Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence最新文献_第3页

Bi-LSTM: Finding Network Anomaly Based on Feature Grouping Clustering 基于特征分组聚类的网络异常发现

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426843

Mengbo Xiong, Hui-ya Ma, Zhou Fang, Dong Wang, Qiuyun Wang, Xuren Wang

{"title":"Bi-LSTM: Finding Network Anomaly Based on Feature Grouping Clustering","authors":"Mengbo Xiong, Hui-ya Ma, Zhou Fang, Dong Wang, Qiuyun Wang, Xuren Wang","doi":"10.1145/3426826.3426843","DOIUrl":"https://doi.org/10.1145/3426826.3426843","url":null,"abstract":"Intrusion detection is one of the key technologies to ensure the security of cyberspace. In this paper, a detection model of Bi-LSTM, whose powerful serialization modeling function can discover the time series characteristics from network data, combined with machine learning algorithm K-means is proposed. We know that the data collected by network sensor or audit log has many attributes. In order to achieve a successful classification with low computational cost, it is important to employing the most relevant and discriminating features. How to extract useful information from those attributes to improve detection rate and reduce false detection are challenging. First, we group attributes according to the conditions on which they are collected or more generally, evenly. Then we cluster attributes of each group with K-means. So, we got the same number of hyper-features as the number of the groups. On the one side data reduction is significant and the data volume was greatly declined up to 85%. On the other side, the extracted features, also called hyper features, are more concentrated and informative than the low-level attributes. Detection rate on the high-level features is better than that on original attributes, both with traditional machine learning classification of C4.5 or our hybrid model. The intrusion detection rate of the powerful serialization model, Bi-LSTM based on K-means, is as high as 99.93%, the accuracy rate as high as 98.84%, and the false detection rate is 0. Moreover, experiments show that our Bi-LSTM model plus K-means works well with new attacks only appeared in test data too, which is meaningful for intrusion detection.","PeriodicalId":202857,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133906989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Mahjong-Strategy based on Weighted Restarting Automata 基于加权重启自动机的麻将策略

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426848

Qichao Wang, Yongming Li, Xiaoyin Chen

{"title":"A Mahjong-Strategy based on Weighted Restarting Automata","authors":"Qichao Wang, Yongming Li, Xiaoyin Chen","doi":"10.1145/3426826.3426848","DOIUrl":"https://doi.org/10.1145/3426826.3426848","url":null,"abstract":"Mahjong is a popular and traditional tile-based game in China, which has a history of several hundred years. In general, Mahjong is played by four players, and each player begins with 13 tiles and changes (i.e., draws and discards) tiles in turn, until a player obtains a so-called winning hand consisting of 14 tiles. We are interested in the minimal number of necessary tile changes in order to obtain a winning hand, i.e., the so-called deficiency number. For this purpose, we develop a Mahjong-intelligence by using weighted restarting automata. Originally, restarting automata have been introduced as a formal model of the linguistic technique of analysis by reduction, which can be used to check the correctness of natural language sentence. In order to study quantitative aspects of restarting automata, we introduce the concept of a weighted restarting automaton. Such an automaton is defined as a pair (M, ω), where M is a restarting automaton on some input alphabet Σ, and ω is a weight function that assigns an element of a semiring S to each transition of M. Thus, each weighted restarting automaton defines a function f: Σ* → S that associates an element of S to each input word over Σ. In this work, we will construct a weighted restarting automaton over the tropical semiring that can determine the deficiency number of a hand of Mahjong-tiles.","PeriodicalId":202857,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123174185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

How long will the Service Time in a Ride-Hailing Service? 网约车的服务时间有多长?

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426828

Chaochao Zhu

引用次数: 0

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence 2020年第三届机器学习与机器智能国际会议论文集

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 1900-01-01 DOI: 10.1145/3426826

引用次数: 0