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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wenhan Hou Bo Cui Yongxin Chen Ru Li Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing College of Computer Science Inner Mongolia University Hohhot China

Phishing scams have become one of the primary frauds on Ethereum, leading to substantial financial losses for users. Therefore, developing an effective phishing detection method for Ethereum holds significant importance for the blockchain ecosystem. However, the heterogeneity of Ethereum poses challenges when it comes to detecting phishing scams. In this paper, we propose a Curriculum Learning-based approach (CLPSD) for Ethereum phishing detection. We collect transaction records to build a graph. In order to distinguish the varying degrees of contribution among nodes, we design a difficulty measurer combining Local Outlier Factor and information entropy. Thus, CLPSD utilizes Graph Convolutional Network to initially learn from easy samples and progressively advance to more complex ones. The experimental results demonstrate that CLPSD outperforms existing methods, highlighting the superior performance of our approach and its significant relevance in enhancing the Ethereum ecosystem.

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Differential Privacy Frequent Closed Itemset Mining over Data Stream

Differential Privacy Frequent Closed Itemset Mining over Dat...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Xuebin Ma Shengyi Guan Yanan Lang Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing Inner Mongolia University Hohhot China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

As the most common technique for mining and analyzing massive data, frequent itemset mining is widely used in various scenarios. However, when the data contains sensitive information, it will bring serious privacy leakage risks to mining and publishing it directly. Therefore, how to efficiently mine frequent itemset without privacy disclosure is a hot issue at present. In data stream, because the data between adjacent timestamps possess certain correlation, which makes it easier to leak privacy for frequent itemset mining in the data stream, and considering that frequent itemset will have combinatorial explosion problem in data stream, frequent closed itemset mining in the data stream will be a better choice. In this paper, we propose a differential privacy algorithm for mining frequent closed itemset in data stream, which is referred to as DPES. The algorithm uses a vertical mining method in each sliding window, which can quickly obtain the frequent closed itemset contained in the current window. Besides, to improve the data utility, we also design an adaptive privacy budget allocation strategy by computing the difference to decide whether the current timestamp should publish a low-noise statistic or an approximate statistic. Finally, we demonstrate that the DPES algorithm satisfies ε-difference privacy by privacy analysis, and the experimental results on several real datasets also show the effectiveness of the DPES algorithm.

关键词： Differential privacy Privacy Correlation Itemsets Publishing Noise Approximation algorithms

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Seed Node Selection Algorithm Based on Node Influence in Opportunistic Offloading 28

Seed Node Selection Algorithm Based on Node Influence in Opp...

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28th IEEE Symposium on Computers and Communications, ISCC 2023

作者： Tang, Qi Hang, Ruijie Xu, Gang Zhang, Gaofeng Li, Shuai Huang, Baoqi College of Computer Science Inner Mongolia University Hohhot010021 China Inner Mongolia A.R. Key Laboratory of Wireless Networking and Mobile Computing Hohhot010021 China Ordos Dongsheng Manshi Middle School Ordos017000 China

ISBN: (纸本)9798350300482

As a kind of mobile traffic offloading technology, opportunistic triage downloads and distributes data through seed nodes. Therefore, how to efficiently and accurately select suitable seed nodes becomes a key problem of opportunistic streaming technology. To address the problem of insufficient research on seed node content coverage in existing studies, this paper combines the characteristics of opportunity networks and the solution idea of the influence maximization problem and proposes the evaluation model of node influence for the first time. Then proposes a seed node selection algorithm (SNSNI) based on node influence on this basis. Experimental results show that the set of seed nodes selected by SNSNI algorithm can obtain smaller average message transmission delay and more covered nodes in the triage scenario compared with the random algorithm, and fewer seed nodes are required to achieve the same effect. © 2023 IEEE.

