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Information Technology (ACIT)

作者： Hamza Alkofahi David Umphress Heba Alawneh Department of Software Engineering Jordan University of Science and Technology Irbid Jordan Department of Computer Science and Software Engineering Auburn University Auburn USA Department of Cybersecurity Al Hussein Technical University Amman Jordan

ISBN: (数字)9798350320244

ISBN: (纸本)9798350320251

Aggressive integration of validation checks into web framework software has altered the attack surface of web applications by reducing the opportunity for traditional injection flaws. The hacking community's reaction has shifted to a more subtle - and more challenging to detect - form of attacks, that of discovering and exploiting underlying application business logic. The lack of accurate business rules defining the final application product extends its logical vulnerability surface. We propose a novel black-box approach for discovering authorization business rules in web applications through users' dynamic behavior. Allowing applications that lack formal specifications to be better tested for logic vulnerabilities. Our approach discovers groups using agglomerative hierarchical clustering based on different profiling techniques that capture users' actions and privileges. We also automated the process of identifying the optimal number of roles. The results indicated high quality and stability in discovering business rules, even in smaller datasets.

关键词： Authorization Closed box Behavioral sciences Formal specifications Data mining Information technology computer crime

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Fine feature reconstruction in point clouds by adversarial domain translation

Fine feature reconstruction in point clouds by adversarial d...

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Graphics Interface 2020, GI 2020

作者： Raina, Prashant Popa, Tiberiu Mudur, Sudhir Department of Computer Science and Software Engineering Concordia University

Point cloud neighborhoods are unstructured and often lacking in fine details, particularly when the original surface is sparsely sampled. This has motivated the development of methods for reconstructing these fine geometric features before the point cloud is converted into a mesh, usually by some form of upsampling of the point cloud. We present a novel data-driven approach to reconstructing fine details of the underlying surfaces of point clouds at the local neighborhood level, along with normals and locations of edges. This is achieved by an innovative application of recent advances in domain translation using GANs. We "translate" local neighborhoods between two domains: point cloud neighborhoods and triangular mesh neighborhoods. This allows us to obtain some of the benefits of meshes at training time, while still dealing with point clouds at the time of evaluation. By resampling the translated neighborhood, we can obtain a denser point cloud equipped with normals that allows the underlying surface to be easily reconstructed as a mesh. Our reconstructed meshes preserve fine details of the original surface better than the state of the art in point cloud upsampling techniques, even at different input resolutions. In addition, the trained GAN can generalize to operate on low resolution point clouds even without being explicitly trained on low-resolution data. We also give an example demonstrating that the same domain translation approach we use for reconstructing local neighborhood geometry can also be used to estimate a scalar field at the newly generated points, thus reducing the need for expensive recomputation of the scalar field on the dense point cloud. © 2020 Canadian Information Processing Society. All rights reserved.

关键词： Machine learning

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Multiobjective Vehicle Routing Optimization with Time Windows: A Hybrid Approach Using Deep Reinforcement Learning and NSGA-II

arXiv

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arXiv 2024年

作者： Wu, Rixin Wang, Ran Hao, Jie Wu, Qiang Wang, Ping Niyato, Dusit The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China The Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing Jiangsu210038 China The Department of Electrical Engineering and Computer Science Lassonde School of Engineering York University M3J 1P3 Canada The School of Computer Science and Engineering Nanyang Technological University 639798 Singapore

