Many alerts place an unnecessary burden on Ops teams instead of helping them solve issues. This presentation describes the phenomenon and four ways to address it.
A Practical Guide to Anomaly Detection for DevOpsBigPanda
Recent years have seen an explosion in the volumes of data that modern production environments generate. Making fast educated decisions about production incidents is more challenging than ever. BigPanda's team is passionate about solutions such as anomaly detection that tackle this very challenge.
CNIT 152 12 Investigating Windows Systems (Part 1 of 3)Sam Bowne
This document provides an overview of analyzing the Windows NTFS file system for digital forensics investigations. It discusses the Master File Table (MFT) structure, how it tracks file metadata including timestamps, and how to recover deleted files. Tools for examining the MFT such as Velociraptor and WinHex are presented. Other Windows artifacts covered include Prefetch files, event logs, scheduled tasks, and volume shadow copies. The document provides technical details on these elements to help explain how Windows tracks files and how this data can be used for investigations.
CNIT 152: 4 Starting the Investigation & 5 LeadsSam Bowne
Slides for a college course based on "Incident Response & Computer Forensics, Third Edition" by by Jason Luttgens, Matthew Pepe, and Kevin Mandia, at City College San Francisco.
Website: https://samsclass.info/152/152_F18.shtml
Keeping the Pulse of Your Data: Why You Need Data Observability Precisely
With the explosive growth of DataOps to drive faster and better-informed business decisions, proactively understanding the health of your data is more important than ever. Data observability is one of the foundational capabilities of DataOps and an emerging discipline used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered.
Join Paul Rasmussen and Shalaish Koul from Precisely, to learn how data observability can be used as part of a DataOps strategy to prevent data issues from wreaking havoc on your analytics and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence.
Topics you will hear addressed in this webinar:
Data observability – what is it and how it is different from other monitoring solutions
Why now is the time to incorporate data observability into your DataOps strategy
How data observability helps prevent data issues from impacting downstream analytics
Examples of how data observability can be used to prevent real-world issues
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...Amazon Web Services
"This is a technical architect's case study of how Loggly has employed the latest social-media-scale technologies as the backbone ingestion processing for our multi-tenant, geo-distributed, and real-time log management system. This presentation describes design details of how we built a second-generation system fully leveraging AWS services including Amazon Route 53 DNS with heartbeat and latency-based routing, multi-region VPCs, Elastic Load Balancing, Amazon Relational Database Service, and a number of pro-active and re-active approaches to scaling computational and indexing capacity.
The talk includes lessons learned in our first generation release, validated by thousands of customers; speed bumps and the mistakes we made along the way; various data models and architectures previously considered; and success at scale: speeds, feeds, and an unmeltable log processing engine."
CNIT 121: 12 Investigating Windows Systems (Part 1 of 3)Sam Bowne
Slides for a college course based on "Incident Response & Computer Forensics, Third Edition" by by Jason Luttgens, Matthew Pepe, and Kevin Mandia.
Teacher: Sam Bowne
Twitter: @sambowne
Website: https://samsclass.info/121/121_F16.shtml
This document discusses the benefits of a top-down approach to monitoring systems compared to a bottom-up approach. It provides examples of companies like Netflix and GitHub that use key performance indicators (KPIs) and high-level metrics to monitor overall system health from the top-down rather than monitoring individual components from the bottom-up. The document also discusses how BigPanda uses a pipeline latency metric as a KPI to monitor the reliability and performance of its unified monitoring dashboard.
Data pipelines observability: OpenLineage & MarquezJulien Le Dem
This document discusses OpenLineage and Marquez, which aim to provide standardized metadata and data lineage collection for data pipelines. OpenLineage defines an open standard for collecting metadata as data moves through pipelines, similar to metadata collected by EXIF for images. Marquez is an open source implementation of this standard, which can collect metadata from various data tools and store it in a graph database for querying lineage and understanding dependencies. This collected metadata helps with tasks like troubleshooting, impact analysis, and understanding how data flows through complex pipelines over time.
A Practical Guide to Anomaly Detection for DevOpsBigPanda
Recent years have seen an explosion in the volumes of data that modern production environments generate. Making fast educated decisions about production incidents is more challenging than ever. BigPanda's team is passionate about solutions such as anomaly detection that tackle this very challenge.
