Artificial intelligence and machine learning are increasingly being adopted across industries. AI comes in various forms like narrow AI, general AI, and deep learning. Machine learning algorithms like supervised learning, unsupervised learning and reinforcement learning are used to build AI systems. The document discusses how AI is being used in security applications like malware detection. It also covers emerging technologies like the Internet of Things and associated security issues due to lack of encryption and authentication in many IoT devices.
AI & ML in Cyber Security - Why Algorithms Are DangerousRaffael Marty
Every single security company is talking in some way or another about how they are applying machine learning. Companies go out of their way to make sure they mention machine learning and not statistics when they explain how they work. Recently, that's not enough anymore either. As a security company you have to claim artificial intelligence to be even part of the conversation.
Guess what. It's all baloney. We have entered a state in cyber security that is, in fact, dangerous. We are blindly relying on algorithms to do the right thing. We are letting deep learning algorithms detect anomalies in our data without having a clue what that algorithm just did. In academia, they call this the lack of explainability and verifiability. But rather than building systems with actual security knowledge, companies are using algorithms that nobody understands and in turn discover wrong insights.
In this talk I will show the limitations of machine learning, outline the issues of explainability, and show where deep learning should never be applied. I will show examples of how the blind application of algorithms (including deep learning) actually leads to wrong results. Algorithms are dangerous. We need to revert back to experts and invest in systems that learn from, and absorb the knowledge, of experts.
Takeoff Projects helps students complete their academic projects.You can enrol with friends and receive machine learning project ideas kits at your doorstep. You can learn from experts, build latest projects, showcase your project to the world and grab the best jobs. Get started today!
Active Directory is audited loosely during SOX and ITGC audits, however, it is misunderstood and often audited ineffectively and inefficiently. This presentation will provide an overview of Active Directory design and guidelines for auditing it.
After completing this session, you will be able to:
Understand in broad strokes, Active Directory
Understand different forest designs
Understand how to use Powershell to audit AD
Understand how an AD data warehouse can be used to streamline audits
Every single security company is talking about how they are using machine learning—as a security company you have to claim artificial intelligence to be even part of the conversation. However, this approach can be dangerous when we blindly rely on algorithms to do the right thing. Rather than building systems with actual security knowledge, companies are using algorithms that nobody understands and, in turn, discovering wrong insights.
In this session, we will discuss:
• Limitations of machine learning and issues of explainability
• Where deep learning should never be applied
• Examples of how the blind application of algorithms can lead to wrong results
Introduction to IoT, Current trends and challenges. It also describes some of the industry standard platforms such as Microsoft Azure IoT Edge and AWS IoT. Trends described includes Edge computing, Security, Cognitive Computing, Analytics, Containers and Microservices
AI In Cybersecurity – Challenges and SolutionsZoneFox
With the rise of automation and artificial intelligence, you may be wondering how much of an impact this has on IT security. The question is, where will the future of machine learning and AI in cybersecurity take us and what are the limitations and advantages this technology offers in defending against the insider threat?
Join us to find out more about AI and where you should be applying it right now.
Learning outcomes:
The current state of AI practice and research, and how this is impacting its use in cyber security
What the current strengths and weaknesses are with existing AI approaches
What next generation AI will deliver for us with regards to ensuring we can promptly detect and respond to security incidents
How to perform Secure Data Labeling for Machine LearningSkyl.ai
Data annotations or more commonly called data labeling are an integral part of AI and Machine Learning.
One of the biggest concerns that organizations have while doing AI and ML is handling data.
Many organizations have concerns about data security and privacy of the training data, especially highly regulated industries like Healthcare, Banking, Government, etc. where data privacy and security are paramount.
What you will learn:
- Risks associated with data annotations and how to manage data privacy and data protection
- How to handle deployments and infrastructure to manage data security
- How to manage collaborative contributors for secure data labeling to balance scale, security, cost, and quality in data labeling
AI & ML in Cyber Security - Why Algorithms Are DangerousRaffael Marty
Every single security company is talking in some way or another about how they are applying machine learning. Companies go out of their way to make sure they mention machine learning and not statistics when they explain how they work. Recently, that's not enough anymore either. As a security company you have to claim artificial intelligence to be even part of the conversation.
