Lstm Based Short Message Service(SMS) Modeling for Spam Clas

发布时间：2023-04-25 21:53

　　短信服务(SMS)在现代通信技术中得到了广泛的推广。短消息服务组件是现代社会中最快、最常用的电子消息发送方法。垃圾邮件或未经请求的短信已成为组织、网络系统和私人客户端的一个值得注意的问题。通过垃圾短信,垃圾邮件发送者正在影响时间和内存空间,这是计算世界中最重要的资产。垃圾邮件的分类是一个有趣而突出的问题。这里介绍了与垃圾邮件相关的问题以及努力管理垃圾邮件的不同方法。对SMS中的垃圾邮件可用性进行分类是一项具有挑战性的任务,因此,在这方面已经进行了大量的研究,这些研究采用了机器学习技术,如朴素Bayes(NB)、随机森林(RF)和支持向量机(SVM),用于垃圾邮件分类。虽然这些方法表现出了足够的性能,但在垃圾邮件分类方面效率不够。因此,需要进行严格的研究,以找到更准确、更稳健的方法。为了解决这个问题,我们提出了一种新的长期短期记忆(LSTM)方法,它是一种具有包括记忆细胞在内的门控机制的递归神经网络(RNN)的高级结构。此外,本研究还采用了Word2Vec工具,该工具将简化文本转换为向量空间中单词的表示形式。为了评估我们的方法的有效性,SMS数据集已被免费使用。实验结果表明,该方法优于最...

【文章页数】：52 页

【学位级别】：硕士

【文章目录】：
摘要
Abstract
1 Introduction
    1.1 What is Spam
    1.2 Spamming Motivations
    1.3 Research Background and Significance
        1.3.1 Research Background
        1.3.2 Research Significance
    1.4 Overseas and Domestic Research Progress
        1.4.1 Spam Detection in Short Message Service(SMS)
        1.4.2 Spam Detection in Email
    1.5 Main Contents and Structure of the Thesis
        1.5.1 Main Contents
        1.5.2 The Structure of the Thesis
    1.6 Summary
2 Basic Theory and Related Work
    2.1 Basic Theory of Machine Learning
        2.1.1 Unsupervised Learning
        2.1.2 Supervised Learning
    2.2 Spam Filtering Techniques
        2.2.1 Machine Learning Approach to Spam Filtering
        2.2.2 Artificial Neural Network
        2.2.3 Deep Neural Network
    2.3 Spam Filtering Challenges for Machine Learning
        2.3.1 False Positive
        2.3.2 Concept Drift Handling in SMS
        2.3.3 E-mail Ranking or Prioritizing
    2.4 Summary
3 Experimental Model
    3.1 Proposed method LSTMs
    3.2 Word Embedding
    3.3 Word2Vec
        3.3.1 Skip-gram Model
        3.3.2 Continuous Bag-of-Words(CBOW)Model
    3.4 Data Set
    3.5 Traditional Baseline Methods
        3.5.1 SVM(Support Vector Machine)
        3.5.2 Decision Tree
        3.5.3 KNN(K-Nearest Neighbors)
        3.5.4 Random Forest
        3.5.5 NB(Na?ve Bayes)
    3.6 Summary
4 Results and Discussions
    4.1 Spam Detection Framework
        4.1.1 Detecting Strategy
        4.1.2 Contributions
        4.1.3 Data Preprocessing
    4.2 Comparative Study of Results
        4.2.1 Comparative Results
        4.2.2 Detecting Results
5 Conclusion and Future Work
References
Research Projects and Publications in Master Study
Acknowledgement



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