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Detecting and Mitigating Distributed Denial of Service Attacks in Software-Defined Networking

computers, Materials & Continua 2025年第6期83卷 4515-4535页

作者： Abdullah M.Alnajim Faisal Mohammed Alotaibi Sheroz Khan Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia College of Computer Engineering and Sciences Department of information systemPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Electrical Engineering College of Engineering and Information TechnologyOnaizah CollegesQassim56447Saudi Arabia

Distributed denial of service(DDoS)attacks are common network attacks that primarily target Internet of Things(IoT)*** are critical for emerging wireless services,especially for applications with limited *** attacks pose significant risks to entrepreneurial businesses,preventing legitimate customers from accessing their *** attacks require intelligent analytics before processing service *** denial of service(DDoS)attacks exploit vulnerabilities in IoT devices by launchingmulti-point distributed *** attacks generate massive traffic that overwhelms the victim’s network,disrupting normal *** consequences of distributed denial of service(DDoS)attacks are typically more severe in software-defined networks(SDNs)than in traditional *** centralised architecture of these networks can exacerbate existing vulnerabilities,as these weaknesses may not be effectively addressed in this *** preliminary objective for detecting and mitigating distributed denial of service(DDoS)attacks in software-defined networks(SDN)is to monitor traffic patterns and identify anomalies that indicate distributed denial of service(DDoS)*** implements measures to counter the effects ofDDoS attacks,and ensure network reliability and availability by leveraging the flexibility and programmability of SDN to adaptively respond to *** authors present a mechanism that leverages the OpenFlow and sFlow protocols to counter the threats posed by DDoS *** results indicate that the proposed model effectively mitigates the negative effects of DDoS attacks in an SDN environment.

关键词： Software-defined networking(SDN) distributed denial of service(DDoS)attack sampling Flow(sFlow) OpenFlow OpenDaylight controller

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Enhanced Demand-Side Management for Electric Vehicle Charging Stations Using Pyramidal Dilation Attention Neural Networks 6

Enhanced Demand-Side Management for Electric Vehicle Chargin...

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6th International Conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Venkateswaran, N. Ramprabu, J. Marimuthu, Ramaprabha Ramana, Nagella Venkata Kotha, Mahesh Mallala, Balasubbareddy Department of Master of Business Administration Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Tamil Nadu Coimbatore641049 India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Management Studies Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle517325 India CMR Technical Campus Telangana Hyderabad501401 India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India

ISBN: (纸本)9798331522667

With the improvement in EV’s popularity, controlling the load demand of EVCS’s has become an essential task to keep the grid balanced and efficient. Conventional DSM techniques tend to rely on historic patterns to predict charging trends, hence cannot serve as viable solutions to peak load problems. In order to improve charging demand forecasting, this research presents a Pyramidal Dilation Attention Convolutional Neural Network (PDACNN) for the DSM of EV charging stations. By using precise forecasting and ideal charging schedule, this study aims to increase the effectiveness of DSM in EV charging stations. The PDACNN is used to predict the charging load for EV stations, allowing for optimized scheduling of charging sessions that align with grid capacity and energy availability. By then, the proposed method is implemented on MATLAB platform and evaluated with other existing methods such as Artificial Neural Network (ANN), Deep Reinforcement Learning (DRL), and Genetic Algorithm (GA). The proposed PDACNN method achieves an RMSE of 0.0753 and an accuracy of 98.9%, demonstrating its effectiveness in EV charging load forecasting and demand-side management. © 2025 IEEE.

关键词： Charging stations

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A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 533-546页

作者： Hao Wu Xin Luo MengChu Zhou Muhyaddin J.Rawa Khaled Sedraoui Aiiad Albeshri Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714and also with the University of Chinese Academy of SciencesBeijing 100049China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA IEEE 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 College of Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Computer Science King Abdulaziz UniversityJeddah 21481Saudi Arabia

A large-scale dynamically weighted directed network(DWDN)involving numerous entities and massive dynamic interaction is an essential data source in many big-data-related applications,like in a terminal interaction pattern analysis system(TIPAS).It can be represented by a high-dimensional and incomplete(HDI)tensor whose entries are mostly *** such an HDI tensor contains a wealth knowledge regarding various desired patterns like potential links in a DWDN.A latent factorization-of-tensors(LFT)model proves to be highly efficient in extracting such knowledge from an HDI tensor,which is commonly achieved via a stochastic gradient descent(SGD)***,an SGD-based LFT model suffers from slow convergence that impairs its efficiency on large-scale *** address this issue,this work proposes a proportional-integralderivative(PID)-incorporated LFT *** constructs an adjusted instance error based on the PID control principle,and then substitutes it into an SGD solver to improve the convergence *** studies on two DWDNs generated by a real TIPAS show that compared with state-of-the-art models,the proposed model achieves significant efficiency gain as well as highly competitive prediction accuracy when handling the task of missing link prediction for a given DWDN.