This paper proposes a weight-aware deep reinforcement learning (WADRL) approach designed to address the multiobjective vehicle routing problem with time windows (MOVRPTW), aiming to use a single deep reinforcement learning (DRL) model to solve the entire multiobjective optimization problem. The Non-dominated sorting genetic algorithm-II (NSGA-II) method is then employed to optimize the outcomes produced by the WADRL, thereby mitigating the limitations of both approaches. Firstly, we design an MOVRPTW model to balance the minimization of travel cost and the maximization of customer satisfaction. Subsequently, we present a novel DRL framework that incorporates a transformer-based policy network. This network is composed of an encoder module, a weight embedding module where the weights of the objective functions are incorporated, and a decoder module. NSGA-II is then utilized to optimize the solutions generated by WADRL. Finally, extensive experimental results demonstrate that our method outperforms the existing and traditional methods. Due to the numerous constraints in VRPTW, generating initial solutions of the NSGA-II algorithm can be time-consuming. However, using solutions generated by the WADRL as initial solutions for NSGA-II significantly reduces the time required for generating initial solutions. Meanwhile, the NSGA-II algorithm can enhance the quality of solutions generated by WADRL, resulting in solutions with better scalability. Notably, the weight-aware strategy significantly reduces the training time of DRL while achieving better results, enabling a single DRL model to solve the entire multiobjective optimization problem. © 2024, CC BY.

关键词： Reinforcement learning

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Main Confidentiality Challenges in Network Security: A Literature Review

Main Confidentiality Challenges in Network Security: A Liter...

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Information Technology (ACIT)

作者： Mahmoud Asassfeh Mohammad Rasmi Al-Mousa Waleed Amer Adai Mohammad Almomani Ala’a Al-Shaikh Suha Afaneh Mohammed Nayef Alrefai Ali Elrashidi Ghassan Samara Department of Cybersecurity Zarqa University Zarqa Jordan Department of Computer Science Zarqa University Zarqa Jordan Department of Software Enginereeing Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Nowadays, a big challenge is how to protect information from unwanted accesses. It constitutes a big challenge when we have different ways to access data. That is, a lot of information today travels through networks and our challenge today is to guarantee that traveled information should be secret that is not divulged by intruders. There are many different ways to assure that information being transmitted through a network is secret. In this paper, after we have shown in Section 2 a brief description of the security problems inside a local network, the most well-known network security models are presented. This paper provides a review of the confidentiality in network security. Confidentiality is a property that aims to ensure that only authorized users can access the required information and data while unauthorized users and third parties cannot learn it. Confidentiality components encompass the use of access controls, encryption with cryptographic techniques, and secure data communications. Confidentiality has encountered many threats and security risks during its use and deployment such as eavesdropping, network traffic analysis, passive attacks, deception and impersonation attacks, insecure user authentication, and weak password vulnerabilities. Therefore, strategies and measures should be applied and used to enhance the confidentiality and thus secure the communication of data across the internet and other means.

关键词： Authentication Telecommunication traffic Passwords Impersonation attacks Network security Encryption Data communication Information technology Systematic literature review Eavesdropping

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Flying sensor network optimization using bee intelligence for internet of things

Flying sensor network optimization using bee intelligence fo...

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Intelligent Systems Conference, IntelliSys 2020

作者： Salam, Abdu Javaid, Qaisar Ali, Gohar Ahmad, Fahad Ahmad, Masood Wahid, Ishtiaq Department of Computer Science and Software Engineering International Islamic University Islamabad Islamabad Pakistan Department of Information System and Technology Sur University Sur Oman Department of Computer Science Abdul Wali Khan University Mardan Mardan Pakistan

ISBN: (纸本)9783030551896

Flynig sensor network (FSN) for internet of things (IoT) consist of flying nodes called sensors and ground segments. The flying nodes may be operated manually or it may be automated devices. The flying segment of IoT has different nature compared to ordinary mobile sensor network. The flying speed and diverse directions of nodes make it harder to route the sensor information in a desired way. The data may be collected on the basis of contract opportunities. Here the timely delivery may not be guaranteed. To ensure the desired operation of the FSN, the delivery of data to the base station either deployed in the air or on the ground segment must be ensured in an efficient manner. In this paper, the mating intelligence of bees is used to ensure the delivery of data. The energy consumption is reduced by reducing the amount of control messages and transmitting redundant information. The network lifetime is increased. Simulation is conducted to evaluate the performance of the proposed scheme. The simulation results show that the proposed scheme outperforms existing schemes under consideration. © Springer Nature Switzerland AG 2021.

