prepares separate key signing procedures using triple-DES (data encryption standard) to embed with the accumulated health data. The fusion process is carried out by defining key headers and integrity footers for authentication and verification. Therefore, the fusion generates a combined sequence of linear authentication and validation procedures enclosing the health data. In this scheme, the role of federated learning is to prevent permuted sequences for the same health data. This research integrates Small Language Model (SLM) and Large Language Model (LLM) into the federated learning module to support secure, scalable, and intelligent healthcare data sharing. Their collaboration enhances context-aware training while preserving privacy across decentralized, encrypted environments. A similar sequence mapped between the header and footer is responsible for discarding unauthorized data handling. The learning process verifies the permutation for many-to-one header to footer and vice versa. Therefore, the proposed fusion scheme generates a linear dependency between the actual and security-related data for maximum privacy. The proposed scheme achieves the following: the computation time is confined by 12.424%, the privacy leakage by 12.923%, and the computation efficiency is improved by 11.46%, as observed under the maximum sequences. © 1975-2011 IEEE.

关键词： Sensitive data

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Incompressibility of H-free edge modification problems: Towards a dichotomy 28

Incompressibility of H-free edge modification problems: Towa...

引用

28th Annual European Symposium on Algorithms, ESA 2020

作者： Marx, Dániel Sandeep, R.B. CISPA Helmholtz Center for Information Security Saarbrücken Germany Department of Computer Science and Engineering Indian Institute of Technology Dharwad India

ISBN: (纸本)9783959771627

Given a graph G and an integer k, the H-free Edge Editing problem is to find whether there exist at most k pairs of vertices in G such that changing the adjacency of the pairs in G results in a graph without any induced copy of H. The existence of polynomial kernels for H-free Edge Editing (that is, whether it is possible to reduce the size of the instance to kO(1) in polynomial time) received significant attention in the parameterized complexity literature. Nontrivial polynomial kernels are known to exist for some graphs H with at most 4 vertices (e.g., path on 3 or 4 vertices, diamond, paw), but starting from 5 vertices, polynomial kernels are known only if H is either complete or empty. This suggests the conjecture that there is no other H with at least 5 vertices were H-free Edge Editing admits a polynomial kernel. Towards this goal, we obtain a set H of nine 5-vertex graphs such that if for every H ∈ H, H-free Edge Editing is incompressible and the complexity assumption NP 6 coNP/poly holds, then H-free Edge Editing is incompressible for every graph H with at least five vertices that is neither complete nor empty. That is, proving incompressibility for these nine graphs would give a complete classification of the kernelization complexity of H-free Edge Editing for every H with at least 5 vertices. We obtain similar result also for H-free Edge Deletion. Here the picture is more complicated due to the existence of another infinite family of graphs H where the problem is trivial (graphs with exactly one edge). We obtain a larger set H of nineteen graphs whose incompressibility would give a complete classification of the kernelization complexity of H-free Edge Deletion for every graph H with at least 5 vertices. Analogous results follow also for the H-free Edge Completion problem by simple complementation. © Dániel Marx and R. B. Sandeep.

关键词： Graphic methods

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Sustainable and intelligent time-series models for epidemic disease forecasting and analysis

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Sustainable Technology and Entrepreneurship 2024年第2期3卷

作者： Chhabra, Anureet Singh, Sunil K. Sharma, Akash Kumar, Sudhakar Gupta, Brij B. Arya, Varsha Chui, Kwok Tai Department of Computer Science Chandigarh College of Engineering and Technology Chandigarh 160019 India International Center for AI and Cyber Security Research and Innovations & Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India Department of Electrical and Computer Engineering Lebanese American University Beirut 1102 Lebanon School of Computing Skyline University College United Arab Emirates Department of Business Administration Asia University Taiwan Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Hong Kong Metropolitan University (HKMU) 695014 Hong Kong

There is an increasing risk of outbreaks escalating into epidemics, despite huge advances in medical science. Epidemics like COVID-19, Monkeypox, Influenza and HIV have been affecting people and public health infrastructure at an alarming rate around the world. COVID-19 alone has infected more than 500 million people out of which 6 million have died over 100 countries. HIV is also a major global public health issue and has claimed 85.6 million lives till 2023. Forecasting the trends of these epidemics is important in order to efficiently manage national and global health risks by improving early warning systems. Therefore an intelligent framework to forecast epidemic diseases is proposed and a detailed comparative analysis is conducted using different time-series models. This study contributes to (Sustainable Development Goal) SDG-3 by predicting epidemics disease trends precisely using ARIMA, Polynomial Regression, SARIMA, Holt's, Fb-Prophet time-series models, which can decrease the burden on healthcare systems. Using the best-suited models, the Mean Absolute Percentage Error (MAPE) values for Monkeypox, HIV, COVID-19 and Influenza forecasting were 0.0129, 0.0035, 0.0011, and 0.024 respectively. © 2023 The Author(s)

关键词： ARIMA COVID-19 Epidemic disease Fb-prophet Holt's model Influenza MonkeyPox SDG-3 Time-series forecasting

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Comparing SWIPT Techniques for Zero-Energy RIS