Guess what. It's all baloney. We have entered a state in cyber security that is, in fact, dangerous. We are blindly relying on algorithms to do the right thing. We are letting deep learning algorithms detect anomalies in our data without having a clue what that algorithm just did. In academia, they call this the lack of explainability and verifiability. But rather than building systems with actual security knowledge, companies are using algorithms that nobody understands and in turn discover wrong insights.
In this talk I will show the limitations of machine learning, outline the issues of explainability, and show where deep learning should never be applied. I will show examples of how the blind application of algorithms (including deep learning) actually leads to wrong results. Algorithms are dangerous. We need to revert back to experts and invest in systems that learn from, and absorb the knowledge, of experts.
Takeoff Projects helps students complete their academic projects.You can enrol with friends and receive machine learning project ideas kits at your doorstep. You can learn from experts, build latest projects, showcase your project to the world and grab the best jobs. Get started today!
Active Directory is audited loosely during SOX and ITGC audits, however, it is misunderstood and often audited ineffectively and inefficiently. This presentation will provide an overview of Active Directory design and guidelines for auditing it.
After completing this session, you will be able to:
Understand in broad strokes, Active Directory
Understand different forest designs
Understand how to use Powershell to audit AD
Understand how an AD data warehouse can be used to streamline audits
Every single security company is talking about how they are using machine learning—as a security company you have to claim artificial intelligence to be even part of the conversation. However, this approach can be dangerous when we blindly rely on algorithms to do the right thing. Rather than building systems with actual security knowledge, companies are using algorithms that nobody understands and, in turn, discovering wrong insights.
In this session, we will discuss:
• Limitations of machine learning and issues of explainability
• Where deep learning should never be applied
• Examples of how the blind application of algorithms can lead to wrong results
Introduction to IoT, Current trends and challenges. It also describes some of the industry standard platforms such as Microsoft Azure IoT Edge and AWS IoT. Trends described includes Edge computing, Security, Cognitive Computing, Analytics, Containers and Microservices
AI In Cybersecurity – Challenges and SolutionsZoneFox
With the rise of automation and artificial intelligence, you may be wondering how much of an impact this has on IT security. The question is, where will the future of machine learning and AI in cybersecurity take us and what are the limitations and advantages this technology offers in defending against the insider threat?
Join us to find out more about AI and where you should be applying it right now.
Learning outcomes:
The current state of AI practice and research, and how this is impacting its use in cyber security
What the current strengths and weaknesses are with existing AI approaches
What next generation AI will deliver for us with regards to ensuring we can promptly detect and respond to security incidents
How to perform Secure Data Labeling for Machine LearningSkyl.ai
Data annotations or more commonly called data labeling are an integral part of AI and Machine Learning.
One of the biggest concerns that organizations have while doing AI and ML is handling data.
Many organizations have concerns about data security and privacy of the training data, especially highly regulated industries like Healthcare, Banking, Government, etc. where data privacy and security are paramount.
What you will learn:
- Risks associated with data annotations and how to manage data privacy and data protection
- How to handle deployments and infrastructure to manage data security
- How to manage collaborative contributors for secure data labeling to balance scale, security, cost, and quality in data labeling
Machine learning algorithms are permeating our world. With applications in banking, investing, social media, advertising, and crime prevention, to name a few, these little black boxes are increasingly being used to inform and drive decisions about our lives and businesses. Machine Learning Risk Management is an often overlooked aspect of creating, deploying, and monitoring machine learning applications. Andrew will explain the dangers associated with an absence of controls during the machine learning process. He will then demonstrate how controls prevent modeling biases and suggest ways to develop and deploy machine learning applications with a control-centric, engineered approach.
This deck is from Interpol Conference 2017, these slides shows the holistic view of machine learning in cyber security for better organization readiness
An introductory take on the ethical issues surrounding the use of algorithms and machine learning in finance, education, law enforcement and defense. This work was stimulated by, but is not a product or authorized content from the IEEE P7003 WG.
Disclaimer: This work is mine alone and does not reflect view of IEEE, IEEE 7003 WG, my employer.
Machine Learning: What Assurance Professionals Need to Know Andrew Clark
Machine learning has evolved past an esoteric technique worked on by academics and research institutes into a viable technology being deployed at many companies. Machine learning has been significantly changing the competitive landscape of business models worldwide, contributing to the demise of established business, such as Blockbuster, to creating entirely new businesses, such as algorithmic advertising. This presentation strives to address the questions of what assurance professionals need to know about this technology and how to provide assurance around machine learning implementations and its unique risks.
