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Malicious Traffic Classification for IoT based on Graph Attention network and Long Short-Term Memory network

Malicious Traffic Classification for IoT based on Graph Atte...

引用

Asia-Pacific network Operations and Management Symposium (APNOMS)

作者： Lei Zhang Lizhuang Tan Huiling Shi Hongyang Sun Wei Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

With the rapid development of digital services and the Internet of Things (IoT), the number and diversity of cyber attacks have reached unprecedented levels, making network traffic classification a crucial means of maintaining network security. However, in the face of increasingly complex traffic attacks, traditional analysis of individual traffic flows is no longer sufficient for effective detection. Therefore, to reveal the potential correlations between network traffic flows, we propose a novel IoT malicious traffic classification model called EGAT-LSTM. This model combines an improved Graph Attention network (GAT) with Long Short-Term Memory (LSTM) network to effectively capture the spatial topology and temporal features of network traffic, ultimately achieving efficient classification. Moreover, we optimize the aggregation function of network node features within the attention mechanism to effectively address the issue of essential information retention during the feature learning process. The experimental results on two publicly available IoT datasets demonstrate that our method achieves improvements in both binary and multi-class classification tasks, exhibiting significant advantages over some current approaches.

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L2fusion: Low-Light Oriented Infrared and Visible Image Fusion

L2fusion: Low-Light Oriented Infrared and Visible Image Fusi...

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IEEE International Conference on Image Processing

作者： Xiang Gao Guohua Lv Aimei Dong Zhonghe Wei Jinyong Cheng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Infrared and visible image fusion aims to integrate salient targets and abundant texture information into a single fused image. Existing methods typically ignore the issue of illumination, so that there are problems of weak texture details and poor visual perception in case of low illumination. To address this issue, we propose a low-light oriented infrared and visible image fusion network, named L2Fusion. In particular, we first design a decomposition network according to Retinex theory to obtain the reflectance features of a visible image with low-light. Then, these features are integrated with the features extracted from the corresponding infrared image by a residual network. The finally fused image largely eliminates the negative impact caused by low illumination, and contains both salient targets and abundant texture information. Extensive experiments demonstrate the superiority of our L2Fusion over the state-of-the-art methods, in terms of both visual effect and quantitative metrics.

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Method and system of power load classification and decomposition based on deep learning

Method and system of power load classification and decomposi...

引用

Smart Grid and Energy Engineering (SGEE), International Conference on

作者： Hao Sun Hongtai Yuan Yudong Geng Jing Chen Chuanfu Zhang Wen Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

This paper introduces a system for the classification and decomposition of electrical loads, addressing the shortcomings of current non-intrusive load decomposition methods that are prone to errors. We propose a method that first classifies electrical loads and then performs decomposition, along with the corresponding system workflow. Initially, we collect household electricity consumption data using a proprietary load collection device developed by the company, constructing an electrical load dataset. Subsequently, we train classification and decomposition methods using this dataset, conducting comparative experiments with various methods to select the most effective one. Based on the trained electrical load classification and decomposition models, we develop an electrical load inference program. The key implementation lies in applying the electrical load classification and decomposition inference program to edge devices. Leveraging the cloud server hosting a backend web service for the mobile app, we interact with users through the app, achieving precise decomposition and monitoring of electrical loads. Integrating edge computing and cloud computing, coupled with a mobile app serving as the interaction platform, this paper presents a high-precision, simplified, and highly scalable solution for electrical load analysis and management. In experimental comparisons, we find that using the Gated Recurrent Unit (GRU) method from deep learning and the Sequence-to-Point Neural network (Seq2point) method outperform other methods in classification and decomposition, significantly improving the accuracy of electrical load classification. Therefore, we select the GRU method and Seq2Point method as the core techniques for the electrical load classification and decomposition system. This system not only demonstrates significant advantages in performance but also possesses high accuracy, model simplification, and scalability, providing an efficient and reliable solution for elec

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A Global Task Scheduling Method Based on network Measurement and Prediction in computing Power networks

A Global Task Scheduling Method Based on Network Measurement...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Chuanfu Zhang Wen Li Jing Chen Jing Ge Di Wang Na Li Hao Sun Yudong Geng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

To improve the task scheduling efficiency in computing power networks, this paper proposes a global task scheduling method based on network measurement and prediction in computing power networks (GTS-MP), which selects a more suitable computing power cluster and storage platform with a higher network availability score between them to implement tasks. The experiments are conducted on a test bench of urban area networks in 12 cities. The results show that this method greatly reduces the execution time of artificial intelligence model training tasks, with a maximum reduction of 40% compared to other plans.

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Cluster Caching Strategy Based on User Characteristics in Edge networks

Cluster Caching Strategy Based on User Characteristics in Ed...

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Asia-Pacific network Operations and Management Symposium (APNOMS)

作者： Ye Zhang Wei Zhang Hao Hao Kai Zhang Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

To meet the growing traffic demand and relieve the pressure on the core network, edge caching and device-to-device (D2D) communication are used in next generation mobile networks. In this paper, based on a three-way collaboration between user devices (UEs), base stations (BSs) and cloud data center, caching the content most likely to be requested can facilitate direct content delivery without the need to fetch content from remote cloud data center in the mobile edge networks. This reduces the average retrieval latency of content, reduces the backhaul burden and minimises duplicate content delivery. Specifically, we first use a spectral clustering algorithm to cluster users based on their social and physical attributes, and then associate each cluster with a different base station in order to efficiently cache popular contents. Finally, we propose a new caching policy based on content cache value and user activity to take full account of the metrics that influence caching decisions. Experimental results show that our proposed policy can effectively reduce content access latency.

