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Resource investment for DDoS attack resistant SDN: a practical assessment

science China(Information sciences) 2023年第7期66卷 112-129页

作者： Bin YUAN Fan ZHANG Jun WAN Huan ZHAO Shui YU Deqing ZOU Qiangsheng HUA Hai JIN School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science University of Technology Sydney

Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.

关键词： SDN security DDoS attacks resource management queueing theory QoS

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PECMS: modeling a personalized energy and comfort management system based on residents’ behavior anticipation in smart home

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Journal of Reliable Intelligent Environments 2024年第2期10卷 123-136页

作者： Raeiszadeh, Mahsa Tahayori, Hooman Bahadori-jahromi, Ali Department of Computer Science and Engineering and IT Shiraz University Shiraz Iran School of Computing and Engineering University of West London London United Kingdom

Indoor electrical systems are aimed to provide comfort to the occupants. However, their operation is contingent on the presence or needs of the residents. Hence, to optimize energy consumption and guarantee the desired comfort level of residents, any indoor energy control system should consider the occupancy dynamism within houses and the occupants' behavior patterns. Moreover, there is a growing demand for localized and personalized comfort controls in residential buildings to improve the occupants’ satisfaction. This paper presents a Personalized Energy and Comfort Management System (PECMS) that optimizes building energy consumption and meanwhile maintains residents’ intended comfort levels by predicting their trajectories. Considering home thermal characterization, PECMS coordinates the building system devices and residents by anticipating residents’ behavior using an activity mining and tracking method. With this capability, efficient scheduling of the electrical devices would be achieved. PECMS is simulated and tested on a dataset from a real-world smart home project, including the home’s actual thermal zones, temperatures, and residents’ preferences. Comparative analysis is conducted to evaluate PECMS against existing methods and systems, showcasing its effectiveness in achieving the desired trade-off between energy consumption and occupant comfort levels. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Energy utilization

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Optimal Voltage Regulator Coordination via Cuckoo Search Algorithm in Distributed Generation System to Enhance Grid Stability

Optimal Voltage Regulator Coordination via Cuckoo Search Alg...

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2024 IEEE Sustainable Power and Energy Conference, iSPEC 2024

作者： Kamal, Hamid Ali Afrouzi, Hadi Nabipour Jayamani, Elammaran Ektesabi, Mehran Siang, Chua Hong En, Lee Hui Swinburne University of Technology Faculty of Engineering Computing and Science Sarawak Malaysia Swinburne University of Technology Faculty of Engineering Computing and Science Melbourne Australia

ISBN: (纸本)9798350395075

Hybrid power systems need frequent voltage regulation to handle fluctuations in load profile and renewable energy output, ensuring effective voltage management. This increased need for regulation extends the operating time of the tap changer in transformers, accelerating the degradation of the on-load tap changer (OLTC) device, which happens to be the most costly component of the transformer. In this paper, the optimal coordinated operations among distribution static compensator (DSTATCOM), distributed generation (DGs), and OLTC are proposed using the Cuckoo Search algorithm (CSA). The aim is to optimize voltage deviations, OLTC usage, and power losses. The effectiveness of this approach is evaluated across two realistic scenarios using 32 distribution test systems. The simulation outcomes demonstrate how important coordinated management is in ensuring that voltage standards are met at each node while lowering power losses. © 2024 IEEE.

关键词： Distribution transformers

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Human Drawing-Driven Automated Evaluation for Parkinson's Disease Detection

Human Drawing-Driven Automated Evaluation for Parkinson's Di...

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Pareek, Keshav Jain, Garima Jain, Sanat Bhargav, Vaibhav Vit Bhopal University School of Computing Science Engineering and Artificial Intelligence Sehore India Vit Bhopal University School of Computing Science and Engineering Sehore India

ISBN: (纸本)9798331529833

Globally, Parkinson's Disease is the second most prevalent neurodegenerative disorder, which is identified by distinct symptoms like tremors, bradykinesia, rigidity, and postural instability. Early signs may include reduced facial expression, impaired hand-eye coordination, and decreased muscle-brain coordination. As PD progresses, symptoms worsen, potentially leading to severe disability. Although there is no cure, early detection and intervention can improve patient outcomes. Traditional PD diagnosis methods, involving clinical assessments and neurological tests, are often time-consuming and subjective. Machine Learning (ML) provides a promising alternative for automating PD detection using data-driven algorithms. This paper explores various ML methods, including Logistic Regression, K-Nearest Neighbors (KNN), Linear Discriminant Analysis, Random Forest, Gaussian Naive Bayes, Decision Tree, and Support Vector Classifier (SVC), to predict early-stage PD symptoms. After experiments, the Gaussian Naive Bayes model scored the highest accuracy of 78%, showcasing the potential of ML to improve early detection. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Robotic Automation System for Indoor Greenhouse Applications 12