What are Cognitive Applications? What is exciting about them? They represent a whole new way of human computer interaction and acting on data insights. Introducing IBM Watson and how to develop Cognitive applications. AI, Machine Learning compared and contrasted.
Fairness and Transparency: Algorithmic Explainability, some Legal and Ethical...Patrick Van Renterghem
In this presentation, Nazanin Gifani discussed some of the ethical and legal issues of automated decision making, including algorithmic fairness, transparency and explainability. The big question here is: can AI help us to make fairer decisions ?
Prepping the Analytics organization for Artificial Intelligence evolutionRamkumar Ravichandran
This is a discussion document to be used at the Big Data Spain at Madrid on Nov 18th, 2016. The key takeaway from the deck is that AI is reality and much closer than we realize. It will impact our Analytics Community in a very different way vs. an average Consumer. We can shape and guide the revolution if we start preparing for it now - right from our mindset, design thinking principles and productization of Analytics (API-zation). AI is a need to address the problems of scale, speed, precision in the world that is getting more and more complex around us - it is not humanly possible to answer all the questions ourselves and we will need machines to do it for us. The flow of the story line begins with a reality check on popular misconceptions and some background on AI. It then delves into all the ways it can optimize the current flow and ends with the "Managing Innovation Playbook" a set of three steps that should guide our innovation programs - Strategy, Execution & Transformation, i.e., the principles that tell us what we want to get out of it, how to get it done and finally how much the benefits permanent and consistently improving.
Would love to hear your feedback, thoughts and reactions.
Robert Mercier, Senior Network Services Lead at Next Dimension, Reviews IIoT and its impact on the Manufacturing sector. He specifically addresses the value of IT/OT convergence; something that is highly valuable for the Automotive Manufacturing space.
AI-Driven Science and Engineering with the Global AI and Modeling Supercomput...Geoffrey Fox
Most things are dominated by Artificial Intelligence (AI). Technology Companies like Amazon, Google, Facebook, and Microsoft are AI First organizations.
Engineering achievement today is highlighted by the AI buried in a vehicle or machine. Industry (Manufacturing) 4.0 focusses on the AI-Driven future of the Industrial Internet of Things.
Software is eating the world.
We can describe much computer systems work as designing, building and using the Global AI and Modelling supercomputer which itself is autonomously tuned by AI. We suggest that this is not just a bunch of buzzwords but has profound significance and examine consequences of this for education and research.
Naively high-performance computing should be relevant for the AI supercomputer but somehow the corporate juggernaut is not making so much use of it. We discuss how to change this.
Getting Real About Security Management and “Big Data” EMC
It’s an exciting yet daunting time to be a security professional. Security threats are becoming more aggressive and voracious. Governments and industry bodies are getting more prescriptive around compliance. Combined with exponentially more complex IT environments, security management is increasingly challenging. Moreover, new “Big Data” technologies purport bringing advanced analytic techniques like predictive analysis and advanced statistical techniques close to the security professional.
Microservices are an effective approach to orchestrate services in the cloud. The microservices architectural style is an approach to develop a single application as a suite of small services, each running in its own process and communicating with lightweight mechanisms ( API ).
To be more effective they need a contextual evaluation of the meaning of data of IoT generating always more data.Machine Learning can support Microservices to extract meaning from Big Data making Microservices smarter and speedier. Industries can have huge benefits from this approach.
As the adoption of AI technologies increases and matures, the focus will shift from exploration to time to market, productivity and integration with existing workflows. Governing Enterprise data, scaling AI model development, selecting a complete, collaborative hybrid platform and tools for rapid solution deployments are key focus areas for growing data scientist teams tasked to respond to business challenges. This talk will cover the challenges and innovations for AI at scale for the Enterprise focusing on the modernization of data analytics, the AI ladder and AI life cycle and infrastructure architecture considerations. We will conclude by viewing the benefits and innovation of running your modern AI and Data Analytics applications such as SAS Viya and SAP HANA on IBM Power Systems and IBM Storage in hybrid cloud environments.
