This package includes all labs in the Network Monitoring and Reconnaissance category, as well as all new labs in the category released during your subscription period.

Specific prerequisites vary by lab, but generally include basic knowledge of TCP/IP networking and network setup principles.

EXPECTED DURATION

12 hours, self-paced. Pause and continue at any time.
12 CPEs awarded on successful completion.

• Identifying Live Machines and Services on an Unknown Network

  Students will use tools such as nmap, unicornscan, and fping to identify systems on a local network, including both Unix and Windows targets. Students will identify the operating systems these systems are running, as well as the types of network services they are providing.

• Service Identification I

  Students will use multiple tools to identify services, including software package and version information, running on unknown systems. Network services to be targeted will include those running on non-standard ports or behind firewall rules.

• Service Identification II

  Students will build on the Service Identification I exercise to use service-specific information-gathering tools. Students will gather vendor, software, and version information, as well as any configuration information available remotely. Students will then use scripting tools to automate this process.

• Log Analysis with RSYSLOG

  This lab teaches students to setup and configure a central RSYSLOG server that will receive and store logs from FreeBSD, Linux and Windows clients.

  Students will learn to configure log forwarding on the clients, and log rotation and filtering on the server. They will also learn to use Logwatch to analyze logs and fail2ban to automatically respond to suspicious activity found in the logs.

• Log Analytics with Splunk

  In this lab the student will learn how to configure and securely run the Splunk Enterprise security information collection and analysis platform. The objective of the lab is to deploy multiple instances of Splunk data forwarders through a deployment server and analyze the logs received from the servers. The student will write custom scripts to generate logs, create both visual and textual reports, organize these reports into a single dashboard, and learn to recognize malicious activity.

• Log Analytics with Elastic Stack

  Elastic Stack is a group of services designed to take data from almost any type of source and in almost any type of format, and to search, analyze and visualize that data in real time. In this lab, Elastic Stack will be used for log analytics. Students will learn to set up and run the Elasticsearch, Logstash and Kibana components of Elastic Stack. Multiple computers in a small network will forward their logs to a central server where they will be processed by Elastic Stack. Student will use Kibana to view logs, filter them and set up dashboards. Information in the logs will be used to identify and block an on-going attack.