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基于随机量子层的变分量子卷积神经网络鲁棒性研究
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作者 戚晗 王敬童 +1 位作者 abdullah gani 拱长青 《信息网络安全》 CSCD 北大核心 2024年第3期363-373,共11页
近年来,量子机器学习被证明与经典机器学习一样会被一个精心设计的微小扰动干扰从而造成识别准确率严重下降。目前增加模型对抗鲁棒性的方法主要有模型优化、数据优化和对抗训练。文章从模型优化角度出发,提出了一种新的方法,旨在通过... 近年来,量子机器学习被证明与经典机器学习一样会被一个精心设计的微小扰动干扰从而造成识别准确率严重下降。目前增加模型对抗鲁棒性的方法主要有模型优化、数据优化和对抗训练。文章从模型优化角度出发,提出了一种新的方法,旨在通过将随机量子层与变分量子神经网络连接组成新的量子全连接层,与量子卷积层和量子池化层组成变分量子卷积神经网络(Variational Quantum Convolutional Neural Networks,VQCNN),来增强模型的对抗鲁棒性。文章在KDD CUP99数据集上对基于VQCNN的量子分类器进行了验证。实验结果表明,在快速梯度符号法(Fast Gradient Sign Method,FGSM)、零阶优化法(Zeroth-Order Optimization,ZOO)以及基于遗传算法的生成对抗样本的攻击下,文章提出的VQCNN模型准确率下降值分别为11.18%、15.21%和33.64%,与其它4种模型相比准确率下降值最小。证明该模型在对抗性攻击下具有更高的稳定性,其对抗鲁棒性更优秀。同时在面对基于梯度的攻击方法(FGSM和ZOO)时的准确率下降值更小,证明文章提出的VQCNN模型在面对此类攻击时更有效。 展开更多
关键词 随机量子电路 量子机器学习 对抗性攻击 变分量子线路
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Detecting and Mitigating DDOS Attacks in SDNs Using Deep Neural Network
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作者 Gul Nawaz Muhammad Junaid +5 位作者 Adnan Akhunzada abdullah gani Shamyla Nawazish Asim Yaqub Adeel Ahmed Huma Ajab 《Computers, Materials & Continua》 SCIE EI 2023年第11期2157-2178,共22页
Distributed denial of service(DDoS)attack is the most common attack that obstructs a network and makes it unavailable for a legitimate user.We proposed a deep neural network(DNN)model for the detection of DDoS attacks... Distributed denial of service(DDoS)attack is the most common attack that obstructs a network and makes it unavailable for a legitimate user.We proposed a deep neural network(DNN)model for the detection of DDoS attacks in the Software-Defined Networking(SDN)paradigm.SDN centralizes the control plane and separates it from the data plane.It simplifies a network and eliminates vendor specification of a device.Because of this open nature and centralized control,SDN can easily become a victim of DDoS attacks.We proposed a supervised Developed Deep Neural Network(DDNN)model that can classify the DDoS attack traffic and legitimate traffic.Our Developed Deep Neural Network(DDNN)model takes a large number of feature values as compared to previously proposed Machine Learning(ML)models.The proposed DNN model scans the data to find the correlated features and delivers high-quality results.The model enhances the security of SDN and has better accuracy as compared to previously proposed models.We choose the latest state-of-the-art dataset which consists of many novel attacks and overcomes all the shortcomings and limitations of the existing datasets.Our model results in a high accuracy rate of 99.76%with a low false-positive rate and 0.065%low loss rate.The accuracy increases to 99.80%as we increase the number of epochs to 100 rounds.Our proposed model classifies anomalous and normal traffic more accurately as compared to the previously proposed models.It can handle a huge amount of structured and unstructured data and can easily solve complex problems. 展开更多
关键词 Distributed denial of service(DDoS)attacks software-defined networking(SDN) classification deep neural network(DNN)
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求解整数线性规划问题的量子近似优化算法
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作者 戚晗 何婉莹 +1 位作者 邱涛 abdullah gani 《沈阳航空航天大学学报》 2023年第3期28-36,共9页
量子近似优化算法是一种量子经典混合算法,它可以在多项式时间内求得组合优化问题的最优解。但是在低迭代水平时,得到问题最优解的概率较低。为了应对这一挑战,基于改进的目标哈密顿量,设计了一种具有较少量子门的量子线路,简化了求解过... 量子近似优化算法是一种量子经典混合算法,它可以在多项式时间内求得组合优化问题的最优解。但是在低迭代水平时,得到问题最优解的概率较低。为了应对这一挑战,基于改进的目标哈密顿量,设计了一种具有较少量子门的量子线路,简化了求解过程,提高了求解精度。通过求解整数线性规划问题进行实验,以验证所提出解决方案的可靠性,实验部署在本源量子的pyQpanda环境中。结果表明,平均执行时间为原始时间的20.8%,概率由54.1563%提高到82.9%。 展开更多
关键词 量子计算 量子近似优化算法 整数线性规划 伊辛模型 哈密顿量
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Deep Learning Based Classification of Wrist Cracks from X-ray Imaging 被引量:1
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作者 Jahangir Jabbar Muzammil Hussain +3 位作者 Hassaan Malik abdullah gani Ali Haider Khan Muhammad Shiraz 《Computers, Materials & Continua》 SCIE EI 2022年第10期1827-1844,共18页
Wrist cracks are the most common sort of cracks with an excessive occurrence rate.For the routine detection of wrist cracks,conventional radiography(X-ray medical imaging)is used but periodically issues are presented ... Wrist cracks are the most common sort of cracks with an excessive occurrence rate.For the routine detection of wrist cracks,conventional radiography(X-ray medical imaging)is used but periodically issues are presented by crack depiction.Wrist cracks often appear in the human arbitrary bone due to accidental injuries such as slipping.Indeed,many hospitals lack experienced clinicians to diagnose wrist cracks.Therefore,an automated system is required to reduce the burden on clinicians and identify cracks.In this study,we have designed a novel residual network-based convolutional neural network(CNN)for the crack detection of the wrist.For the classification of wrist cracks medical imaging,the diagnostics accuracy of the RN-21CNN model is compared with four well-known transfer learning(TL)models such as Inception V3,Vgg16,ResNet-50,and Vgg19,to assist the medical imaging technologist in identifying the cracks that occur due to wrist fractures.The RN-21CNN model achieved an accuracy of 0.97 which is much better than its competitor`s approaches.The results reveal that implementing a correct generalization that a computer-aided recognition system precisely designed for the assistance of clinician would limit the number of incorrect diagnoses and also saves a lot of time. 展开更多