Webinar: Machine Learning para MicrocontroladoresEmbarcados
Neste webinar, serão apresentados conceitos sobre inteligência artificial, assim como ferramentas disponíveis para o desenvolvimento integradas ao MPLAB X e ao Harmony 3 e demonstração de um sistema de detecção de anomalia utilizando um microcontrolador da família ATSAMD21 (ARM Cortex M0+).
Machine learning algorithms are permeating our world. With applications in banking, investing, social media, advertising, and crime prevention, to name a few, these little black boxes are increasingly being used to inform and drive decisions about our lives and businesses. Machine Learning Risk Management is an often overlooked aspect of creating, deploying, and monitoring machine learning applications. Andrew will explain the dangers associated with an absence of controls during the machine learning process. He will then demonstrate how controls prevent modeling biases and suggest ways to develop and deploy machine learning applications with a control-centric, engineered approach.
This deck is from Interpol Conference 2017, these slides shows the holistic view of machine learning in cyber security for better organization readiness
An introductory take on the ethical issues surrounding the use of algorithms and machine learning in finance, education, law enforcement and defense. This work was stimulated by, but is not a product or authorized content from the IEEE P7003 WG.
Disclaimer: This work is mine alone and does not reflect view of IEEE, IEEE 7003 WG, my employer.
Machine Learning: What Assurance Professionals Need to Know Andrew Clark
Machine learning has evolved past an esoteric technique worked on by academics and research institutes into a viable technology being deployed at many companies. Machine learning has been significantly changing the competitive landscape of business models worldwide, contributing to the demise of established business, such as Blockbuster, to creating entirely new businesses, such as algorithmic advertising. This presentation strives to address the questions of what assurance professionals need to know about this technology and how to provide assurance around machine learning implementations and its unique risks.
What are Cognitive Applications? What is exciting about them? They represent a whole new way of human computer interaction and acting on data insights. Introducing IBM Watson and how to develop Cognitive applications. AI, Machine Learning compared and contrasted.
Fairness and Transparency: Algorithmic Explainability, some Legal and Ethical...Patrick Van Renterghem
In this presentation, Nazanin Gifani discussed some of the ethical and legal issues of automated decision making, including algorithmic fairness, transparency and explainability. The big question here is: can AI help us to make fairer decisions ?
Prepping the Analytics organization for Artificial Intelligence evolutionRamkumar Ravichandran
This is a discussion document to be used at the Big Data Spain at Madrid on Nov 18th, 2016. The key takeaway from the deck is that AI is reality and much closer than we realize. It will impact our Analytics Community in a very different way vs. an average Consumer. We can shape and guide the revolution if we start preparing for it now - right from our mindset, design thinking principles and productization of Analytics (API-zation). AI is a need to address the problems of scale, speed, precision in the world that is getting more and more complex around us - it is not humanly possible to answer all the questions ourselves and we will need machines to do it for us. The flow of the story line begins with a reality check on popular misconceptions and some background on AI. It then delves into all the ways it can optimize the current flow and ends with the "Managing Innovation Playbook" a set of three steps that should guide our innovation programs - Strategy, Execution & Transformation, i.e., the principles that tell us what we want to get out of it, how to get it done and finally how much the benefits permanent and consistently improving.
Would love to hear your feedback, thoughts and reactions.
Robert Mercier, Senior Network Services Lead at Next Dimension, Reviews IIoT and its impact on the Manufacturing sector. He specifically addresses the value of IT/OT convergence; something that is highly valuable for the Automotive Manufacturing space.
AI-Driven Science and Engineering with the Global AI and Modeling Supercomput...Geoffrey Fox
Most things are dominated by Artificial Intelligence (AI). Technology Companies like Amazon, Google, Facebook, and Microsoft are AI First organizations.
Engineering achievement today is highlighted by the AI buried in a vehicle or machine. Industry (Manufacturing) 4.0 focusses on the AI-Driven future of the Industrial Internet of Things.
Software is eating the world.
We can describe much computer systems work as designing, building and using the Global AI and Modelling supercomputer which itself is autonomously tuned by AI. We suggest that this is not just a bunch of buzzwords but has profound significance and examine consequences of this for education and research.
Naively high-performance computing should be relevant for the AI supercomputer but somehow the corporate juggernaut is not making so much use of it. We discuss how to change this.