关键词： Internet of things

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Hierarchical Continual Learning for Domain-Knowledge Retention in Healthcare Federated Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Choudhry, Imran Arshad Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia University of Central Punjab Faculty of Information Technology and Computer Science Department of Software Engineering Lahore Pakistan

Internet of Medical Things (IoMT) applications encounter issues with data protection, continual adaptation, and domain-specific knowledge retention, especially in consumer-centric IoMT scenarios. We overcome these obstacles and facilitate effective knowledge retention and task adaptation in IoMT applications. This study attempts to create a unique privacy-preserving federated learning framework that combines a hierarchical learning structure with Continual Learning (CL). Despite the advancements in Federated Learning (FL), current models have trouble integrating changing datasets in real-time while protecting privacy, as well as catastrophic forgetting, which occurs when previously learned knowledge is lost when adjusting to new tasks. We present a hierarchical learning framework that makes use of three levels of models -Junior Model (JM), Consultant Model (CM), and Senior Consultant Model (SCM) -to overcome these drawbacks. Each level of the model aids in archived retention and domain-knowledge adaptation. To guarantee that the model maintains valuable information over time and adapts to new tasks with ease, our method blends domain adaptation strategies with ongoing learning approaches like knowledge distillation and elastic weight consolidation (EWC). We compare the suggested methodology with current state-of-the-art (SOTA) models on healthcare datasets for tasks like illness diagnosis and medical image categorization. According to our findings, the hierarchical continual learning model performs better than SOTA techniques in terms of accuracy, task adaptability, and privacy protection. In the healthcare industry, our study sets a new standard for privacy-preserving, continuously adaptable federated learning systems, allowing for real-time, scalable IoMT applications that can adapt dynamically to a variety of changing datasets. © 1975-2011 IEEE.

关键词： Patient treatment

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Personalized Federated Learning with Enhanced Implicit Generalization

Personalized Federated Learning with Enhanced Implicit Gener...

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International Joint Conference on Neural Networks (IJCNN)

作者： Heping Liu Songbai Liu Junkai Ji Qiuzhen Lin Jianyong Chen Kay Chen Tan College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Integrating personalization into federated learning is crucial for addressing data heterogeneity and surpassing the limitations of a single aggregated model. Personalized federated learning excels at capturing inter-client similarities and meeting diverse client needs through custom-made models. However, even with personalized approaches, it’s essential to aggregate knowledge among clients to ensure universal benefits. This paper proposes Federated Dual Objectives and Dual Models (FedDodm), a novel approach that employs two independent models to separately address explicit personalization and implicit generalization objectives in personalized federated learning. By treating these objectives as distinct loss functions and training models accordingly, we achieve a balance between the two through a fusion method. Extensive experiments across various models and learning tasks demonstrate that FedDodm outperforms state-of-the-art federated learning approaches, marking a significant advancement in effectively integrating personalized and generalized knowledge.

关键词： Training Federated learning Aggregates Neural networks Data models

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Inference Attacks on Genomic Data Based on Probabilistic Graphical Models

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Big Data Mining and Analytics 2020年第3期3卷 225-233页

作者： Zaobo He Junxiu Zhou Department of Computer Science and Software Engineering Miami UniversityOxfordOH 45011USA Department of Computer Science Northern Kentucky UniversityHighland HeightsKY 41099USA

The rapid progress and plummeting costs of human-genome sequencing enable the availability of large amount of personal biomedical information,leading to one of the most important concerns—genomic data *** personal biomedical data are highly correlated with relatives,with the increasing availability of genomes and personal traits online(i.e.,leakage unwittingly,or after their releasing intentionally to genetic service platforms),kin-genomic data privacy is *** propose new inference attacks to predict unknown Single Nucleotide Polymorphisms(SNPs)and human traits of individuals in a familial genomic dataset based on probabilistic graphical models and belief *** this method,the adversary can predict the unobserved genomes or traits of targeted individuals in a family genomic dataset where some individuals’genomes and traits are observed,relying on SNP-trait association from Genome-Wide Association Study(GWAS),Mendel’s Laws,and statistical relations between *** genome inferences have relatively high computational complexity with the input of tens of millions of SNPs and human ***,we propose an approach to publish genomic data with differential privacy *** finding an approximate distribution of the input genomic dataset relying on Bayesian networks,a noisy distribution is obtained after injecting noise into the approximate ***,synthetic genomic dataset is sampled and it is proved that any query on synthetic dataset satisfies differential privacy guarantee.