关键词： Big data high dimensional and incomplete(HDI)tensor latent factorization-of-tensors(LFT) machine learning missing data optimization proportional-integral-derivative(PID)controller

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An effective communication and computation model based on a hybridgraph-deeplearning approach for SIoT

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Digital Communications and Networks 2022年第6期8卷 900-910页

作者： M.S.Mekala Gautam Srivastava Ju H.Park Ho-Youl Jung Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38544South Korea RLRC LAb for Autonomous Vehicle Parts and Materials Innovation Yeungnam UniversityGyeongsan38544South Korea Department of Math and Computer Science Brandon UniversityBrandonCanada Department of Electrical Engineering Yeungnam UniversityGyeongsanSouth Korea Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

Social Edge Service(SES)is an emerging mechanism in the Social Internet of Things(SIoT)orchestration for effective user-centric reliable communication and *** services are affected by active and/or passive attacks such as replay attacks,message tampering because of sharing the same spectrum,as well as inadequate trust measurement methods among intelligent devices(roadside units,mobile edge devices,servers)during computing and *** issues lead to computation and communication overhead of servers and computation *** address this issue,we propose the HybridgrAph-Deep-learning(HAD)approach in two stages for secure communication and ***,the Adaptive Trust Weight(ATW)model with relation-based feedback fusion analysis to estimate the fitness-priority of every node based on directed graph theory to detect malicious nodes and reduce computation and communication ***,a Quotient User-centric Coeval-Learning(QUCL)mechanism to formulate secure channel selection,and Nash equilibrium method for optimizing the communication to share data over edge *** simulation results confirm that our proposed approach has achieved effective communication and computation performance,and enhanced Social Edge Services(SES)reliability than state-of-the-art approaches.

关键词： Edge computing Adaptive trust weight(ATW)model Quotient user-centric coeval-learning(QUCL)mechanism Deep learning Service reliability

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Accumulative Time Based Ranking Method to Reputation Evaluation in Information Networks

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Journal of computer Science & Technology 2022年第4期37卷 960-974页

作者： Hao Liao Qi-Xin Liu Ze-Cheng Huang Ke-Zhong Lu Chi Ho Yeung Yi-Cheng Zhang National Engineering Laboratory on Big Data System Computing Technology College of Computer Science and SoftwareEngineeringShenzhen UniversityShenzhen 518060China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen UniversityShenzhen 518060China Guangdong Province Engineering Center of China-Made High Performance Data Computing System College of ComputerScience and Software EngineeringShenzhen UniversityShenzhen 518060China Institute of Big Data Intelligent Management and Decision Shenzhen UniversityShenzhen 518060China Department of Science and Environmental Studies The Education University of Hong KongHong Kong 999077China Department of Physics University of FribourgFribourg 1700Switzerland

Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

关键词： temporal network behavior dynamics reputation evaluation ranking algorithm

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The simplified and enlarged memory management with efficiency through cells 4

The simplified and enlarged memory management with efficienc...

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4th IEEE International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Radhakrishnan, Niranchana Alsalami, Zaid Al-Qaisy, Shams A. Chamoli, Sushant Hayder, Huda Ghobash, Ali Qusayjawad, Ahmed Alliance University Alliance College of Engineering and Design Department of Cse karnataka bangalore India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering najaf Iraq Al-farahidi University baghdad Iraq Graphic Era Hill University Department of Cse India Graphic Era Deemed to Be University uttarakhand dehradun248007 India National University of Science and Technology Abbas College of Pharmacy dhiqar64001 Iraq Alkunooze University College Computer Engineer Department basra Iraq Kut University College Department of Electrical Engineering Technologies wasit Iraq

ISBN: (纸本)9798350360165

Memory with multiple ports is widely employed in numerous domains, especially for parallel computations that require rapidity and efficiency. Conventional SRAM-based memory modules encounter constraints on performance and flexibility, including the need to handle a multitude of write inputs and read outputs. Standard cell-based memory, in contrast, provides greater parameterization and flexibility. However, current tools encounter difficulties in constructing these memories as a result of intricate circuitry and a lack of understanding regarding the regular structure of these arrays. This may occasionally lead to an inability to converge under specific circumstances. New techniques for the physical and logical implementation of many-ported standard cell memory (MPSCMs) are introduced in this study. It is suggested that the traditional design process be substituted with two discrete methodologies that offer direction and control to design tools in order to improve the performance, area utilisation, and power efficiency of these memory arrays. An assessment and evaluation process was undertaken on MPSCM macros of different dimensions utilising a commercial 65 nm CMOS technology. These macros were compared to their equivalents that were developed employing conventional and cutting-edge techniques. © 2024 IEEE.

