With technologies such as Machine Learning and Deep Neural Networks (DNN), these technologies employ next-generation server infrastructure that spans immense Windows and Linux cluster environments. This Article shows Log Analytics plays a major role in the management of Real-Time and Log Data. Additionally, for DNNs, these application stacks don’t only involve traditional system resources (CPUs, Memory), but also graphics processing units (GPUs). With a nontraditional infrastructure environment, the Microsoft Research Operations team needed a highly flexible, scalable, and Windows and Linux compatible service to troubleshoot and determine causes across the full stack. Log Analytics supports log search through billions of records, Real-Time Analytics Stack metric collection, and rich custom visualizations across numerous sources. These out of the box features paired with the flexibility of available data sources made Log Analytics a great option to produce visibility & insights by correlating across DNN clusters & components.
When Data is mined and organized properly, it provides a wealth of indicators to resolve the most complex issues. Source- The Truth is in the Logs
The relevance of the log file can differ from one person to another. It may be possible that the particular log data can be beneficial for one user but irrelevant for another user. Therefore, the use of log data can be lost inside the large cluster. Therefore, the analysis of the log file is an important aspect these days. With the management of real-time data, the user can use the log file for making decisions. But, as the volume of data increases let's say to gigabytes then, it becomes impossible for the traditional methods to analyze such a huge log file and determine the valid data. By ignoring the log data, a huge gap of relevant information will be created. So, the solution to this Log Analytics problem is to use Deep Learning Neural Network as a training classifier for the log data. With this, it’s not required to read the whole log file data by the human being. By combining the use of log data with Deep Learning, it becomes possible to gain relevant optimum performance and comprehensive operational visibility. Along with the analysis of log data, there is also a need to classify the log file into pertinent and irrelevant data. With this approach, time and performance effort could be saved, and close to accurate results could be obtained. This can only possible with the help of Log Analytics.
Understanding Log Data
Before discussing the analysis of the log file first, we should understand the log file. The log is data that is produced automatically by the system and stores the information about the events that are taking place inside the operating system. It stores the data in every period. The log data can be presented in the form of a pivot table or file. In the log file or table, the records are arranged according to the time. Every software applications and systems produce log files. Some of the examples of log files are transaction log file, event log file, audit log file, server logs, etc. Logs are usually application-specific. Therefore, log analysis of a much-needed task to extract valuable information from the log file.
Log Data Source
Information within the Log Data
Database Management System
It consists of information about the uncommitted transactions, changes made by the rollback operations, and the changes that are not updated in the database. It is performed to retain the ACID (Atomicity, Consistency, Isolation, Durability) property at the time of crashes
Internet Relay Chat (IRC) and Instant Messaging (IM)
In the case of IRC, it consists of server messages during the time interval the user is being connected to the channel. On the other hand, to enable the privacy of the user IM allows storing the messages in encrypted form as a message log. These logs require a password to decrypt and view.
Network Devices such as web servers, routers, switches, printers, etc.
Syslog messages provide the information by where, when, and why, i.e., IP-Address, Timestamp, and the log message. It contains two bits: facility (source of the message) and security (degree of the importance of the log message)
Server Log File
It is created automatically and contains the information about the user in the form of three stages such as IP-Address of the remote server, timestamp and the document requested by the user
It provides details about the interaction between containers, Docker service, and the host machine. By combining these interactions, the cycle of the containers and disruption within the Docker service could be identified.
These logs are sent to Syslog and managed by log level. They are used for monitoring the cluster, auditing records, extracting robust information about the server, and much more.
How To Do Log Analysis?
The steps for the processing of Log Analysis are described below -
Collection and Cleaning of data
Structuring of Data
Analysis of Data
Firstly, Log data is collected from various sources. The collected information should be precise and informative as the type of received data can affect the performance. Therefore, the information should be collected from real users. Each type of Log contains a different kind of information. After the collection of data, the data is represented in the form of a Relational Database Management System (RDMS). Each record is assigned a unique primary key, and the Entity-Relationship model is developed to interpret the conceptual schema of the data. Once the log data is arranged properly then, the process of cleaning of data has to be performed. This is because there can be the possibility of the presence of corrupted log data. The reasons for corruption of log data are given below -
Crashing of the disk where log data is stored
Applications are terminated abnormally
Disturbance in the configuration of input/output
Presence of the virus in the system and much more
Log data is large as well as complex. Therefore, the presentation of log data directly affects their ability to correlate with the other data. An important aspect is that the log data can directly connect to the other log data so that a deep understanding of the log data can be interpreted by the team members. The steps implemented for the structuring of log data are given below -
Clarity about the usage of collected log data
The same assets involve the data so that the values of log data are consistent. This means that naming conventions can be used
Correlation between the objects is created automatically due to the presence of nested files in the log data. It’s better to avoid nested files from the log data.