Robotic Automation System for Indoor Greenhouse Applications

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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Qi, Josiah Yang Hung, Lai Chean Jo, Hudyjaya Siswoyo Kiat, Justin Sia Wei Ting, Evon Lim Wan Dunn, Michelle Swinburne University of Technology Faculty of Engineering Computing and Science Kuching Malaysia Swinburne University of Technology Faculty of Engineering Computing and Science Melbourne Australia

ISBN: (纸本)9798350340204

As the world advances, the global population is projected to reach an estimated 9 billion by 2050, leading to a higher demand for food. To boost agricultural production without harming the environment, new and sustainable technologies are essential for improving efficiency and output. Precision farming has existed for years, but as its efficiency improves, scalability on large farms has emerged as a significant challenge. This project proposes using a mobile robotic system that can be deployed in existing greenhouses with minor changes and little additional infrastructure to assist current laborers in the agricultural sector. This paper would focus on the development and early onsite testing of the proposed robot in an indoor greenhouse environment. The robot successfully interacted with plants in 105 out of 135 test runs in a laboratory setting giving a success rate of 75.5%. Onsite testing where done with similar configurations to the laboratory settings to identify potential problems in a different environment. © 2024 IEEE.

关键词： Greenhouse Automation Greenhouses Object Detection Precision Agriculture Precision Farming Robotics Visual Control Feedback

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ANNDRA-IoT:A Deep Learning Approach for Optimal Resource Allocation in Internet of Things Environments

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Computer Modeling in engineering & sciences 2025年第3期142卷 3155-3179页

作者： Abdullah M.Alqahtani Kamran Ahmad Awan Abdulaziz Almaleh Osama Aletri Department of Electrical and Electronic Engineering College of Engineering and Computer ScienceJazan UniversityJazan45142Saudi Arabia Department of Information Technology The University of HaripurHaripur22620Pakistan College of Computer Science King Khalid UniversityAbha62529Saudi Arabia Department of Computing College of Engineering and ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia

Efficient resource management within Internet of Things(IoT)environments remains a pressing challenge due to the increasing number of devices and their diverse *** study introduces a neural network-based model that uses Long-Short-Term Memory(LSTM)to optimize resource allocation under dynam-ically changing *** to monitor the workload on individual IoT nodes,the model incorporates long-term data dependencies,enabling adaptive resource distribution in real *** training process utilizes Min-Max normalization and grid search for hyperparameter tuning,ensuring high resource utilization and consistent *** simulation results demonstrate the effectiveness of the proposed method,outperforming the state-of-the-art approaches,including Dynamic and Efficient Enhanced Load-Balancing(DEELB),Optimized Scheduling and Collaborative Active Resource-management(OSCAR),Convolutional Neural Network with Monarch Butterfly Optimization(CNN-MBO),and Autonomic Workload Prediction and Resource Allocation for Fog(AWPR-FOG).For example,in scenarios with low system utilization,the model achieved a resource utilization efficiency of 95%while maintaining a latency of just 15 ms,significantly exceeding the performance of comparative methods.

关键词： Internet of things resource optimization deep learning optimal resource allocation neural network efficiency

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Real-Time Multi Fractal Trust Evaluation Model for Efficient Intrusion Detection in Cloud

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Intelligent Automation & Soft computing 2023年第8期37卷 1895-1907页

作者： S.Priya R.S.Ponmagal Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyChengapattu603203Tamil NaduIndia Department of Computing Technologies School of ComputingSRM Institute of Science and TechnologyChengapattu603203Tamil NaduIndia

Handling service access in a cloud environment has been identified as a critical challenge in the modern internet world due to the increased rate of intrusion *** address such threats towards cloud services,numerous techniques exist that mitigate the service threats according to different *** rule-based approaches are unsuitable for new threats,whereas trust-based systems estimate trust value based on behavior,flow,and other ***,the methods suffer from mitigating intrusion attacks at a higher *** article presents a novel Multi Fractal Trust Evaluation Model(MFTEM)to overcome these *** method involves analyzing service growth,network growth,and quality of service *** process estimates the user’s trust in various ways and the support of the user in achieving higher service performance by calculating Trusted Service Support(TSS).Also,the user’s trust in supporting network stream by computing Trusted Network Support(TNS).Similarly,the user’s trust in achieving higher throughput is analyzed by computing Trusted QoS Support(TQS).Using all these measures,the method adds the Trust User Score(TUS)value to decide on the clearance of user *** proposed MFTEM model improves intrusion detection accuracy with higher performance.