CNIT 152 12 Investigating Windows Systems (Part 1 of 3)Sam Bowne
This document provides an overview of analyzing the Windows NTFS file system for digital forensics investigations. It discusses the Master File Table (MFT) structure, how it tracks file metadata including timestamps, and how to recover deleted files. Tools for examining the MFT such as Velociraptor and WinHex are presented. Other Windows artifacts covered include Prefetch files, event logs, scheduled tasks, and volume shadow copies. The document provides technical details on these elements to help explain how Windows tracks files and how this data can be used for investigations.
CNIT 152: 4 Starting the Investigation & 5 LeadsSam Bowne
Slides for a college course based on "Incident Response & Computer Forensics, Third Edition" by by Jason Luttgens, Matthew Pepe, and Kevin Mandia, at City College San Francisco.
Website: https://samsclass.info/152/152_F18.shtml
Keeping the Pulse of Your Data: Why You Need Data Observability Precisely
With the explosive growth of DataOps to drive faster and better-informed business decisions, proactively understanding the health of your data is more important than ever. Data observability is one of the foundational capabilities of DataOps and an emerging discipline used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered.
Join Paul Rasmussen and Shalaish Koul from Precisely, to learn how data observability can be used as part of a DataOps strategy to prevent data issues from wreaking havoc on your analytics and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence.
Topics you will hear addressed in this webinar:
Data observability – what is it and how it is different from other monitoring solutions
Why now is the time to incorporate data observability into your DataOps strategy
How data observability helps prevent data issues from impacting downstream analytics
Examples of how data observability can be used to prevent real-world issues
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...Amazon Web Services
"This is a technical architect's case study of how Loggly has employed the latest social-media-scale technologies as the backbone ingestion processing for our multi-tenant, geo-distributed, and real-time log management system. This presentation describes design details of how we built a second-generation system fully leveraging AWS services including Amazon Route 53 DNS with heartbeat and latency-based routing, multi-region VPCs, Elastic Load Balancing, Amazon Relational Database Service, and a number of pro-active and re-active approaches to scaling computational and indexing capacity.
The talk includes lessons learned in our first generation release, validated by thousands of customers; speed bumps and the mistakes we made along the way; various data models and architectures previously considered; and success at scale: speeds, feeds, and an unmeltable log processing engine."
CNIT 121: 12 Investigating Windows Systems (Part 1 of 3)Sam Bowne
Slides for a college course based on "Incident Response & Computer Forensics, Third Edition" by by Jason Luttgens, Matthew Pepe, and Kevin Mandia.
Teacher: Sam Bowne
Twitter: @sambowne
Website: https://samsclass.info/121/121_F16.shtml
This document discusses the benefits of a top-down approach to monitoring systems compared to a bottom-up approach. It provides examples of companies like Netflix and GitHub that use key performance indicators (KPIs) and high-level metrics to monitor overall system health from the top-down rather than monitoring individual components from the bottom-up. The document also discusses how BigPanda uses a pipeline latency metric as a KPI to monitor the reliability and performance of its unified monitoring dashboard.
Data pipelines observability: OpenLineage & MarquezJulien Le Dem
This document discusses OpenLineage and Marquez, which aim to provide standardized metadata and data lineage collection for data pipelines. OpenLineage defines an open standard for collecting metadata as data moves through pipelines, similar to metadata collected by EXIF for images. Marquez is an open source implementation of this standard, which can collect metadata from various data tools and store it in a graph database for querying lineage and understanding dependencies. This collected metadata helps with tasks like troubleshooting, impact analysis, and understanding how data flows through complex pipelines over time.
Lambda architecture is a popular technique where records are processed by a batch system and streaming system in parallel. The results are then combined during query time to provide a complete answer. Strict latency requirements to process old and recently generated events made this architecture popular. The key downside to this architecture is the development and operational overhead of managing two different systems.
There have been attempts to unify batch and streaming into a single system in the past. Organizations have not been that successful though in those attempts. But, with the advent of Delta Lake, we are seeing lot of engineers adopting a simple continuous data flow model to process data as it arrives. We call this architecture, The Delta Architecture.
Eze Castle Integration is a managed service provider (MSP), cloud service provider (CSP), and internet service provider (ISP) that delivers services to more than 1,000 clients around the world. Different departments within Eze Castle have devised their own log aggregation solutions in order to provide visibility, meet regulatory compliance requirements, conduct cybersecurity investigations, and help engineers with troubleshooting infrastructure issues. In 2019, they partnered with Elastic to consolidate the data generated from different systems into a single pane of glass. And thanks to the ease of deployment on Elastic Cloud, professional consultation services from Elastic engineers, and on-demand training courses available on Elastic Learning, Eze Castle was able to go from proof-of-concept to a fully functioning ""Eze Managed SIEM"" product within a month!