关键词： Single Nucleotide Polymorphism(SNP)-trait association belief propagation factor graph data sanitization

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Millimeter-Wave MIMO Array with Low Interactions Between its Antenna Elements for Fifth Generation Wireless Communication Networks

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Journal of Infrared, Millimeter, and Terahertz Waves 2025年第7期46卷 1-22页

作者： Alibakhshikenari, Mohammad Ali, Esraa Mousa din, Iftikhar ud Virdee, Bal S. Ullah, Sadiq Khan, Salahuddin See, Chan Hwang Saber, Takfarinas Limiti, Ernesto Department of Electronics Engineering University of Rome “Tor Vergata” Rome Italy Department of Electrical and Electronics Engineering Dogus University Istanbul Türkiye Communications and Computer Engineering Department Al-Ahliyya Amman University Amman Jordan Telecommunication Engineering Department University of Engineering and Technology Mardan Pakistan Center for Communications Technology London Metropolitan University London UK Department of Electrical Engineering University of Hafr Al Batin Hafr Al Batin Saudi Arabia School of Engineering and the Built Environment Edinburgh Napier University Edinburgh U.K. Lero the Research Ireland Centre for Software School of Computer Science University of Galway Galway Ireland

This paper presents a novel, compact eight-port circular MIMO antenna system designed for millimeter-wave (mmWave) 5G communication, offering a wide impedance bandwidth and high isolation. The proposed antenna array operates over a broad measured frequency range of 25–35 GHz, achieving a 10 GHz bandwidth through a unique integration of circular and rectangular slots in the ground plane. Fabricated on a low-loss Rogers RT5880 substrate (εr = 2.2, thickness = 0.8 mm, tanδ = 0.0009), the design demonstrates excellent performance without requiring additional decoupling structures. The antenna achieves high isolation greater than 28 dB and a peak gain of 9.65 dB at 26 GHz and 28 GHz, enabling effective operation in high-attenuation mmWave environments. Compared to prior art, the presented system supports more antenna elements within a compact footprint while maintaining low Envelope Correlation Coefficient (ECC < 0.05) and high diversity gain, making it ideal for enhanced MIMO performance. Comprehensive analysis of S-parameters, radiation patterns, surface currents, and efficiency validates its suitability for next-generation 5G mmWave applications. The combination of compact geometry, high port count, wideband coverage, and exceptional isolation constitutes the core novelty of this work.

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Internet of Robotic Things: Current Technologies and applications

arXiv

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arXiv 2022年

作者： Samara, Ghassan Hussein, Abla Matarneh, Israa Abdullah Alrefai, Mohammed Al-Safarini, Maram Y. Computer Science Department Zarqa University Zarqa Jordan Software Engineering Department Zarqa University Zarqa Jordan

The Internet of Robotic Things (IoRT) is a new domain that aims to link the IoT environment with robotic systems and technologies. IoRT connects robotic systems, connects them to the cloud, and transfers critical information as well as knowledge exchange to conduct complicated and intricate activities that a human cannot readily perform. The pertinent notion of IoRT has been discussed in this paper, along with the issues that this area faces on a daily basis. Furthermore, technological applications have been examined in order to provide a better understanding of IoRT and its current development phenomenon. The study describes three layers of IoRT infrastructure: network and control, physical, and service and application layer. In the next section, IoRT problems have been presented, with a focus on data processing and the security and safety of IoRT technological systems. In addition to discussing the difficulties, appropriate solutions have been offered and recommended. IoRT is regarded as an essential technology with the ability to bring about a plethora of benefits in smart society upon adoption, contributing to the generation and development of smart cities and industries in the near future. © 2022, CC BY.

关键词： Internet of things

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