关键词： Intrusion detection cloud systems trusted service support trusted network support trust user score trusted QoS support

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A Review of Sustainable Farming with Robotics in Indoor Greenhouse Environments 12

A Review of Sustainable Farming with Robotics in Indoor Gree...

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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

ISBN: (纸本)9798350340204

Current trend projected the global population to increase to 9 billion by the year 2050 which will increase the demand of food production by around 70%. The agricultural sector also supplies produce to bio-based products such as bioenergy and bioplastics which is also increasing the demand for crops. However, the situation is not as simple as planting more crops, as current agricultural practices are not sustainable. Environmental impact and climate changes could be reduced with the utilization of indoor farming could bring about a more sustainable farming solution. Combined with the utilization of precision farming solutions such as IoT based sensors and robotic systems indoor farming could become the next step in sustainable farming. The implementation of precision farming in indoor farming comes with its problems which may cause a challenge in the development and deployment period. This paper serves as the preliminary research for the development of a robotic system to be deployed at Sarawak's Digital Economy Corporation (SDEC)'s greenhouse. © 2024 IEEE.

关键词： Sustainable agriculture

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Minimal Context-Switching Data Race Detection with Dataflow Tracking

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Journal of Computer science & Technology 2024年第1期39卷 211-226页

作者：郑龙李洋辛杰刘海峰郑然廖小飞金海 National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Data race is one of the most important concurrent anomalies in multi-threaded *** con-straint-based techniques are leveraged into race detection,which is able to find all the races that can be found by any oth-er sound race ***,this constraint-based approach has serious limitations on helping programmers analyze and understand data ***,it may report a large number of false positives due to the unrecognized dataflow propa-gation of the ***,it recommends a wide range of thread context switches to schedule the reported race(in-cluding the false one)whenever this race is exposed during the constraint-solving *** ad hoc recommendation imposes too many context switches,which complicates the data race *** address these two limitations in the state-of-the-art constraint-based race detection,this paper proposes DFTracker,an improved constraint-based race detec-tor to recommend each data race with minimal thread context ***,we reduce the false positives by ana-lyzing and tracking the dataflow in the *** this means,DFTracker thus reduces the unnecessary analysis of false race *** further propose a novel algorithm to recommend an effective race schedule with minimal thread con-text switches for each data *** experimental results on the real applications demonstrate that 1)without removing any true data race,DFTracker effectively prunes false positives by 68%in comparison with the state-of-the-art constraint-based race detector;2)DFTracker recommends as low as 2.6-8.3(4.7 on average)thread context switches per data race in the real world,which is 81.6%fewer context switches per data race than the state-of-the-art constraint based race ***,DFTracker can be used as an effective tool to understand the data race for programmers.

关键词： data race satisfiability modulo theory multi-threaded program dynamic detection

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ReCSA:a dedicated sort accelerator using ReRAM-based content addressable memory

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Frontiers of Computer science 2023年第2期17卷 1-13页

作者： Huize LI Hai JIN Long ZHENG Yu HUANG Xiaofei LIAO National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

With the increasing amount of data,there is an urgent need for efficient sorting algorithms to process large data *** sorting algorithms have attracted much attention because they can take advantage of different hardware's *** the traditional hardware sort accelerators suffer“memory wall”problems since their multiple rounds of data transmission between the memory and the *** this paper,we utilize the in-situ processing ability of the ReRAM crossbar to design a new ReCAM array that can process the matrix-vector multiplication operation and the vector-scalar comparison in the same array *** this designed ReCAM array,we present ReCSA,which is the first dedicated ReCAM-based sort *** hardware designs,we also develop algorithms to maximize memory utilization and minimize memory exchanges to improve sorting *** sorting algorithm in ReCSA can process various data types,such as integer,float,double,and *** also present experiments to evaluate the performance and energy efficiency against the state-of-the-art sort *** experimental results show that ReCSA has 90.92×,46.13×,27.38×,84.57×,and 3.36×speedups against CPU-,GPU-,FPGA-,NDP-,and PIM-based platforms when processing numeric data *** also has 24.82×,32.94×,and 18.22×performance improvement when processing string data sets compared with CPU-,GPU-,and FPGA-based platforms.

关键词： ReCAM parallel sorting architecture design processing-in-memory

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