Learn about Eze Castle's journey with Elastic and how they grew Eze Managed SIEM from zero to 100 customers In less than 14 months.
In the slide deck, we describe how graph databases are used at Netflix. Graph databases can be faster than relational databases for deeply-connected data - a strength of the underlying model. We have used JanusGraph on top of Cassandra. Both technologies are Open Source.
This document discusses the roles and responsibilities involved in incident response (IR). It describes the incident manager who leads the investigation team, and the remediation team leader who coordinates remediation activities. It outlines the IR process including initial response, investigation, and remediation phases. It provides guidance on hiring IR talent, preserving evidence, analyzing data, developing indicators of compromise, and creating reports.
This document provides an overview and introduction to Splunk, including:
1. It discusses the challenges of machine data including volume, velocity, variety and variability.
2. Splunk's mission is to make machine data accessible, usable and valuable to everyone.
3. It demonstrates how Splunk can unlock critical insights from machine data sources like order processing, social media, customer service systems and more.
Network topologies describe the layout of connections between devices in a network. The main types are ring, star, bus, mesh, tree, and hybrid. Ring topology uses a closed loop connection where data passes through each node sequentially. Bus topology connects all devices to a single cable. Star topology connects all devices to a central node. Mesh topology connects each device to every other device. Tree topology branches out from a root node.
Data lineage and observability with Marquez - subsurface 2020Julien Le Dem
This document discusses Marquez, an open source metadata management system. It provides an overview of Marquez and how it can be used to track metadata in data pipelines. Specifically:
- Marquez collects and stores metadata about data sources, datasets, jobs, and runs to provide data lineage and observability.
- It has a modular framework to support data governance, data lineage, and data discovery. Metadata can be collected via REST APIs or language SDKs.
- Marquez integrates with Apache Airflow to collect task-level metadata, dependencies between DAGs, and link tasks to code versions. This enables understanding of operational dependencies and troubleshooting.
- The Marquez community aims to build an open
For a college course at City College San Francisco.
Based on: "Incident Response & Computer Forensics, Third Edition" by by Jason Luttgens, Matthew Pepe, and Kevin Mandia, ASIN: B00JFG7152
More information at: https://samsclass.info/152/152_F19.shtml
The document discusses various topics related to software development security including programming concepts, compilers and interpreters, procedural vs object-oriented programming, application development methods like waterfall vs agile, database security concepts, and assessing software vulnerabilities. It provides an overview of machine code, source code, and assembly language. It also describes compilers and interpreters, top-down vs bottom-up programming, open source vs proprietary software, and the software development lifecycle (SDLC) process.
Data Lake or Data Warehouse? Data Cleaning or Data Wrangling? How to Ensure t...Anastasija Nikiforova
This presentation was delivered as part of the Data Science Seminar titled “When, Why and How? The Importance of Business Intelligence“ organized by the Institute of Computer Science (University of Tartu) in cooperation with Swedbank.
In this presentation I talked about:
*“Data warehouse vs. data lake – what are they and what is the difference between them?” (structured vs unstructured, static vs dynamic (real-time data), schema-on-write vs schema on-read, ETL vs ELT) with further elaboration on What are their goals and purposes? What is their target audience? What are their pros and cons?
*“Is the Data warehouse the only data repository suitable for BI?” – no, (today) data lakes can also be suitable. And even more, both are considered the key to “a single version of the truth”. Although, if descriptive BI is the only purpose, it might still be better to stay within data warehouse. But, if you want to either have predictive BI or use your data for ML (or do not have a specific idea on how you want to use the data, but want to be able to explore your data effectively and efficiently), you know that a data warehouse might not be the best option.
*“So, the data lake will save my resources a lot, because I do not have to worry about how to store /allocate the data – just put it in one storage and voila?!” – no, in this case your data lake will turn into a data swamp! And you are forgetting about the data quality you should (must!) be thinking of!
*“But how do you prevent the data lake from becoming a data swamp?” – in short and simple terms – proper data governance & metadata management is the answer (but not as easy as it sounds – do not forget about your data engineer and be friendly with him [always… literally always :D) and also think about the culture in your organization.
