Study Guide 300 215 Cbrfir Conducting Forensic Analysis And Incident Response Using Cisco Technologies For Cybersecurity Exam
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Study Guide - 300-215 CBRFIR: Conducting Forensic Analysis and Incident Response Using Cisco Technologies for Cybersecurity Exam
The 300-215 CBRFIR exam focuses on conducting forensic analysis and incident response using Cisco technologies to effectively detect, investigate, and respond to cybersecurity incidents. This certification covers a comprehensive range of topics, beginning with foundational concepts of digital forensics and incident response, including the principles and phases of incident handling such as preparation, identification, containment, eradication, recovery, and lessons learned. Legal considerations and maintaining the chain of custody for digital evidence are emphasized to ensure integrity and compliance. The guide delves into forensic techniques and procedures encompassing data collection, memory and disk forensics, network forensics, and log and artifact analysis, supported by hashing and imaging techniques for preserving evidence. Endpoint-based analysis teaches how to identify host-based indicators, analyze registries, file systems, running processes, and use Cisco Secure Endpoint (AMP) for malware detection and behavioral analysis. Network-based analysis focuses on packet capture, protocol analysis, anomaly detection, and leveraging Cisco Secure Network Analytics (Stealthwatch) and NetFlow telemetry for threat detection. The importance of analyzing alert data and logs through normalization, correlation, and utilizing tools like Cisco SecureX and SIEMs is highlighted. Threat hunting and intelligence integration explain methodologies for IOC enrichment, using threat intelligence platforms, open-source intelligence, and Cisco’s Threat Grid and Talos. The use of Cisco tools such as AMP, Threat Grid, Stealthwatch, and SecureX for forensics and incident response is covered thoroughly. Finally, the guide outlines incident response playbooks, automation, best practices, compliance standards, and post-incident activities to ensure efficient and effective cybersecurity operations, supported by real-world scenarios and practice questions to reinforce learning.
JNCIA: Juniper Networks Certified Internet Associate Study Guide
Author: Joseph M. Soricelli
language: en
Publisher: John Wiley & Sons
Release Date: 2006-02-20
Here's the book you need to prepare for the JNCIA exam, JN0-201, from Juniper Networks. Written by a team of Juniper Network trainers and engineers, this Study Guide provides: Assessment testing to focus and direct your studies In-depth coverage of official test objectives Hundreds of challenging practice questions, in the book and on the CD Authoritative coverage of all test objectives, including: Working with the JUNOS software Implementing Juniper Networks boot devices Troubleshooting Routing Information Protocol Implementing a routing policy Configuring and monitoring an OSPF Network Implementing Border Gateway Protocol Monitoring and troubleshooting an IS-IS network Understanding the Reverse Path Forwarding process Operating firewall filters Using Multiprotocol Label Switching Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.
Modeling Techniques in Predictive Analytics
Today, successful firms compete and win based on analytics. Modeling Techniques in Predictive Analytics brings together all the concepts, techniques, and R code you need to excel in any role involving analytics. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business challenges and business cases, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and even spatio-temporal data. For each problem, Miller explains why the problem matters, what data is relevant, how to explore your data once you’ve identified it, and then how to successfully model that data. You’ll learn how to model data conceptually, with words and figures; and then how to model it with realistic R programs that deliver actionable insights and knowledge. Miller walks you through model construction, explanatory variable subset selection, and validation, demonstrating best practices for improving out-of-sample predictive performance. He employs data visualization and statistical graphics in exploring data, presenting models, and evaluating performance. All example code is presented in R, today’s #1 system for applied statistics, statistical research, and predictive modeling; code is set apart from other text so it’s easy to find for those who want it (and easy to skip for those who don’t).