Skip to main content

What is machine data?

Machine data is information automatically generated by computer software in the form of a log message, debug message, error message, or status message, as a response to an event. Usuallly the information was generated for the purpose of troubleshooting computer software and computer hardware. Machine data includes informative information about the performance of an operating system from services and processes like package management, resource utilization, and network management.

AWS Logs

Informative data from support service monitoring, alarms and a dashboards for metrics, and can also track security-relevant activities, such as login and logout events.

Authentication

Authentication data can help identify users that are struggling to log in to applications and provide insight into potentially anomalous behaviors, such as activities from different locations within a specified time period.

Firewall

Firewall data can provide visibility into blocked traffic in case an application is having communication problems. It can also be used to help identify traffic to malicious and unknown domains.

Network Statistics

Network statistical data can provide visibility into the network's role in overall availability and performance of critical services. It's also an important source for identifying advanced persistent threats.

System Logs

System logs are key to troubleshooting system problems and can be used to alert security teams to network attacks, a security breach or compromised software.

Web Server

Web logs are critical in debugging web application and server problems, and can also be used to detect attacks, such as SQL injections.

Sensor Data

Sensor data can provide visibility into system performance and support compliance reporting of devices. It can also be used to proactively identify systems that require maintenance.

Comments

Post a Comment

Comments to this blog will be reviewed within 72 hours. No trolling please

Popular posts from this blog

OpenStack+Ceph as Software-Defined Storage

SDS reduces the costs of the management of growing data stores by decoupling storage management from its hardware to allow for centralized management of cheaper, popular commodity hardware. The example SDS ecosystem uses open source software like OpenStack as a front-end interface on top of Ceph as the resource provider of a RADOS cluster of commodity solid-state drives. OpenStack provides user-friendly wrappers for accessing and modifying underlying Ceph storage. OpenStack comes in the form of distributed microservices with RESTful API's: Block (Cinder), File (Manila), Image (Glance), and Object (Swift). Each microservice can scale-out as a cluster of stand-alone services to accommodate the varying demands of high-growth storage. With OpenStack the underlying Ceph storage can address the block storage needs, file storage needs, image storage needs, and object storage needs of datacenters adopting open source as their new norm in an industry trend for high performace and high a...
1 comment
Read more

The meaning of time in reinforcement learning

Reinforcement learning (RL) is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning is concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward through the process of trial and error. In reinforcement learning an agent starts at an empty state then analyzes the available datasets according to a policy of positive states and negative states. Rather than being explicitly taught as in supervised learning the correct set of actions for performing a task, reinforcement learning uses rewards as signals for positive states and punishments as signals for negative states. The agent obtains the best path to a desirable reward as a cumulation of positive states and negative states. As compared to unsupervised learning, reinforcement learning is different in terms of goals. While the goal in unsupervised learning is to find similarities and differences...
Post a Comment
Read more

Principal Component Analysis

Principal Component Analysis (PCA) is a common technique in statistical analysis, widely used for pattern recognition, data compression, image preprocessing, signal-noise analysis, and high resolution spectrum analysis. Principal Component Analysis transforms a group of activites into a set of unique components, where each component has a numerical degree of distance and relatedness from an agreed on centered component. The first component has the largest possible variance (it accounts for most of the variability in the group). Each succeeding component has the highest variance that is orthogonal to the preceding components. The transformation of the group proceeds linearly from a group with a high degree of dimensionality to a group with a low degree of dimensionality of which the components of the group with a low degree of dimensionality are uncorrelated. Principal Component Analysis is also used in the forecast of a most likely outcome through time-series analysis and regress...
Post a Comment
Read more