*“So, the use of a data warehouse is the key to high quality data?” – no, it is not! Having ETL do not guarantee the quality of your data (transform&load is not data quality management). Think about data quality regardless of the repository!
*“Are data warehouses and data lakes the only options to consider or are we missing something?“– true! Data lakehouse!
*“If a data lakehouse is a combination of benefits of a data warehouse and data lake, is it a silver bullet?“– no, it is not! This is another option (relatively immature) to consider that may be the best bit for you, but not a panacea. Dealing with data is not easy (still)…
In addition, in this talk I also briefly introduced the ongoing research into the integration of the data lake as a data repository and data wrangling seeking for an increased data quality in IS. In short, this is somewhat like an improved data lakehouse, where we emphasize the need of data governance and data wrangling to be integrated to really get the benefits that the data lakehouses promise (although we still call it a data lake, since a data lakehouse is nut sufficiently mature concept with different definitions of it).
This document provides an overview of building, evaluating, and optimizing a RAG (Retrieve-and-Generate) conversational agent for production. It discusses setting up the development environment, prototyping the initial system, addressing challenges when moving to production like latency, costs, and quality issues. It also covers approaches for systematically evaluating the system, including using LLMs as judges, and experimenting and optimizing components like retrieval and generation through configuration tuning, model fine-tuning, and customizing the pipeline.
Live data collection on Windows systems can be done using prebuilt kits like Mandiant Redline or Velociraptor, by creating your own scripted toolkit using built-in and free tools to collect processes, network connections, system logs and other volatile data, while following best practices like testing your methods first and being cautious of malware on investigated systems.
Databricks CEO Ali Ghodsi introduces Databricks Delta, a new data management system that combines the scale and cost-efficiency of a data lake, the performance and reliability of a data warehouse, and the low latency of streaming.
Using Canary Honeypots for Network Security Monitoringchrissanders88
In this presentation I talk about how honeypots that have more traditionally been used for research purposes can also be used as an effective part of a network security monitoring strategy.
VAPT defines a wide range of security testing services to ascertain and address cyber security exposures. It includes vulnerability testing through perimeter scans for missing patches or custom exploits to bypass perimeters, as well as penetration testing by simulating real-world attacks to provide a point-in-time assessment of vulnerabilities and threats to a network infrastructure. Customers can inquire more about these security testing and analysis services by contacting the company.
stackconf 2022: Open Source for Better ObservabilityNETWAYS
In the cloud native era systems are getting ever more dynamic and complex. With containers and microservices architecture, monitoring and troubleshooting systems is more challenging than ever before. The open source community has risen up to the challenge and delivered solutions that fit modern environments. Open source projects such as Prometheus and the ELK Stack have gathered massive adoption with developers and DevOps engineers, who also carry this skillset between companies and grow the adoption. New open standards, such as OpenMetrics, OpenTracing and OpenTelemetry, are emerging to converge the industry and prevent vendor lock-in. In this talk I will talk about observability, the recommended open source tools and standards, and how to combine them to help you achieve effective observability in your environment.
Cloudflare uses ClickHouse to analyze over 1 million DNS queries per second from its global network. ClickHouse is a column-oriented database that allows Cloudflare to perform complex ad-hoc queries and aggregations over trillions of rows of DNS log data with dimensions like timestamp, zone, and location. They store raw logs for 3 months and aggregated data indefinitely to monitor trends and traffic over time. The multi-tenant ClickHouse cluster at Cloudflare inserts over 8 million rows per second and has excellent query performance for common aggregations used in their analytics.
This session will go into best practices and detail on how to architect a near real-time application on Hadoop using an end-to-end fraud detection case study as an example. It will discuss various options available for ingest, schema design, processing frameworks, storage handlers and others, available for architecting this fraud detection application and walk through each of the architectural decisions among those choices.
Netreo whitepaper 5 ways to avoid it management becoming shelfwarePeter Reynolds
This document provides 5 ways to keep IT management software from becoming shelfware or unused after purchase. The top reasons software becomes shelfware are: 1) Too many unnecessary alerts that are ignored; 2) Having to access information from multiple sources; 3) Complex interfaces that are difficult to use; 4) High maintenance and administration needs; 5) Purchasing more licenses than needed. The document recommends focusing on minimizing unnecessary alerts, providing a single dashboard, simplifying the interface, reducing administration through automation, and subscription-based purchasing to avoid shelfware.