Getting Real About Security Management and “Big Data” EMC
It’s an exciting yet daunting time to be a security professional. Security threats are becoming more aggressive and voracious. Governments and industry bodies are getting more prescriptive around compliance. Combined with exponentially more complex IT environments, security management is increasingly challenging. Moreover, new “Big Data” technologies purport bringing advanced analytic techniques like predictive analysis and advanced statistical techniques close to the security professional.
Microservices are an effective approach to orchestrate services in the cloud. The microservices architectural style is an approach to develop a single application as a suite of small services, each running in its own process and communicating with lightweight mechanisms ( API ).
To be more effective they need a contextual evaluation of the meaning of data of IoT generating always more data.Machine Learning can support Microservices to extract meaning from Big Data making Microservices smarter and speedier. Industries can have huge benefits from this approach.
As the adoption of AI technologies increases and matures, the focus will shift from exploration to time to market, productivity and integration with existing workflows. Governing Enterprise data, scaling AI model development, selecting a complete, collaborative hybrid platform and tools for rapid solution deployments are key focus areas for growing data scientist teams tasked to respond to business challenges. This talk will cover the challenges and innovations for AI at scale for the Enterprise focusing on the modernization of data analytics, the AI ladder and AI life cycle and infrastructure architecture considerations. We will conclude by viewing the benefits and innovation of running your modern AI and Data Analytics applications such as SAS Viya and SAP HANA on IBM Power Systems and IBM Storage in hybrid cloud environments.
Webinar: Machine Learning para MicrocontroladoresEmbarcados
Neste webinar, serão apresentados conceitos sobre inteligência artificial, assim como ferramentas disponíveis para o desenvolvimento integradas ao MPLAB X e ao Harmony 3 e demonstração de um sistema de detecção de anomalia utilizando um microcontrolador da família ATSAMD21 (ARM Cortex M0+).
Model governance in the age of data science & AIQuantUniversity
As more and more open-source technologies penetrate enterprises, data scientists have a plethora of choices for building, testing and scaling models. In addition, data scientists have been able to leverage the growing support for cloud-based infrastructure and open data sets to develop machine learning applications. Even though there are multiple solutions and platforms available to build machine learning solutions, challenges remain in adopting machine learning in the enterprise. Many of the challenges are associated with how machine learning process can be formalized. As the field matures, formal mechanism for a replicable, interpretable, auditable process for a complete machine learning pipeline from data ingestion to deployment is warranted. Projects like Docker, Binderhub, MLFlow are efforts in this quest and research and industry efforts on replicable machine learning processes are gaining steam. Heavily regulated industries like financial and healthcare industries are looking for best practices to enable their research teams to reproduce research and adopt best practices in model governance. In this talk, we will discuss the challenges and best practices of governing AI and ML model in the enterprise
Most people think a successful data product requires just three things: data, the
right algorithm, and good execution. But as anyone who’s tried to create one
knows, an effective product requires much more. In this talk, Dr. Correa Bahnsen
will share his successes—and failures—in building data products for information
security, and why an isolated data science team is a recipe for failure.
Functionalities in AI Applications and Use Cases (OECD)AnandSRao1962
This presentation was given at the OECD Network of AI Specialists (ONE) held in Paris on February 26 and 27. It covers the methodology for assessing AI use cases by technology, value chain, use, business impact, business value, and effort required.
These slides were used at the first Aarhus Follower Group meet-up for the EU-funded project IoTCrawler. They entail an introduction to the project aswell as a more in depth presentation of the difference between web search and Internet of Things (IoT) search an the development of Internet of Things. Furthermore some of the scenarios from the project are presented.
When you wake up in the morning, you probably unlock your smartphone with your fingerprint, talk to it in your own language to open your email or agenda or weather apps, ask for a recommendation for a meeting later in the day and look for the shortest path to its location. Our lives are being reshaped thanks to the amount of available data, to the computing capabilities, to Machine Learning (ML) and recently Deep Learning (DL) algorithms.
How does a ML algorithm work? What are the steps to take to success an ML project? What should one do to apply DL? Is ML hard to Learn? Is it hard to apply?