关键词： mobile traffic offloading node influence opportunistic triage seed node

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2-Hop Routing Strategy for Overlapping Communities based on Social Intimacy 25

2-Hop Routing Strategy for Overlapping Communities based on ...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Liu, Zhanguo Xu, Gang Cheng, Yuan Huang, Baoqi Bai, Xiangyu Inner Mongolia A.R. Key Laboratory of Wireless Networking and Mobile Computing Hohhot010021 China Inner Mongolia University College of Computer Science Hohhot010021 China

ISBN: (纸本)9781665405270

In an opportunistic network with social attributes, the selection of relay nodes is mainly done according to community division. In the social opportunity network, community partition is the key to reducing data forwarding delay and improving the success rate of data delivery. To solve the problem that existing opportunistic network routing algorithms do not analyze the impact of overlap and transitivity of community on opportunistic routing, a 2-hop routing strategy for overlapping communities based on social intimacy(TOCSI) is proposed. The algorithm first introduces the overlapping community division method PercoMCV to reasonably divide the node community structure and designs the opportunity route based on the result of the community division. Experiments show that compared with Epidemic, Prophet, and ORRF algorithms, TOCSI can effectively improve the success rate of message delivery and reduce the average data forwarding delay and routing overhead. © 2022 IEEE.

关键词： Routing algorithms

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GA-SVR Traffic Flow Prediction Based on Phase Space Reconstruction with Improved KNN Method 25

GA-SVR Traffic Flow Prediction Based on Phase Space Reconstr...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Li, Jia Xu, Gang Huang, Baoqi Bai, Xiangyu Inner Mongolia A.R. Key Laboratory of Wireless Networking and Mobile Computing Hohhot010021 China College of Computer Science Inner Mongolia University Hohhot010021 China

ISBN: (纸本)9781665405270

Traffic flow prediction plays an important role in intelligent traffic management. In order to solve the problem that the prediction model has low accuracy in traffic flow prediction when the amount of data is small. Considering the chaotic nature of the traffic flow time series data, the phase space reconstruction method is adopted to process the data, and then the SVR model is used to predict the processed data. The two parameters of phase space reconstruction, including embedding dimension and delay time, have a great impact on the final prediction accuracy. In this paper, the improved KNN method is used to select the parameters of phase space reconstruction and construct the KNN-GA-SVR model. Compared with the CC-GA-SVR model of phase space parameter selection based on C-C method, this model improves the prediction accuracy, is effective and feasible for the prediction of short-term traffic flow, and has strong applicability. © 2022 IEEE.

关键词： Support vector machines

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Short-Time Traffic Flow Prediction Based on K-means++ and LSTM 20

Short-Time Traffic Flow Prediction Based on K-means++ and LS...

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20th IEEE International Symposium on Parallel and Distributed Processing with Applications, 12th IEEE International Conference on Big Data and Cloud computing, 12th IEEE International Conference on Sustainable computing and Communications and 15th IEEE International Conference on Social computing and networking, ISPA/BDCloud/SocialCom/SustainCom 2022

作者： Li, Jia Xu, Gang Cheng, Yuan Huang, Baoqi Inner Mongolia A.R. Key Laboratory of Wireless Networking and Mobile Computing Hohhot010021 China College of Computer Science Inner Mongolia University Hohhot010021 China

ISBN: (纸本)9781665464970

With the gradual increase in the number of vehicles owned by people, the problem of urban traffic congestion is becoming more and more serious. Timely and accurate traffic flow prediction helps travel vehicles to plan their routes reasonably, avoid congested roads and reduce environmental pollution. In order to take full advantage of the daily periodicity of the traffic flow of a road section, K-means++ with improved initial clustering centroids is applied to analyze the similarity of the daily traffic flow patterns and classify the traffic flow patterns with day as the time interval. One can separate the traffic patterns affected by different factors, and the other can exclude individual days containing abnormal data trends formed by unexpected road conditions, which can save the deep learning model from the interference of other irrelevant data features. Finally, a Long Short-Term Memory (LSTM) model is combined to predict the traffic flow value at the next time point based on the data of the previous hour. In the experiment, the K-means-LSTM model can significantly reduce the prediction error of short-time traffic flow and has better prediction performance by comparing with the prediction results of the support vector machine (SVR) model optimized by genetic algorithm (GA) and the LSTM model. © 2022 IEEE.