React Faster and Better: New Approaches for Advanced Incident ResponseSilvioPappalardo
It’s impossible to prevent everything (we see examples of this in the press every week), so you must be prepared to respond. The sad fact is that you will be breached. Maybe not today or tomorrow, but it will happen. So response is more important than any specific control. But it’s horrifying how unsophisticated most organizations are about response.
This is compounded by the reality of an evolving attack space, which means even if you do incident response well today, it won’t be good enough for tomorrow.
Lambda architecture is a popular technique where records are processed by a batch system and streaming system in parallel. The results are then combined during query time to provide a complete answer. Strict latency requirements to process old and recently generated events made this architecture popular. The key downside to this architecture is the development and operational overhead of managing two different systems.
There have been attempts to unify batch and streaming into a single system in the past. Organizations have not been that successful though in those attempts. But, with the advent of Delta Lake, we are seeing lot of engineers adopting a simple continuous data flow model to process data as it arrives. We call this architecture, The Delta Architecture.
Eze Castle Integration is a managed service provider (MSP), cloud service provider (CSP), and internet service provider (ISP) that delivers services to more than 1,000 clients around the world. Different departments within Eze Castle have devised their own log aggregation solutions in order to provide visibility, meet regulatory compliance requirements, conduct cybersecurity investigations, and help engineers with troubleshooting infrastructure issues. In 2019, they partnered with Elastic to consolidate the data generated from different systems into a single pane of glass. And thanks to the ease of deployment on Elastic Cloud, professional consultation services from Elastic engineers, and on-demand training courses available on Elastic Learning, Eze Castle was able to go from proof-of-concept to a fully functioning ""Eze Managed SIEM"" product within a month!
Learn about Eze Castle's journey with Elastic and how they grew Eze Managed SIEM from zero to 100 customers In less than 14 months.
In the slide deck, we describe how graph databases are used at Netflix. Graph databases can be faster than relational databases for deeply-connected data - a strength of the underlying model. We have used JanusGraph on top of Cassandra. Both technologies are Open Source.
This document discusses the roles and responsibilities involved in incident response (IR). It describes the incident manager who leads the investigation team, and the remediation team leader who coordinates remediation activities. It outlines the IR process including initial response, investigation, and remediation phases. It provides guidance on hiring IR talent, preserving evidence, analyzing data, developing indicators of compromise, and creating reports.
This document provides an overview and introduction to Splunk, including:
1. It discusses the challenges of machine data including volume, velocity, variety and variability.
2. Splunk's mission is to make machine data accessible, usable and valuable to everyone.
3. It demonstrates how Splunk can unlock critical insights from machine data sources like order processing, social media, customer service systems and more.
Network topologies describe the layout of connections between devices in a network. The main types are ring, star, bus, mesh, tree, and hybrid. Ring topology uses a closed loop connection where data passes through each node sequentially. Bus topology connects all devices to a single cable. Star topology connects all devices to a central node. Mesh topology connects each device to every other device. Tree topology branches out from a root node.
Data lineage and observability with Marquez - subsurface 2020Julien Le Dem
This document discusses Marquez, an open source metadata management system. It provides an overview of Marquez and how it can be used to track metadata in data pipelines. Specifically:
- Marquez collects and stores metadata about data sources, datasets, jobs, and runs to provide data lineage and observability.
- It has a modular framework to support data governance, data lineage, and data discovery. Metadata can be collected via REST APIs or language SDKs.
- Marquez integrates with Apache Airflow to collect task-level metadata, dependencies between DAGs, and link tasks to code versions. This enables understanding of operational dependencies and troubleshooting.
- The Marquez community aims to build an open
For a college course at City College San Francisco.
Based on: "Incident Response & Computer Forensics, Third Edition" by by Jason Luttgens, Matthew Pepe, and Kevin Mandia, ASIN: B00JFG7152
More information at: https://samsclass.info/152/152_F19.shtml
The document discusses various topics related to software development security including programming concepts, compilers and interpreters, procedural vs object-oriented programming, application development methods like waterfall vs agile, database security concepts, and assessing software vulnerabilities. It provides an overview of machine code, source code, and assembly language. It also describes compilers and interpreters, top-down vs bottom-up programming, open source vs proprietary software, and the software development lifecycle (SDLC) process.
