The proceedings contain 284 papers. The topics discussed include: a dynamic anomaly detection approach for fault detection on fire alarm system based on fuzzy-PSO-CNN approach;enhanced skin cancer prediction with anal...
ISBN:
(纸本)9798350313987
The proceedings contain 284 papers. The topics discussed include: a dynamic anomaly detection approach for fault detection on fire alarm system based on fuzzy-PSO-CNN approach;enhanced skin cancer prediction with analysis using machinelearning algorithms;comparison study of different neural network models for assessing employability skills of IT graduates;performance evaluation of machinelearning algorithms for prediction of cardiac failure;design of improvised DCM-based tunable true random number generator;design and implementation of piezoelectric shoe;prediction and processing of environment geography images using deep learning techniques;human activity recognition in video surveillance using long-term recurrent convolutional network;and energy-efficient task offloading for edge computing-based smart grid networks using human urbanization.
Threats of the network that have ever/never happened could be anomalies. Protecting networks against malicious access has always been challenging and despite a long time study into this matter, it never becomes easier...
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With the deepening of the construction of the new power system, the energy technology will be transformed from production, transformation and transmission to digital, network and intelligent, and the technological cha...
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We propose LESS-VFL, a communicationefficient feature selection method for distributed systems with vertically partitioned data. We consider a system of a server and several parties with local datasets that share a sa...
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We propose LESS-VFL, a communicationefficient feature selection method for distributed systems with vertically partitioned data. We consider a system of a server and several parties with local datasets that share a sample ID space but have different feature sets. The parties wish to collaboratively train a model for a prediction task. As part of the training, the parties wish to remove unimportant features in the system to improve generalization, efficiency, and explainability. In LESS-VFL, after a short pre-training period, the server optimizes its part of the global model to determine the relevant outputs from party models. This information is shared with the parties to then allow local feature selection without communication. We analytically prove that LESS-VFL removes spurious features from model training. We provide extensive empirical evidence that LESS-VFL can achieve high accuracy and remove spurious features at a fraction of the communication cost of other feature selection approaches.
machinelearning-based network traffic categorization has become a crucial field of study because of the rising intricacy of Internet services and the expanding usage of en-cryption. A thorough examination of the appl...
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Traditional network architecture can no longer meet the development needs of technologies such as cloud computing and big data. SDN network architecture has the characteristics of high openness and programmability, wh...
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Decentralized learning is appealing as it enables the scalable usage of large amounts of distributed data and resources without resorting to any central entity, while promoting privacy since every user minimizes the d...
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Decentralized learning is appealing as it enables the scalable usage of large amounts of distributed data and resources without resorting to any central entity, while promoting privacy since every user minimizes the direct exposure of their data. Yet, without additional precautions, curious users can still leverage models obtained from their peers to violate privacy. In this paper, we propose DECOR, a variant of decentralized SGD with differential privacy (DP) guarantees. In DECOR, users securely exchange randomness seeds in one communication round to generate pairwise-canceling correlated Gaussian noises, which are injected to protect local models at every communication round. We theoretically and empirically show that, for arbitrary connected graphs, DECOR matches the central DP optimal privacy-utility trade-off. We do so under SecLDP, our new relaxation of local DP, which protects all user communications against an external eavesdropper and curious users, assuming that every pair of connected users shares a secret, i.e., an information hidden to all others. The main theoretical challenge is to control the accumulation of non-canceling correlated noise due to network sparsity. We also propose a companion SecLDP privacy accountant for public use.
network traffic is vulnerable to attacks by hackers and threat actors. There are varieties of attacks which can be both passive and aggressive which used to threaten network security and privacy. For identifying such ...
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The integration of renewable energy sources for decentralized power generation in the existing power system network has resulted in continuous changing topology of the network. The existing adaptive protection schemes...
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With the rapid development of cloud computing and mobile Internet, there have been a variety of network attacks, among which distributed denial of service (DDoS) is one of the most fatal attacks. Traditional machine l...
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