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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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TRS Scheduling for Improved QoS Performance in Cloud System

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computers, Materials & Continua 2023年第4期75卷 1547-1559页

作者： G.John Samuel Babu M.Baskar Department of Computer Science and Engineering School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamilnadu603203India Department of Computing Technologies School of ComputingCollege of Engineering andTechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamilnadu603203India

Numerous methods are analysed in detail to improve task schedulingand data security performance in the cloud environment. The methodsinvolve scheduling according to the factors like makespan, waiting time,cost, deadline, and popularity. However, the methods are inappropriate forachieving higher scheduling performance. Regarding data security, existingmethods use various encryption schemes but introduce significant serviceinterruption. This article sketches a practical Real-time Application CentricTRS (Throughput-Resource utilization–Success) Scheduling with Data Security(RATRSDS) model by considering all these issues in task scheduling anddata security. The method identifies the required resource and their claim timeby receiving the service requests. Further, for the list of resources as services,the method computes throughput support (Thrs) according to the number ofstatements executed and the complete statements of the service. Similarly, themethod computes Resource utilization support (Ruts) according to the idletime on any duty cycle and total servicing time. Also, the method computesthe value of Success support (Sus) according to the number of completions forthe number of allocations. The method estimates the TRS score (ThroughputResource utilization Success) for different resources using all these supportmeasures. According to the value of the TRS score, the services are rankedand scheduled. On the other side, based on the requirement of service requests,the method computes Requirement Support (RS). The selection of service isperformed and allocated. Similarly, choosing the route according to the RouteSupport Measure (RSM) enforced route security. Finally, data security hasgets implemented with a service-based encryption technique. The RATRSDSscheme has claimed higher performance in data security and scheduling.

关键词： Cloud task scheduling TRS quality of service RSM route security data security SDE RATRSDS

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Feature analysis of fake news: improving fake news detection in social media

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Journal of Cyber Security Technology 2023年第4期7卷 224-241页

作者： Leung, Johnathan Vatsalan, Dinusha Arachchilage, Nalin The School of Computer Science The University of Auckland Auckland New Zealand The School of Computing Macquarie University Sydney Australia

Fake news is a threat to society, and its spread can have real-world consequences in many situations. For example, attackers have weaponised fake news to influence user opinion by causing users to emotionally react to fake news. On the other hand, fake news can also be a threat to national democracy. Therefore, we investigate textual sentiment, visual sentiment, behavioural and metadata features that entice users, using a dataset of posts from Reddit and another from Twitter, that were already categorised into the labels of fake news and real news. First, we extract features, such as visual sentiment, textual sentiment, behavioural reactions, and metadata, and then analyse various features for fake news prediction. We then run a machine learning experiment to classify posts that help improve fake news detection in social media. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Metadata

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Classification of Indian Medicinal Flowers using MobileNetV2 18

Classification of Indian Medicinal Flowers using MobileNetV2

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

作者： Bipin Nair, B.J. Arjun, B. Abhishek, S. Abhinav, N.M. Madhavan, Varun School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science Mysuru India School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru India

ISBN: (纸本)9789380544519

Image-based classification of medicinal flowers is a crucial and game-changing work that can help in developing the traditional medical practices and healthcare techniques that have been followed in countries including India and China for centuries. Accurate work can help enhance traditional practices, making them faster and more efficient. Further studies on this topic can help in drug discovery. On doing a thorough review of the previous works we have found a lack of works regarding the classification of Indian Medicinal flowers and to overcome this, our work presents a robust methodology for classifying Indian medicinal flowers using the MobileNetV2 light-weight network. We have worked with a dataset of 3343 flowers that are divided across 13 classes consisting of images of flowers taken in their natural environment under varying backgrounds, light, and climatic conditions which were then preprocessed to make it fit for our task. The images in the dataset are used with MobileNetV2 to classify each of the images into the individual class of the flower. While doing a comparative study, we inferred the best parameters for optimal results with our dataset and measured its performance using various reference metrics. The highest accuracy that we obtained with MobileNetV2 was 98.23%. It was also observed that the performance of the deep learning model is affected by the distribution and representation of each flowers in the dataset. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： classification deep learning Image processing Medicinal flowers MobileNet