Now, the next step is to analyze the structured form of log data. This can be performed by various methods such as Pattern Recognition, Normalization, Classification using Machine Learning, Correlation Analysis, and much more.
Importance of Log Analysis
Indexing and crawling are two important aspects. If the content does not include indexing and crawling, then the update of data will not occur properly within time, and the chance of duplicates values will be increased. But, with the use of log analytics, it will be possible to examine the issues of crawling and indexing of data. This can be performed by examining the time taken by Google to crawl the data and at what location Google is spending large time. In the case of large websites, it becomes difficult for the team to maintain the record of changes that are made on the site. With the use of log analysis, updated changes can be retained in the regular period thus helps to determine the quality of the website. In a Business point of view, frequent crawling of the website by Google is an important aspect as it point towards the value of the product or services. Log analytics make it possible to examine how often Google views the page site. The changes that are made on the page site should be updated quickly at that time to maintain the freshness of the content. This can also be determined by the log analysis. Acquiring the real informative data automatically and measuring the level of security within the system.
In today's generation, the volume of data is increasing day by day. Because of these circumstances, there is a great need to extract useful information from large data that are further use for making decisions. Knowledge Discovery and Data Mining are used to solve this problem, these are two distinct terms. Knowledge Discovery is a kind of process used for extracting useful information from the database. Whereas, Data Mining is one of the steps involved in this process. Data Mining is the algorithm used for extracting the patterns from the data. Knowledge Discovery involves various steps such as Data Cleaning, Data Integration, Data Selection, Data Transformation, Data Mining, Pattern Evaluation, Knowledge Presentation. Knowledge Discovery is a process that has total focus on driving the useful information from the database, interpretation of storage mechanism of data, implementation of optimum algorithms, and visualization of results. This process gives more importance to finding the understandable patterns of evidence that further used for grasping useful information. Data Mining involves the extraction of patterns and fitting of the model. The concept behind the fitting of the model is to ensure what type of information is inferred from the processing of the model. It works on three aspects such as model representation, model estimation, and search. Some of the common Data Mining techniques are Classification, Regression, and Clustering.
Log Data Mining
After performing an analysis of logs, now the next step is to perform log mining. Log Mining is a technique that uses Data Mining for the analysis of logs. With the introduction of the Data Mining technique for log analysis the quality of analysis of log data increases. In this way the analytics approach moves towards software and automated analytic systems. But, there are few challenges to perform log analysis using data mining. These are -
Day by day volume of log data is increasing from megabytes to gigabytes or even petabytes. Therefore, there is a need for advanced tools for log analysis.
The essential information is missing from the log data. So, more efforts are needed to extract useful data.
The different numbers of logs are analyzed from various sources to move deep into the knowledge. So, logs in various formats have to be analyzed.
The presence of different logs creates the problem of redundancy of data without any identification. This leads to the question of synchronization between the sources of log data.
As shown in Fig the process of log mining consist of three phases. Firstly, log data is collected from various sources like Syslog, Message log, etc. After receiving the log data, it is aggregated together using Log Collector. After aggregation second phase is started. In this, data cleaning is performed by removing irrelevant data or corrupted data that can affect the accuracy of the process. After cleaning, log data is represented in the structured form of data (Integrated form) so that queries could be executed on them. After that, the transformation process is performed to convert into the required format for performing normalization and pattern analysis. Useful patterns are obtained by performing Pattern Analysis. Various data mining techniques are used such as Association Rules, Clustering, etc. to grasp the relevant information from the patterns. This information is used for decision-making and for alerting the unusual behavior of the design by the organization.
What is Network Security Automation
The security of the network should be maintained automatically by the machine. But, if it is performed by the human being then, it will become unfavorable for the organization. Therefore, optimum usage of automation helps in controlling network security. Apache Open Source Projects Started For Real-time Log Analytics and Network Security
Developing and implementing the protection layer faster than the attackers
Failures within the network.
The infrastructure used for developing automated network security is described below - The system provides various services such as alerting of threat event in real time, Feature Engineering, and better data integration layer. Firstly, all the relevant data for analyzing is taken into account and collected by the buffer of the corresponding system. When the data is ingested into the buffer, the engine of the system starts working. After buffering the data, data is normalized in the standard format so that messages from different topology can correlate with the data. After that quality of normalized data is improved. For example, the ip_address attribute can be enhanced by providing detailed information about host_id details. Then, information is retrieved from the enriched data to examine the threat events. Whenever the threat event is detected, a message will be displayed as an alert. This means that the labeling of threat events is performed in the form of messages. All these processes are performed by the system engine in real-time. After alerting of threat event, all the labeled events are stored in Apache Hadoop for long-term storage and further usage for the next generation analysis process.
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Log Analytics Services
Get Real-Time Insights into Machine Data. XenonStack Log Analytics Services monitors, aggregates, indexes, and analyzes all the log data from your infrastructure. Collect and correlate the data from multiple sources with smart Analytics using Machine Learning and Deep Learning.