Artificial Intelligence in testing - A STeP-IN Evening Talk Session Speech by...Kalilur Rahman
AI is the new ELECTRICITY - said Andrew Ng. There are two sides of the coin. There are a lot of nay-sayers for AI. At the end of the day, it will be Augmented Intelligence, Adaptive Intelligence, Automated Intelligence that will propel human intelligence forward - more than anything else. It will be a great time ahead. Whether it would be an "Eye(AI) Wash" as skeptics say or an "I wish" from them for starting late on the journey, only time will tell. It is a matter of when and how long, instead of an If. #ArtificialIntelligence #IntelligentTesting #QCoE #NextGenTesting #QualityFocusedDelivery #DigitalInnovation #ITIndustry #NewAgeIT #InnovativeTesting#AIFication #Automation #DigitalEconomy #Singularity #Transcendence #Futurism
Keynote presentation from ECBS conference. The talk is about how to use machine learning and AI in improving software engineering. Experiences from our project in Software Center (www.software-center.se).
Correlation Analysis Modeling Use Case - IBM Power Systems Gautam Siwach
Do the people having good financial standing ,higher education level, a steady job corresponds to commit fewer crime, and Does the uneducated, or poor people commit more crime?
Data Source : From the Communities and Crime Un-normalized Data Set
Website : http://archive.ics.uci.edu/ml/machine-learning-databases/00211/CommViolPredUnnormalizedData.txt
Total Observations : 2215
Total Variables : 147
AI in the Enterprise: Past, Present & Future - StampedeCon AI Summit 2017StampedeCon
Artificial Intelligence has entered a renaissance thanks to rapid progress in domains as diverse as self-driving cars, intelligent assistants, and game play. Underlying this progress is Deep Learning – driven by significant improvements in Graphic Processing Units and computational models inspired by the human brain that excel at capturing structures hidden in massive complex datasets. These techniques have been pioneered at research universities and digital giants but mainstream enterprises are starting to apply them as open source tools and improved hardware become available. Learn how AI is impacting analytics today and in the future.
Learn how AI is affecting the enterprise including applications like fraud detection, mobile personalization, predicting failures for IoT and text analysis to improve call center interactions. We look at how practical examples of assessing the opportunity for AI, phased adoption, and lessons going from research, to prototype, to scaled production deployment.
AI & ML in Cyber Security - Welcome Back to 1999 - Security Hasn't ChangedRaffael Marty
We are writing the year 2017. Cyber security has been a discipline for many years and thousands of security companies are offering solutions to deter and block malicious actors in order to keep our businesses operating and our data confidential. But fundamentally, cyber security has not changed during the last two decades. We are still running Snort and Bro. Firewalls are fundamentally still the same. People get hacked for their poor passwords and we collect logs that we don't know what to do with. In this talk I will paint a slightly provocative and dark picture of security. Fundamentally, nothing has really changed. We'll have a look at machine learning and artificial intelligence and see how those techniques are used today. Do they have the potential to change anything? How will the future look with those technologies? I will show some practical examples of machine learning and motivate that simpler approaches generally win. Maybe we find some hope in visualization? Or maybe Augmented reality? We still have a ways to go.
Using Algorithmia to leverage AI and Machine Learning APIsRakuten Group, Inc.
We are entering a new era of software development. Companies are realizing that AI and machine learning are critical to success in business, both to save cost on repetitive tasks, and to enable to new features and products that would be impossible without machine intelligence. Algorithmia makes these tools available through web APIs that makes tools like computer vision and natural language processing available to companies everywhere. Kenny will talk about how sharing of intelligent APIs can improve your applications.
https://rakutentechnologyconference2016.sched.org/event/8aS5/using-algorithmia-to-leverage-ai-and-machine-learning-apis
Rakuten Technology Conference 2016
http://tech.rakuten.co.jp/
Algorithm Marketplace and the new "Algorithm Economy"Diego Oppenheimer
Talk by Diego Oppenheimer CEO of Algorithmia.com at Data Day Texas 2016.