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Secure location-aware geocast routing in Internet of Vehicles

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Multimedia Tools and Applications 2025年第18期84卷 19669-19692页

作者： Kumar, Sunil Dohare, Upasana Kumar, Sushil Department of Computer Science and Information Technology Central University of Haryana Mahendragarh India School of Computing Science & Engineering Galgotias University Greater Noida India School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

Efficient message dissemination in Vehicular Ad Hoc Networks (VANETs) relies on robust connectivity between neighboring vehicular nodes, yet it is often compromised by malicious intruders. While recent literature proposes various approaches to enhance geocasting performance, security considerations for connectivity are often overlooked. To address this gap, we propose the Secure Location-Aware Cache Agent based Geocast Routing (SL-CAGR) protocol, emphasizing an Intrusion Prevention Technique (IPT) for message integrity preservation and a Connectivity Reliant Packet Forwarding (CRPF) algorithm for improved VANET connectivity. Our protocol introduces a network model for cache agent-based geocast routing on the Internet of Vehicle (IoV) environment, implements IPT using Diffie-Hellman digital signatures, and presents a secure geocast routing strategy focusing on re-caching and cache agent selection. Empirical simulations demonstrate SL-CAGR's superiority in packet delivery ratio, packet loss, duplication, hop count, end-to-end delay, and network load compared to these protocols. Simulation results demonstrate that SL-CAGR outperforms state-of-the-art techniques like CAG, CTRC, and D-Flooding. SL-CAGR stands out by merging intrusion prevention, secure geocast routing, and cache agent selection, ensuring robust security and efficiency in VANETs. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Vehicular ad hoc networks

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AI based Pentest For EHR and Other Health Monitoring Devices 5

AI based Pentest For EHR and Other Health Monitoring Devices

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Kumaran, U. Gurupriya, M. Chinta, Hitesh Surya Maganti, Akshita Uma Sai Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science Bangalore India Amrita School of Computing Dept. of Computer Science Bangalore India

ISBN: (纸本)9798331504403

The increasing reliance on Electronic Health Records (EHRs) and health monitoring devices has made healthcare systems vulnerable to cyberattacks. This research proposes an AI-based penetration testing framework to address these security concerns. By integrating machine learning techniques, our framework offers a more realistic approach to learning actual attack patterns, improving detection accuracy and response time compared to traditional methods. The framework's ability to minimize false positives and adapt to new threats demonstrates its effectiveness in strengthening healthcare system security. This research highlights the potential of AI-driven penetration testing in safeguarding patient information and protecting the integrity of healthcare systems. By identifying and addressing vulnerabilities, this framework can contribute to a more secure and resilient healthcare environment. © 2024 IEEE.

关键词： Electronic health record

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Efficient Ship:A Hybrid Deep Learning Framework for Ship Detection in the River

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computer Modeling in Engineering & sciences 2024年第1期138卷 301-320页

作者： Huafeng Chen Junxing Xue Hanyun Wen Yurong Hu Yudong Zhang School of Computer Engineering Jingchu University of TechnologyJingmen448000China School of Computer Science Yangtze UniversityJingzhou434023China School of Computing and Mathematic Sciences University of LeicesterLeicesterLE17RHUK

Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore *** deep learning researches on optical image-based ship detection mainly focus on improving one-stage detectors for real-time ship detection but sacrifices the accuracy of *** solve this problem,we present a hybrid ship detection framework which is named EfficientShip in this *** core parts of the EfficientShip are DLA-backboned object location(DBOL)and CascadeRCNN-guided object classification(CROC).The DBOL is responsible for finding potential ship objects,and the CROC is used to categorize the potential ship *** also design a pixel-spatial-level data augmentation(PSDA)to reduce the risk of detection model *** compare the proposed EfficientShip with state-of-the-art(SOTA)literature on a ship detection dataset called *** show our ship detection framework achieves a result of 99.63%(mAP)at 45 fps,which is much better than 8 SOTA approaches on detection accuracy and can also meet the requirements of real-time application scenarios.