Data Lake or Data Warehouse? Data Cleaning or Data Wrangling? How to Ensure t...Anastasija Nikiforova
This presentation was delivered as part of the Data Science Seminar titled “When, Why and How? The Importance of Business Intelligence“ organized by the Institute of Computer Science (University of Tartu) in cooperation with Swedbank.
In this presentation I talked about:
*“Data warehouse vs. data lake – what are they and what is the difference between them?” (structured vs unstructured, static vs dynamic (real-time data), schema-on-write vs schema on-read, ETL vs ELT) with further elaboration on What are their goals and purposes? What is their target audience? What are their pros and cons?
*“Is the Data warehouse the only data repository suitable for BI?” – no, (today) data lakes can also be suitable. And even more, both are considered the key to “a single version of the truth”. Although, if descriptive BI is the only purpose, it might still be better to stay within data warehouse. But, if you want to either have predictive BI or use your data for ML (or do not have a specific idea on how you want to use the data, but want to be able to explore your data effectively and efficiently), you know that a data warehouse might not be the best option.
*“So, the data lake will save my resources a lot, because I do not have to worry about how to store /allocate the data – just put it in one storage and voila?!” – no, in this case your data lake will turn into a data swamp! And you are forgetting about the data quality you should (must!) be thinking of!
*“But how do you prevent the data lake from becoming a data swamp?” – in short and simple terms – proper data governance & metadata management is the answer (but not as easy as it sounds – do not forget about your data engineer and be friendly with him [always… literally always :D) and also think about the culture in your organization.
*“So, the use of a data warehouse is the key to high quality data?” – no, it is not! Having ETL do not guarantee the quality of your data (transform&load is not data quality management). Think about data quality regardless of the repository!
*“Are data warehouses and data lakes the only options to consider or are we missing something?“– true! Data lakehouse!
*“If a data lakehouse is a combination of benefits of a data warehouse and data lake, is it a silver bullet?“– no, it is not! This is another option (relatively immature) to consider that may be the best bit for you, but not a panacea. Dealing with data is not easy (still)…
In addition, in this talk I also briefly introduced the ongoing research into the integration of the data lake as a data repository and data wrangling seeking for an increased data quality in IS. In short, this is somewhat like an improved data lakehouse, where we emphasize the need of data governance and data wrangling to be integrated to really get the benefits that the data lakehouses promise (although we still call it a data lake, since a data lakehouse is nut sufficiently mature concept with different definitions of it).
This document provides an overview of building, evaluating, and optimizing a RAG (Retrieve-and-Generate) conversational agent for production. It discusses setting up the development environment, prototyping the initial system, addressing challenges when moving to production like latency, costs, and quality issues. It also covers approaches for systematically evaluating the system, including using LLMs as judges, and experimenting and optimizing components like retrieval and generation through configuration tuning, model fine-tuning, and customizing the pipeline.
Live data collection on Windows systems can be done using prebuilt kits like Mandiant Redline or Velociraptor, by creating your own scripted toolkit using built-in and free tools to collect processes, network connections, system logs and other volatile data, while following best practices like testing your methods first and being cautious of malware on investigated systems.
Databricks CEO Ali Ghodsi introduces Databricks Delta, a new data management system that combines the scale and cost-efficiency of a data lake, the performance and reliability of a data warehouse, and the low latency of streaming.
Using Canary Honeypots for Network Security Monitoringchrissanders88
In this presentation I talk about how honeypots that have more traditionally been used for research purposes can also be used as an effective part of a network security monitoring strategy.
VAPT defines a wide range of security testing services to ascertain and address cyber security exposures. It includes vulnerability testing through perimeter scans for missing patches or custom exploits to bypass perimeters, as well as penetration testing by simulating real-world attacks to provide a point-in-time assessment of vulnerabilities and threats to a network infrastructure. Customers can inquire more about these security testing and analysis services by contacting the company.
stackconf 2022: Open Source for Better ObservabilityNETWAYS
In the cloud native era systems are getting ever more dynamic and complex. With containers and microservices architecture, monitoring and troubleshooting systems is more challenging than ever before. The open source community has risen up to the challenge and delivered solutions that fit modern environments. Open source projects such as Prometheus and the ELK Stack have gathered massive adoption with developers and DevOps engineers, who also carry this skillset between companies and grow the adoption. New open standards, such as OpenMetrics, OpenTracing and OpenTelemetry, are emerging to converge the industry and prevent vendor lock-in. In this talk I will talk about observability, the recommended open source tools and standards, and how to combine them to help you achieve effective observability in your environment.