关键词： Efficiency

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False Data Injection Cyberattacks Targeting Electric Vehicles in Smart Power Distribution Systems

False Data Injection Cyberattacks Targeting Electric Vehicle...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Ehsan Naderi Arash Asrari Poria Fajri Department of Electrical Engineering College of Engineering and Computer Science Arkansas State University Jonesboro AR USA Department of Electrical and Computer Engineering Purdue University Northwest Hammond IN USA Electrical and Biomedical Engineering Department University of Nevada Reno NV USA

ISBN: (数字)9798350317664

ISBN: (纸本)9798350317671

This paper presents a false data injection (FDI) attack model to target a selection of plugged-in electric vehicles (EVs) in a smart power distribution system resulting in a range of operational issues including but not limited to voltage collapse. To reduce the total cost and difficulty of the cyberattack, attacker utilizes a pre-attack analysis via generating PV and VQ curves for the buses of the distribution system in order to precisely recognize the weakest buses of the system (i.e., the most vulnerable ones) and also the required active and reactive power to be injected into the targeted buses to result in voltage collapse. The effectiveness of the proposed attack model is validated on an IEEE test distribution system modified to contain distributed generation (DG) and EV aggregators.

关键词： Substations Power distribution Transportation Voltage Charging stations Transformers Electric vehicle charging

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Building Personalised XAI Experiences Through iSee: a Case-Based Reasoning-Driven Platform 2

Building Personalised XAI Experiences Through iSee: a Case-B...

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Joint of the 2nd World Conference on eXplainable Artificial Intelligence Late-Breaking Work, Demos and Doctoral Consortium, xAI-2024:LB/D/DC

作者： Caro-Martínez, Marta Liret, Anne Díaz-Agudo, Belén Recio-García, Juan A. Darias, Jesús Wiratunga, Nirmalie Wijekoon, Anjana Martin, Kyle Nkisi-Orji, Ikechukwu Corsar, David Palihawadana, Chamath Pirie, Craig Bridge, Derek Pradeep, Preeja Fleisch, Bruno Department of Software Engineering and Artificial Intelligence Universidad Complutense de Madrid Spain School of Computing Robert Gordon University Aberdeen United Kingdom School of Computer Science & IT University College Cork Ireland British Telecommunications United Kingdom

Nowadays, eXplainable Artificial Intelligence (XAI) is well-known as an important field in computer Science due to the necessity of understanding the increasing complexity of Artificial Intelligence (AI) systems or algorithms. This is the reason why we can find a wide variety of explanation techniques (explainers) in the literature, on top of some XAI libraries. The challenge faced by XAI designers here is deciding what explainers are the most suitable for each scenario, taking into account the AI model, task to explain, user preferences, needs and knowledge, and overall, fitting into the explanation requirements. With the aim of addressing this problem, the iSee project was conceived to provide XAI design users with supporting tools to build their own explanation experiences. As a result, we have developed iSee, a Case-Based Reasoning-driven platform that allows users to create personalised explanation experiences. With the iSee platform, users add their explanation experience requirements, and get the most suitable XAI strategies to explain their own situation, taking advantage of XAI strategies previously used with success in similar context. The iSee platform is composed of different tools and modules: the ontology, the cockpit, the explainer library, the Explanation Experiences Editor (iSeeE3), the chatbot, and the analytics dashboard. This paper introduces these tools as a demo and tutorial for current and future users and for the XAI community. © 2024 Copyright for this paper by its authors.

关键词： Case based reasoning

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The Unique Way of Designing SNN Network for Content Recommending System using Hybrid Algorithms 4

The Unique Way of Designing SNN Network for Content Recommen...

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4th IEEE International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Yadav, Kanchan Sruthi, Pinamala Al-Jawahry, Hassan M. Kumar, Devender Gehlot, Anita Angadi, Sanjeev Kumar Gla University Department of Electrical Engineering Mathura India Cmr College of Engineering & Technology Department of Cse India The Islamic University Najaf Iraq Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Uttaranchal Institute of Technology Uttaranchal University Department of Electronics & Communication Engineering Dehradun248007 India Computer Science and Engineering Nutan College of Engineering and Research Pune Maharashtra Talegaon Dabhade India

ISBN: (纸本)9798350360165

In the rapidly evolving panorama of e-commerce, personalised apparel recommendation systems stand as a pinnacle of technological advancement, aiming to reflect the intricacies of human style feel and social interactions on-line. This paper offers a singular technique via using Siamese Neural Networks (SNN) to research and integrate style fashion consistency with user social circle data, thereby addressing the pervasive challenges of bloodless start and records sparsity in traditional collaborative filtering systems. Our methodology leverages huge statistics from a distinguished social fashion platform, demonstrating good sized upgrades in advice personalization and accuracy. This integration no longer handiest enhances the user experience by way of providing tailor-made fashion suggestions however additionally marks a great step forward within the application of neural networks in the realm of fashion e-commerce. © 2024 IEEE.

关键词： Collaborative filtering

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Optimizing Machine Learning Algorithms to Enhance Blood Donation for Liver Patients: A Case Study 7

Optimizing Machine Learning Algorithms to Enhance Blood Dona...

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7th IET Smart Cities Symposium, SCS 2023

作者： Mijwil, Maad M. El-Kenawy, El-Sayed M. Bala, Indu Aljanabi, Mohammad Abotaleb, Mostafa Ibrahim, Abdelhameed Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood. © The Institution of engineering & Technology 2023.

关键词： Support vector machines

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