关键词： Ship detection deep learning data augmentation object location object classification

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A survey on dynamic graph processing on GPUs: concepts, terminologies and systems

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Frontiers of computer science 2024年第4期18卷 1-23页

作者： Hongru GAO Xiaofei LIAO Zhiyuan SHAO Kexin LI Jiajie CHEN Hai JIN 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 TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graphs that are used to model real-world entities with vertices and relationships among entities with edges,have proven to be a powerful tool for describing real-world problems in *** most real-world scenarios,entities and their relationships are subject to constant *** that record such changes are called dynamic *** recent years,the widespread application scenarios of dynamic graphs have stimulated extensive research on dynamic graph processing systems that continuously ingest graph updates and produce up-to-date graph analytics *** the scale of dynamic graphs becomes larger,higher performance requirements are demanded to dynamic graph processing *** the massive parallel processing power and high memory bandwidth,GPUs become mainstream vehicles to accelerate dynamic graph processing ***-based dynamic graph processing systems mainly address two challenges:maintaining the graph data when updates occur(i.e.,graph updating)and producing analytics results in time(i.e.,graph computing).In this paper,we survey GPU-based dynamic graph processing systems and review their methods on addressing both graph updating and graph *** comprehensively discuss existing dynamic graph processing systems on GPUs,we first introduce the terminologies of dynamic graph processing and then develop a taxonomy to describe the methods employed for graph updating and graph *** addition,we discuss the challenges and future research directions of dynamic graph processing on GPUs.

关键词： dynamic graphs graph processing graph algorithms GPUs

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Energy-Efficient Dynamic Configurable Datapath Architecture for IoT Devices

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Journal of Communications and Information Networks 2024年第3期9卷 251-261页

作者： Ruizhe Zhang Junhui Liu Han Wang Li Lu Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of ChinaChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

This paper introduces a novel RISC-V processor architecture designed for ultra-low-power and energy-efficient applications,particularly for Internet of things(IoT)*** architecture enables runtime dynamic reconfiguration of the datapath,allowing efficient balancing between computational performance and power *** is achieved through interchangeable components and clock gating mechanisms,which help the processor adapt to varying workloads.A prototype of the architecture was implemented on a Xilinx Artix 7 field programmable gate array(FPGA).Experimental results show significant improvements in power efficiency and *** mini configuration achieves an impressive reduction in power consumption,using only 36%of the baseline ***,the full configuration boosts performance by 8%over the *** flexible and adaptable nature of this architecture makes it highly suitable for a wide range of low-power IoT applications,providing an effective solution to meet the growing demands for energy efficiency in modern IoT devices.

关键词： dynamic reconfiguration Internet of things(IoT) power efficiency RISC-V

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TM2SP: A Transformer-based Multi-Level Spatiotemporal Feature Pyramid Network for Video Saliency Prediction

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5236-5250页

作者： Li, Chenming Liu, Shiguang Tianjin University School of Computer Science and Technology College of Intelligence and Computing Tianjin300350 China Tianjin University Tianjin Key Laboratory of Cognitive Computing and Application Tianjin300350 China

This paper proposes an end-to-end video saliency prediction network model, termed TM2SP-Net (Transformer-based Multi-level Spatiotemporal Feature Pyramid Network). Leveraging the strong encoding learning capability of Video Swin Transformer for video data, we design a Multi-level Spatiotemporal Feature Pyramid Network (MLSTFPN) that effectively detects and enriches salient regions and spatial details across different scales. In particular, a pre-trained image saliency detection encoder is employed to extract salient features from each frame, serving as prior knowledge to guide the multi-scale spatiotemporal feature fusion and decoding processes. Additionally, we introduce an Inception Gate-Controlled Fusion (IGCF) and Layered Self-Attention Aggregation Fusion (LSAF) mechanisms to efficiently merge spatiotemporal features across various stages. Finally, extensive experiments conducted on the DHF1K, Hollywood-2, UCF-Sports, and six audio-visual saliency datasets demonstrate the superiority of our method over existing state-of-the-art approaches. © 1991-2012 IEEE.

关键词： Signal encoding

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