Peter Sondergaard VP of Research for Gartner recently said the next digital gold rush is "How we do something with data not just what you do with it". During this talk we will cover a brief history of the different algorithmic advances in computer vision, natural language processing, machine learning and general AI and how they are being applied to Big Data today. From there we will talk about how algorithms are playing a crucial part in the next Big Data revolution, new opportunities that are opening up for startups and large companies alike as well as a first look into the role Algorithm Marketplaces will play in this space.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
2. Agenda
• Artificial Intelligence
• Industries and Jobs
• Adoption
• Drivers and variations
• Machine Learning
• Training Types
• Deep Learning
• Implementation & Products
• Internet of Things (IoT)
• Issues with IoT
• Attacks
• Security Analytics
• Usage of Data
• Advantages, Disadvantages, Recommendation & Conclusion
• Summary
• Q & A
2
5. What is AI?
1:a branch of computer science dealing with the simulation of intelligent
behavior in computers
2:the capability of a machine to imitate intelligent human behavior
• AI Effect Problems already resolved are considered not AI.
• Originally coined by Standard computer scientist in 1956
• https://en.wikipedia.org/wiki/Artificial_intelligence
5
6. Continued (1)..
• AI is a science and technology based on disciplines such as:
• Data science
• Biology
• Psychology
• Mathematics
• Engineering
• Linguistics
6
7. Continued (2)..
• The main goal of AI is to create technology that allows
computers and machines to function in an intelligent manner.
Following are the problems AI tries to resolve:
• Learning
• Natural Language Processing
• Reasoning and problem solving
• Planning
• Creativity
• Social Intelligence
• General Intelligence
• Knowledge representation
• Perception
• Motion and manipulation 7
8. Around Us
• Product Recommendation Amazon, Netflix
• Natural language translation Google, Microsoft
• Spam detection Google, Yahoo, MS, FB, Twitter
• Siri Apple’s personal assistant
• Games Call of Duty and Far Cry
• Customer Support Chat Bots
8
11. Regional Adoption
• TAQNIA can automatically detect and monitor unusual
activities across vast amounts of terrain using Simularity AI
software
• Dubai Electricity and Water Authority use AI to answer
customer enquiries in both English and Arabic, through a 24-
hour chatbot.
• AI Gernas – Drone for traffic mgmt.
11
12. AI Jobs
• Manufacturing workers (19%)
• Banking (18%)
• Construction (10%)
• Public transport (9%)
• Financial analysis (9%)
• Insurance companies (8%)
• Taxi Drivers (7%)
• Farming (6%)
• Policing/Security (5%)
• Healthcare/hospitals (4%)
• Science (4%)
http://uk.businessinsider.com/industries-most-under-threat-from-artificial-intelligence-2017-6/#11-science-4-1
Oxford University study predicted 47% of jobs could be automated by 2033.
12
15. Key Drivers
• Computing performance and speed
• Advances in the implementation of Algorithms
• Ease of collecting data
15
16. AI Variations
• Strong AI Simulate the actual human intelligence (Not Yet)
• Weak AI Exhibit certain criteria but not all (Deep Blue)
• AI between Inspired by human reasoning (IBM Watson)
• General AI Ability to reason in general (Solve any problem)
• Narrow AI Machines designed for specific purpose (Many)
16
19. What is ML?
19
In a 2015 report, ISACA defined machine learning as:
The use of computing resources that have the ability to learn (acquire and apply
knowledge and skills that maximize the chance of success). These cognitive systems
have the potential to learn from business related interactions and deliver
evidence-based responses to transform how organizations think, act and operate.
http://www.isaca.org/Knowledge-Center/Research/ResearchDeliverables/Pages/innovation-insights.aspx
21. Malware Detection
• From Incident Response
• Is a file or event malicious? (Yes, No)
• If malicious, what type of malware is it? (Trojan, Worm, etc.)
• How can I quantify the risks of the attach? (High, Medium, Low)
• How can I determine if the attack is part of a larger campaign against
my infrastructure? (APT, spear phishing)
• How likely am I to get hit again? (Next hour, week, month) 21
22. Traditional Approaches
• Static
• Packet, file type and size
• Static property signatures
• Scalable but lacks coverage
• Behavioral
• Manually create “behavioral signatures”
• Better coverage, but not always scalable
• Reputation
• “Crowdsourcing” the detection
• Can’t detect targeted threats
22
31. Machine Learning Flow
• Data Collection Collecting data ahead of time
• Data Cleaning Combining multiple data source, normalize
• Generate features Max info is extracted from the data
• Model Supervised / Unsupervised Learning
• Label events output
• Use feedback to improve model 31
33. Deep Learning
• Study of artificial neural networks and related machine
learning algorithms that contain more than one hidden layer
• Type of machine learning inspired by the connections
between neurons in the human brain. Researchers developed
a man-made imitation of this biological connectivity known as
artificial neural networks (commonly known as neural nets).