Cloudflare uses ClickHouse to analyze over 1 million DNS queries per second from its global network. ClickHouse is a column-oriented database that allows Cloudflare to perform complex ad-hoc queries and aggregations over trillions of rows of DNS log data with dimensions like timestamp, zone, and location. They store raw logs for 3 months and aggregated data indefinitely to monitor trends and traffic over time. The multi-tenant ClickHouse cluster at Cloudflare inserts over 8 million rows per second and has excellent query performance for common aggregations used in their analytics.
This session will go into best practices and detail on how to architect a near real-time application on Hadoop using an end-to-end fraud detection case study as an example. It will discuss various options available for ingest, schema design, processing frameworks, storage handlers and others, available for architecting this fraud detection application and walk through each of the architectural decisions among those choices.
Netreo whitepaper 5 ways to avoid it management becoming shelfwarePeter Reynolds
This document provides 5 ways to keep IT management software from becoming shelfware or unused after purchase. The top reasons software becomes shelfware are: 1) Too many unnecessary alerts that are ignored; 2) Having to access information from multiple sources; 3) Complex interfaces that are difficult to use; 4) High maintenance and administration needs; 5) Purchasing more licenses than needed. The document recommends focusing on minimizing unnecessary alerts, providing a single dashboard, simplifying the interface, reducing administration through automation, and subscription-based purchasing to avoid shelfware.
React Faster and Better: New Approaches for Advanced Incident ResponseSilvioPappalardo
It’s impossible to prevent everything (we see examples of this in the press every week), so you must be prepared to respond. The sad fact is that you will be breached. Maybe not today or tomorrow, but it will happen. So response is more important than any specific control. But it’s horrifying how unsophisticated most organizations are about response.
This is compounded by the reality of an evolving attack space, which means even if you do incident response well today, it won’t be good enough for tomorrow.
The pioneers in the big data space have battle scars and have learnt many of the lessons in this report the hard way. But if you are a general manger & just embarking on the big data journey, you should now have what they call the 'second mover advantage’. My hope is that this report helps you better leverage your second mover advantage. The goal here is to shed some light on the people & process issues in building a central big data analytics function
Overcoming the difficulties of managing multiple databasesMSM Software
With the management of multiple databases becoming a common problem as organisations evolve and expand it's important to be prepared be aware of some important Do's and Don'ts
HeadTracker is an intranet application for managing candidates along the recruitment life cycle. It stores the recruitment information on a central server -- to be accessed by the recruiters using only a web browser. Alerts, flagging, trash can are some useful features of HeadTracker.
This document outlines an assignment for tourism professionals to create a personal management information system (MIS) to monitor critical information that will affect customer wants and how their organization can provide for those wants. It instructs students to work in teams to identify key information to track, hyperlinks to reliable resources for that information, and ultimately share their outputs to create a "Master TPPMIS" that they can use in their careers. The goal is for students to proactively build a system for staying up-to-date on industry changes since businesses often focus only on short-term targets and may not conduct ongoing, accessible research.
Tackling the ticking time bomb – Data Migration and the hidden risksHarley Capewell
Data migration is intrinsic to most big IT projects and yet
as an industry we have a poor record of tackling it.
If you are faced with responsibility for an IT project where
there will be some element of data migration then this
white paper written by industry expert Johny Morris will
help guide you past the pitfalls that await the unwary and
even how to add value as a consequence of performing
this necessary task.
Get a firsthand look at our new product in a webinar on Tuesday, 17 September.
Many IT organizations tasked with securing infrastructure find that one of their biggest challenges is the vulnerability management process. This process is often manual and inefficient, leaving systems exposed to breaches and attacks.
Our new product, Puppet Remediate, addresses common pain points in the vulnerability management workflow to better protect your infrastructure and reduce manual work. With Remediate, you can:
Eliminate data handoffs between teams. Puppet Remediate integrates with the three major vulnerability scanners — Tenable, Qualys and Rapid7 — providing a single source of truth for IT Ops;
Easily assess the most critical threats. The dashboard shows all vulnerabilities prioritized by relative risk, so you know what to address first;
Agentless remediation. Run a task to remediate vulnerabilities directly from the dashboard. You can upload your own scripts, or make use of existing modules in the Puppet Forge.