关键词： Long short-term memory

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Investigation on Analog and Digital Modulations Recognition Using Machine Learning Algorithms

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World Journal of Engineering and Technology 2024年第4期12卷 867-884页

作者： Jean Ndoumbe Ivan Basile Kabeina Gaelle Patricia Talotsing Soubiel-Noël Nkomo Biloo Laboratory of Computer Engineering Data Science and Artificial Intelligence National Higher Polytechnic School of Douala Douala Cameroon Mobile Computing and Networking Research Laboratory Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada

In the field of radiocommunication, modulation type identification is one of the most important characteristics in signal processing. This study aims to implement a modulation recognition system on two approaches to machine learning techniques, the K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN). From a statistical and spectral analysis of signals, nine key differentiation features are extracted and used as input vectors for each trained model. The feature extraction is performed by using the Hilbert transform, the forward and inverse Fourier transforms. The experiments with the AMC Master dataset classify ten (10) types of analog and digital modulations. AM_DSB_FC, AM_DSB_SC, AM_USB, AM_LSB, FM, MPSK, 2PSK, MASK, 2ASK, MQAM are put forward in this article. For the simulation of the chosen model, signals are polluted by the Additive White Gaussian Noise (AWGN). The simulation results show that the best identification rate is the MLP neuronal method with 90.5% of accuracy after 10 dB signal-to-noise ratio value, with a shift of more than 15% from the k-nearest neighbors’ algorithm.

关键词： Automatic Recognition Artificial Neural Networks K-Nearest Neighbors Machine Learning Analog Modulations Digital Modulations

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Progress and Challenges of Polymorphic Smart Networks

Progress and Challenges of Polymorphic Smart Networks

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Asia-Pacific Network Operations and Management Symposium (APNOMS)

作者： Peixin Liu Xiangyu Bai Zhaoran Wang Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing College of Computer Inner Mongolia University Hohhot China

The current Internet infrastructure and technology system faces major challenges in intelligent, diversified, personalized, highly robust, efficient and other aspects, and it is urgent to reform the network infrastructure. Polymorphic smart network (PINet), as an open network architecture based on full-dimensional definable technology, has been studied by more and more scholars. This paper first introduces the architecture of polymorphic smart network, then introduces the key technologies and related research of polymorphic smart network, then describes its simulation environment, and finally expounds the existing problems and possible breakthrough technologies and key points of PInet.

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Review of Ecological Environment Monitoring Methods of Northern Wetland in Inner Mongolia Based on WSN

Review of Ecological Environment Monitoring Methods of North...

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IEEE International Conference on Communication and Information Systems (ICCIS)

作者： Wenyu Li Xiangyu Bai Kexin Zhou Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing College of Computer Inner Mongolia University Hohhot China

Wetland ecosystem is a special ecosystem, which refers to the area where water and land intersect, including swamps, rivers, lakes, estuaries, bays, etc. These areas usually have special biodiversity and ecological functions, which are of great significance to both human beings and the natural environment. In recent years, the northern wetland ecosystem of Inner Mongolia with Hulun Lake, Wuliangsuhai Lake and Daihai Lake as the core, is facing the problems of reduced water area, poor water quality and serious pollution due to natural and human factors, which has begun to affect other environmental changes and hindered social and economic development. It is urgent to monitor the ecological environment and control pollution in relevant areas. The core of environmental monitoring is data collection. The traditional data acquisition methods have some defects, such as high cost and low efficiency. This paper proposes to use wireless sensor network to carry out long-term automatic monitoring of ecological environment in this area, and summarizes the relevant experience and technology.

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An Overview of Intelligent Algorithm-Based Routing in Delay Tolerant Networks

An Overview of Intelligent Algorithm-Based Routing in Delay ...

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International Conference on Computers, Communications, and Systems (ICCCS)

作者： Lei Yao Xiangyu Bai Zhaoran Wang Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing College of Computer Inner Mongolia University Hohhot China

Delay tolerant network (DTN) is a new paradigm with the development of wireless technology and mobile Ad-hoc network (MANET). Unlike the stable end-to-end connection in the traditional network, the storage - carry - forward mode is adopted in DTN. Routing optimization is the most basic problem in delay tolerant network. It assumes the heavy responsibility of data transmission, and even determines the performance of the whole network. In recent years, with the development of artificial intelligence, there have been many researches on the use of AI to optimize the routing in DTN. However, the relevant literature is insufficient at present. In this paper, we first introduce the methods related to artificial intelligence, and then summarize and discuss the existing literature from five parts: machine learning classifier, prediction task using machine learning, biological heuristic method, reinforcement learning and artificial intelligence assistance.

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