关键词 Wrist cracks FRACTURE deep learning X-rays CNN
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Big data storage technologies: a survey 被引量:17
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作者 Aisha SIDDIQA Ahmad KARIM abdullah gani 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2017年第8期1040-1070,共31页
There is a great thrust in industry toward the development of more feasible and viable tools for storing fast-growing volume, velocity, and diversity of data, termed 'big data'. The structural shift of the storage m... There is a great thrust in industry toward the development of more feasible and viable tools for storing fast-growing volume, velocity, and diversity of data, termed 'big data'. The structural shift of the storage mechanism from traditional data management systems to NoSQL technology is due to the intention of fulfilling big data storage requirements. However, the available big data storage technologies are inefficient to provide consistent, scalable, and available solutions for continuously growing heterogeneous data. Storage is the preliminary process of big data analytics for real-world applications such as scientific experiments, healthcare, social networks, and e-business. So far, Amazon, Google, and Apache are some of the industry standards in providing big data storage solutions, yet the literature does not report an in-depth survey of storage technologies available for big data, investigating the performance and magnitude gains of these technologies. The primary objective of this paper is to conduct a comprehensive investigation of state-of-the-art storage technologies available for big data. A well-defined taxonomy of big data storage technologies is presented to assist data analysts and researchers in understanding and selecting a storage mecha- nism that better fits their needs. To evaluate the performance of different storage architectures, we compare and analyze the ex- isling approaches using Brewer's CAP theorem. The significance and applications of storage technologies and support to other categories are discussed. Several future research challenges are highlighted with the intention to expedite the deployment of a reliable and scalable storage system. 展开更多
关键词 Big data Big data storage NoSQL databases Distributed databases CAP theorem SCALABILITY Consistency-partition resilience Availability-partition resilience
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Review:Data center network architecture in cloud computing:review, taxonomy, and open research issues 被引量:2
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作者 Han QI Muhammad SHIRAZ +3 位作者 Jie-yao LIU abdullah gani Zulkanain ABDUL RAHMAN Torki A.ALTAMEEM 《Journal of Zhejiang University-Science C(Computers and Electronics)》 SCIE EI 2014年第9期776-793,共18页
The data center network(DCN), which is an important component of data centers, consists of a large number of hosted servers and switches connected with high speed communication links. A DCN enables the deployment of r... The data center network(DCN), which is an important component of data centers, consists of a large number of hosted servers and switches connected with high speed communication links. A DCN enables the deployment of resources centralization and on-demand access of the information and services of data centers to users. In recent years, the scale of the DCN has constantly increased with the widespread use of cloud-based services and the unprecedented amount of data delivery in/between data centers, whereas the traditional DCN architecture lacks aggregate bandwidth, scalability, and cost effectiveness for coping with the increasing demands of tenants in accessing the services of cloud data centers. Therefore, the design of a novel DCN architecture with the features of scalability, low cost, robustness, and energy conservation is required. This paper reviews the recent research findings and technologies of DCN architectures to identify the issues in the existing DCN architectures for cloud computing. We develop a taxonomy for the classification of the current DCN architectures, and also qualitatively analyze the traditional and contemporary DCN architectures. Moreover, the DCN architectures are compared on the basis of the significant characteristics, such as bandwidth, fault tolerance, scalability, overhead, and deployment cost. Finally, we put forward open research issues in the deployment of scalable, low-cost, robust, and energy-efficient DCN architecture, for data centers in computational clouds. 展开更多
关键词 Data center network Cloud computing ARCHITECTURE Network topology
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