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4. They point to issues that don’t require
a response
They lack critical information, forcing
you to spend time searching for more
insights in order to gauge their urgency
5. An excess of non-actionable
alerts creates “alert fatigue”,
wasting time and resources and
interfering with the real issues
at hand
7. Do you receive redundant alerts and:
Immediately ignore them?
Realize they aren’t relevant to you?
Perform the same routine actions for
obtaining the actual information you need?
10. 1. Unhelpful titles
The problem:
One of the most important parts of the alert is its title, as it is the first thing
you see.
Cryptic titles force the responders to dig unnecessarily through the body of
the alert for more info.
Extra frustration occurs when different alerts share similar titles, causing
great confusion and wasting time.
11. 1. Unhelpful titles
Example:
You receive an alert titled “CPU LOAD 1.80″ followed by another alert titled
“CPU LOAD 1.90”.
Are these alerts even referring to the same server? Is a 1.80 load critical?
What is affected by this problem?
Wouldn’t it been great if the alert provided answers rather than adding
more questions?
12. 1. Unhelpful titles
Making it actionable:
All alerts should have short yet descriptive titles.
They should enable the responder, at a glance, to know what the problem
is, where it is, and how to address it.
For example: “Server billing-1 load is critical for 5 min” is much more
actionable than “CPU LOAD 1.80”.
13. 2. Lack of vital information
The Problem:
Alert content is often limited or cryptic, forcing us to spend a lot of cycles
understanding the meaning of the alert and searching for more
information in order to gain insight.
Somewhere within my Nagios, Graphite, Pingdom, or New Relic, there is
relevant information to be found, but instead of solving the issue a
significant portion of my valuable time is spent on such searches.
14. 2. Lack of vital information
Example:
When addressing an alert about a server overload, almost always the same
set of tasks are performed.
These include connecting to the server to check for current load or
analyzing trends in the CPU graph.
Moreover, the next time a similar alert happens, you’ll be performing
these same steps over and over.
15. 2. Lack of vital information
Making it actionable:
Identify alerts that require repetitive and predictable searches for more
information
Automatically bundle that information as part of the alert.
list actions that need to be performed or a link to relevant resources
such as scripts, protocols or the developer’s insight into why this might
happen
16. 3. Alerts that don’t require resolution
The Problem:
Production environments are complex and dynamic.
To maintain reliability, vital system information must be accessible to Ops
and Developers.
Our instinct tells us that this can only be accomplished by being notified of
every alert and exception.
In reality, however, the large majority of these alerts don’t require an
action and end up drowning out the ones who do.
17. 3. Alerts that don’t require resolution
Example:
An alert could’ve been sent to indicate that a user entered an invalid credit
card number.
While this information may be very interesting, we do not have any control
over the user’s actions and can therefore do nothing about it.
Getting this alert will only add additional noise.
18. 3. Alerts that don’t require resolution
Making it actionable:
If the alert doesn’t lead to an immediate action on your part,
don’t send it.
Instead, find the issues which will require your attention.
For example, replace the invalid credit card alert with an actionable alert
which specifies that the rate of checkouts has dropped dramatically —
maybe a change was made and a rollback action is required.
Another solution can be a daily / weekly report which aggregates and
visualizes the information that isn’t required in real-time.
This way, the desired information will be available at the right time.
19. 4. Alert routing
The Problem:
In many organizations, everyone receives all the alerts.
This type of practice is usually initiated when teams are small and everyone
is involved in everything.
However, as teams scale and people begin to specialize, the “loudspeaker”
approach to alerting quickly becomes a drag.
20. 4. Alert routing
Example:
Sending alerts regarding connection issues with your 3rd party billing
provider to your DBA team won’t help resolve the alert and will probably
be ignored.
21. 4. Alert routing
Making it actionable:
Send alerts only to people who are relevant to that alert.
Obviously, this is easier said than done, as many alerts can be caused by
several different sources.
In such cases, creating more specific alerts for each source will provide the
necessary granularity to make better routing decisions.
22. Conclusion
Making alerts more actionable can significantly ease your pain
and improve the day to day work.
Simple changes, can have a dramatic impact.
23. Conclusion
Actionable alerts can become irrelevant very quickly.
Have a culture of ongoing improvement to your alerts
Make a habit of periodically reviewing them and removing
the non-actionable ones.