Comparing SWIPT Techniques for Zero-Energy RIS

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IEEE International Conference on Communications Workshops, ICC

作者： Dimitrios Tyrovolas Vasilis K. Papanikolaou Sotiris A. Tegos Yue Xiao Panagiotis D. Diamantoulakis Sotiris Ioannidis Christos Liaskos George K. Karagiannidis Dept. of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Dept. of Electrical and Computer Engineering Technical University of Chania Chania Greece School of Information Science and Technology Southwest Jiaotong University Chengdu China Computer Science Engineering Department University of Ioannina Ioannina and Foundation for Research and Technology Hellas (FORTH) Greece Cyber Security Systems and Applied AI Research Center Lebanese American University (LAU) Lebanon

Providing enhanced quality of service (QoS) to support demanding applications in the most energy-efficient way has been identified as a primary objective of future 6G networks. Towards this direction, the concept of programmable wireless environments (PWEs) has emerged, wherein extremely high QoS can be ensured by controlling the wireless propagation through reconfigurable intelligent surfaces (RIS). To meet the demands of 6G networks in terms of energy efficiency, in this paper, zero-energy RISs (ZERISs) are introduced, which harvest the required amount of energy for their operation and then contribute to the PWE conversion. More specifically, we investigate the performance of a ZERIS-assisted communication system in terms of joint energy-data rate outage probability, which quantifies the trade-off between the harvested energy and information transmission, for three distinct simultaneous wireless information and power transfer (SWIPT) methods are investigated, namely power splitting, time switching, and element splitting. Finally, simulation results validate the derived analysis, leading to useful insights for the ZERIS-assisted network regarding which energy harvesting method achieves better performance in terms of joint energy-data rate outage probability.

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Privacy-based framework for Cyber Resilience of Healthcare based data for use with Machine Learning algorithms

Privacy-based framework for Cyber Resilience of Healthcare b...

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2022 International Conference on Cyber Resilience, ICCR 2022

作者： Sapra, Varun Hasan, Mohammad Kamrul Ghazal, Taher M. Bhadrdwaj, Akashdeep Bharany, Salil Ahmad, Munir Rehman, Ateeq Ur Mohamed, Tamer School of Computer Science University of Petroleum and Energy Studies Dehradun India Center for Cyber Security Faculty of Information Science and Technology Selangor Bangi43600 Malaysia Skyline University College University School of Information Technology City Sharjah1797 United Arab Emirates University of Petroleum and Energy Studies Dehradun India Gndu Cet Department Punjab Amritsar India National College of Business Administration and Economics Lahore Pakistan Gc University Department of Electrical Engineering Lahore Pakistan Canadian University Dubai Dubai United Arab Emirates

ISBN: (纸本)9781665461221

Cyber resilience is the business capability of handling the risks and preparing themselves for responding and recovering from risks. Being a cyber resilient the organization is capable of handling unknown or less known threats and ready to face such adversities and challenges. Healthcare related datasets using Machine learning or ML-based systems for detection of diseases such as Streptococcus pharyngitis will be expected to operate in contested and adversarial environments. Every operation these datasets support depends on their capacity to adjust to threats. To minimize the risk of misdiagnosing and early diagnosis of the disease an intelligent ML method are required. ML has gained a significant success in almost all the domains and has proved its ability in healthcare sector also. This research presents comparison of different ML algorithms to detect Pharyngitis. The study revealed that with reduced feature set Random Forest performs best with 70.11% accuracy and outshined all other implemented techniques. The authors propose a new privacy framework to protect the patient health care data. © 2022 IEEE.

关键词： Machine learning

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Systematic Review: Predictive Models for the Winning Team of Super Leagues (SL)

Systematic Review: Predictive Models for the Winning Team of...

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Business Analytics for Technology and security (ICBATS), International Conference on

作者： Salman Muneer Muhammad Bux Alvi Mahmoud Al Sakhnani Hamamd Raza Taher M. Ghazal Munir Ahmad School of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Systems Engineering The Islamia University of Bahawalpur Bahawalpur Pakistan Skyline University College School of Business University City Sharjah Sharjah UAE School of Computer Science National College of Business Administration & Economics Lahore Pakistan Faculty of Information Science and Technology Center for Cyber Security Universiti Kebangsaan Malaysia (UKM) Bangi Malaysia

The Pakistan Super League (PSL) is one of the most popular cricket leagues in the world, attracting millions of fans and spectators each year. With the 2023 edition of the league just around the corner, the use of predictive models to determine the winning team has gained much attention. This systematic review analyzed the existing literature on predictive models for determining the winning team of PSL 2023. The study examined multiple approaches, including machine learning, statistical modeling, and expert opinion. The results of this review suggest that machine learning models, particularly ensemble models, are the most accurate predictors of PSL match outcomes. These models typically use a combination of features, such as team rankings, player performance statistics, match-winning ratio, and previous match outcomes, to predict the result of a match. This paper discovered that expert opinion-based models could provide valuable insights into the likely winner of a match. These models typically use input from cricket experts and analysts to predict the outcome of a match.

关键词： Ranking (statistics) Analytical models Fans Systematics Machine learning Predictive models History

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