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Job2Vec: A Self-Supervised Contrastive Learning Based HPC Job Power Consumption Prediction Framework

Job2Vec: A Self-Supervised Contrastive Learning Based HPC Jo...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Jie Zhang Jian Song Xiang Li Xuesen Tian Zhigang Zhao Lu Wu Chunxiao Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

In the context of the rapid development of big data and artificial intelligence, the field of HPC is facing significant challenges in energy consumption. To address this challenge, this paper proposes a power consumption prediction framework for HPC jobs based on self-supervised contrastive learning, named Job2Vec. Firstly, HPC jobs are clustered into different clusters based on job logs and power consumption curve shapes. Next, high-quality features of power consumption data at different granularities within each cluster are captured through self-supervised contrastive learning. Lastly, an incremental update strategy is introduced to handle the dynamic changes in HPC data. The experimental results demonstrate that, on the Jinan Supercomputing dataset comprising 1170 HPC jobs, the Job2Vec framework outperforms the baseline models overall in terms of prediction performance. Additionally, we also investigated the model’s performance in single-step and multi-step forecasting, the impact of different indicators on prediction accuracy, and conducted ablation experiments to confirm the indispensability of each module. This is the first time that self-supervised contrastive learning has been introduced into the HPC field, aiming to provide support for energy scheduling in data centers.

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Species-Based Differential Evolution with Migration for Multimodal Optimization 14th

Species-Based Differential Evolution with Migration for Mult...

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14th International Conference on Bio-inspired computing: Theories and Applications, BIC-TA 2019

作者： Li, Wei Fan, Yaochi Jiang, Qiaoyong School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an710048 China

ISBN: (纸本)9789811534249

Due to the fact that many realistic problems are multimodal optimization problems (MMOPs), multimodal optimization has attracted a growing interest. In this paper, a Species-based differential evolution with migration algorithm is proposed for MMOPs. First, a migration strategy is developed to improve the exploration ability of species with less individuals. Then, an improved operation of creating local individuals is introduced to help the algorithm to find high-quality solutions. Finally, an archive mechanism is applied to preserve the best solutions found during previous generations and avoid the loss of the peaks. Experimental results on CEC2013 test problems confirm the effectiveness of the proposed algorithm compared to several well-known multimodal optimization algorithms. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Optimization

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Research on liquid crystal display technology based on regional dynamic dimming algorithm

Research on liquid crystal display technology based on regio...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Zhitao Yu Peng Sun Mingle Zhou Qianlong Liu Shilong Zhao Hisense Video Technology Co. Ltd Qing Dao China Hisense Visual Technology Co. Ltd Qing Dao China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

In vehicle liquid crystal display (LCD) technology has attracted much attention for its wide range of applications in automotive infotainment systems. However, conventional LCD technologies have limitations in terms of energy consumption and display quality, especially in the context of the increasing demand for high contrast and low energy consumption. To address these challenges, this paper carries out a study of LCD technology based on an area dynamic dimming algorithm. The technology significantly improves the contrast ratio of LCDs and effectively reduces energy consumption by dividing the backlight module into multiple independently adjustable regions and dynamically adjusting the backlight brightness of each region according to the image content. The results show that the region dynamic dimming algorithm proposed in this paper can effectively adjust the brightness of the backlight according to the image content, achieving higher display contrast and lower energy consumption.

关键词： Energy consumption Heuristic algorithms Brightness Light emitting diodes Liquid crystal displays Robustness Entropy Energy efficiency Vehicle dynamics Optimization

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Significance-aware Medication Recommendation with Medication Representation Learning

Significance-aware Medication Recommendation with Medication...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yishuo Li Zhufeng Shao Weimin Chen Shoujin Wang Yuehan Du Wenpeng Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Mental Health Center Jinan China Data Science Institute University of Technology Sydney Sydney Australia Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The goal of medication recommendation system is to recommend appropriate pharmaceutical interventions based on a patient’s diagnosis. Most of existing approaches often formulate these recommendations use data on diagnoses, procedures, and prescriptions accumulated in the electronic health records (EHR), and despite the great successes, they seem to have limitations on modelling the significance of medication to a patient’s current visit and mining fine-grained medication representation information. To address these issues, we propose a novel Significanceaware Medication Recommendation (SMRec) framework built on significance of medication to patients and fine-grained medication representation learning. Specifically, we first design a encoding mechanism to compute significance information of medications for each patient’s visit. Then, we utilize the set-level medication co-occurrence graph based on patients’ medical history which integrates temporal dependency to learn fine-grained medication representations. Experimental results on the publicly available MIMIC-III dataset demonstrate the superior effectiveness of our model compared to other approaches 1 .

关键词： Representation learning Drugs Federated learning Computational modeling MIMICs Encoding History

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Demand Response Scheme: Achieving Sustainability in Distributed Data Processing

Demand Response Scheme: Achieving Sustainability in Distribu...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Xueying Zhai Yunfeng Peng Xiuping Guo Liang Zhang Wei Zhang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Guizhou Key Laboratory of Electric Power Big Data Guizhou Institute of Technology Guizhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. Data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization.

关键词： Renewable energy sources Energy consumption Costs Electricity Simulation Carbon dioxide Spatiotemporal phenomena Internet of Things Sustainable development Optimization

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