The Experts below are selected from a list of 53349 Experts worldwide ranked by ideXlab platform
Evangelos E Milios - One of the best experts on this subject based on the ideXlab platform.
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a lightweight algorithm for message type extraction in system Application Logs
IEEE Transactions on Knowledge and Data Engineering, 2012Co-Authors: Adetokunbo Makanju, A N Zincirheywood, Evangelos E MiliosAbstract:Message type or message cluster extraction is an important task in the analysis of system Logs in computer networks. Defining these message types automatically facilitates the automatic analysis of system Logs. When the message types that exist in a Log file are represented explicitly, they can form the basis for carrying out other automatic Application Log analysis tasks. In this paper, we introduce a novel algorithm for carrying out message type extraction from event Log files. IPLoM, which stands for Iterative Partitioning Log Mining, works through a 4-step process. The first three steps hierarchically partition the event Log into groups of event Log messages or event clusters. In its fourth and final stage, IPLoM produces a message type description or line format for each of the message clusters. IPLoM is able to find clusters in data irrespective of the frequency of its instances in the data, it scales gracefully in the case of long message type patterns and produces message type descriptions at a level of abstraction, which is preferred by a human observer. Evaluations show that IPLoM outperforms similar algorithms statistically significantly.
Adetokunbo Makanju - One of the best experts on this subject based on the ideXlab platform.
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a lightweight algorithm for message type extraction in system Application Logs
IEEE Transactions on Knowledge and Data Engineering, 2012Co-Authors: Adetokunbo Makanju, A N Zincirheywood, Evangelos E MiliosAbstract:Message type or message cluster extraction is an important task in the analysis of system Logs in computer networks. Defining these message types automatically facilitates the automatic analysis of system Logs. When the message types that exist in a Log file are represented explicitly, they can form the basis for carrying out other automatic Application Log analysis tasks. In this paper, we introduce a novel algorithm for carrying out message type extraction from event Log files. IPLoM, which stands for Iterative Partitioning Log Mining, works through a 4-step process. The first three steps hierarchically partition the event Log into groups of event Log messages or event clusters. In its fourth and final stage, IPLoM produces a message type description or line format for each of the message clusters. IPLoM is able to find clusters in data irrespective of the frequency of its instances in the data, it scales gracefully in the case of long message type patterns and produces message type descriptions at a level of abstraction, which is preferred by a human observer. Evaluations show that IPLoM outperforms similar algorithms statistically significantly.
A N Zincirheywood - One of the best experts on this subject based on the ideXlab platform.
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a lightweight algorithm for message type extraction in system Application Logs
IEEE Transactions on Knowledge and Data Engineering, 2012Co-Authors: Adetokunbo Makanju, A N Zincirheywood, Evangelos E MiliosAbstract:Message type or message cluster extraction is an important task in the analysis of system Logs in computer networks. Defining these message types automatically facilitates the automatic analysis of system Logs. When the message types that exist in a Log file are represented explicitly, they can form the basis for carrying out other automatic Application Log analysis tasks. In this paper, we introduce a novel algorithm for carrying out message type extraction from event Log files. IPLoM, which stands for Iterative Partitioning Log Mining, works through a 4-step process. The first three steps hierarchically partition the event Log into groups of event Log messages or event clusters. In its fourth and final stage, IPLoM produces a message type description or line format for each of the message clusters. IPLoM is able to find clusters in data irrespective of the frequency of its instances in the data, it scales gracefully in the case of long message type patterns and produces message type descriptions at a level of abstraction, which is preferred by a human observer. Evaluations show that IPLoM outperforms similar algorithms statistically significantly.
Dina Angela - One of the best experts on this subject based on the ideXlab platform.
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sistem pengawasan kinerja jaringan server web apache dengan Log management system elk elasticsearch Logstash kibana
Jurnal Telematika, 2018Co-Authors: Claudia Tarigan, Ventje Jeremias Lewi Engel, Dina AngelaAbstract:A server is a software that has a duty and responsibility to provide information to the web. Any process that occurs within the web server will be recorded in a Log. A Log is a file that contains a list of actions, events (activities) that have been going on in a computer system. By using the Log files on a web server, a lot of things that can be done by system administrators to monitor the performance of the web server. However, the Log on the web server are difficult to understand and read. Log management system is a system that can handle data services Log in large numbers and generate detailed Log information. Logstash is the Application Log management system that can help the system administrator in the performance monitor Log of the web server. Logstash will be combined with the Elasticsearch serves as data storage media and Kibana as visualization. ELK (Elasticsearch, Logstash, Kibana) is used to display and keep an eye on the Log data from web server so, system administrators can know the performance of the web server. Server sebagai penyedia layanan di jaringan memastikan agar semua aktivitas yang berkaitan dengannya dicatat dalam Log. Log adalah file yang berisi daftar aktivitas dan waktu yang terjadi dalam server. Pemantauan kinerja server dengan memanfaatkan Log akan meningkatkan ketahanan dan tingkat layanan sebuah server. Masalahnya adalah Log yang tercatat tersebar di beberapa tempat dan susah untuk langsung diolah karena bentuknya bukan seperti tabel. Log management system dapat menangani data Log dalam jumlah besar, menghasilkan detail informasi aktivitas, dan visualisasi data Log yang lebih mudah dimengerti pengguna. Perangkat lunak ELK (Elasticsearch, Logstash, Kibana) bisa dikombinasikan dan diintegrasikan dalam server web untuk mengelola, manampilkan, dan mengawasi data-data Log yang ada sehingga administrator jaringan dapat mengetahui kinerja yang terdapat pada server web. Implementasi dan uji coba menunjukkan pengawasan kinerja server web Apache yang lebih mudah dimengerti pengguna.
Duan Jua - One of the best experts on this subject based on the ideXlab platform.
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research and design of security audit Log system based on web Application
Netinfo Security, 2014Co-Authors: Duan JuaAbstract:In recent years, with the Web Applications technoLogy continuing to progress and develop, there are more and more demands development about Web Application services, and then the attendant Web Application security attacks are also on the rise. The technical means for cyber attacks are endless at present, but they are generally pre-detection and deal with things in the progress, the corresponding post-detection for less maintenance. In the network center, there are a large number of the server's equipments, Web Log fi les as part of the server detail a variety of events happening every day of equipment system, such as client access to the server request records, hacker intrusion on the site records, and so on. Therefore, in order to effectively manage the maintenance of equipment and timely reduction in the risk of attacks, analyze audit Log for later inspection and maintenance of safety equipment is necessary. Based on this, mainly research and design of security audit Log system based on Web Application, Log audit system consists of three subsystems: the subsystem of Log acquisition, the subsystem of analysis engine and the subsystem Log alarm. The subsystem of Log acquisition uses multiprotocol analysis to collect Log, and to process the corresponding Log normalization and de-emphasis. The subsystem of analysis engine uses the rule base and mathematical statistics method to extract the Log feature and set the appropriate statistic parameters, and then to do the comparative analysis. The subsystem Log alarm is the main confi guration tasks appropriate policy and issued for the audit results show interface, or generate reports and send messages to users.