• Deep learning can use both the common supervised learning
technique and the more complex and cutting edge alternative
of unsupervised learning.
33
37. Continued (2)
• Common considerations include whether the tool will be a
standalone solution or integrated with an SIEM.
• It can also be part of a security operations center (SOC)
with red and blue teams harnessing it or another layer in the
architecture where resources are tight.
• It can be connected through a network switch SPAN port and
collect the data
• A stand-alone server or VM with lots of training data, huge
processing power & python
https://securityintelligence.com/why-machine-learning-is-an-essential-tool-in-the-cisos-arsenal/
37
38. Implementation Approach
• Use case definition Determine the requirement you would
want to address (phishing, privilege users, malware, etc.)
• Pick Organization subsets For PoC, pick couple of
departments from company
• Get Source access Solutions need access to certain files, to
operate
• Understand the results ML solutions deliver probabilistic
results based on a percentage. The solution must provide
supporting evidence when it flags an event so that analysts can
act on it.
38
39. Choosing a AI Vendor
• Vendor is using ML or AI ( Strong, Narrow)
• Technology used for Machine Learning (Deep learning,
Reinforcement)
• Limits of vendor’s ML
• Maintenance cost of ML Technology
https://www.csoonline.com/article/3211594/machine-learning/how-artificial-intelligence-fits-into-cybersecurity.html
39
41. Issues with ML
• ML is bad when there’s massive variation in the data that makes
training useless.
• Like any other technology, machine learning is not something
you can install once and forget about. You need to assure
continuous training with new datasets
• If data is not reviewed, attack code can be injected in to a
dataset.
• If normal behavior is not defined properly, attack is missed due
to false positive
41
42. ML Summary
• Machine learning is a type of artificial intelligence that enables
computers to detect patterns and establish baseline behavior
using algorithms that learn through training or observation.
• Ideal for detecting insider threats, zero day attacks coupled
with behavioral analysis machine learning is able to process
and analyze vast amounts of data that are simply impractical
for humans.
• Know your data. Without applying domain expertise to your
dataset, the result will be an overload of alerts and false
positives.
42
45. Growth of IoT
• The IoT’s growth will in turn drive an exponential rise in the
volumes of data being generated.
• IDC estimating that the number of devices connected to the
Internet will surge from 11 billion in 2016 to 80 billion in 2025
• Generating 180 zettabytes of data every year, up from 4.4
zettabytes in 2013 and 44 zettabytes in 2020.
https://www.pwc.com/gx/en/industries/communications/assets/pwc-ai-and-iot.pdf
45
46. IoT Devices
• Smartphones
• Tablets
• Smart TVs
• Smart Lighting Systems
• Smart HVAC Systems
• Security Cameras and Systems
• Wireless Keyboards
• Wireless Mouses'
• Wireless Headsets
• DVRs
• Smart Cameras
• MiFi-like Routers and Hotspots 46
47. Security Issues..
• Encryption IoT & Cloud is open
• Authentication Unsecure end points
• Firmware updates Outdated version
• Privacy Personal data is readily available
• Web interface Prone to SQL & cross-site scripting
• Backdoor Poorly developed system
https://www.networkworld.com/article/3200030/internet-of-things/researchers-find-gaps-in-iot-security.html
47
48. Attack on IoT Devices
• Continuously scan the internet for the IP address of Internet
of things (IoT) devices.
• Using a table of more than 60 common factory default
usernames and passwords, and logs into them to infect them
with the Mirai malware.
• This piece of malicious code took advantage of devices
running out-of-date versions of the Linux kernel and relied on
the fact that most users do not change the default
usernames/passwords on their devices
• Took down GitHub, DYN, Netflix, Krebs, Twitter, and a number
of other major websites
48
51. IoT Summary
• Access controls for IoT devices
• Endpoint devices has to be hardened
• Information flow control has to be controlled
• Encryption between IoT and Cloud has to be with SSL/TLS
• Vendor patches to be rigorously tested and applied
• Authentication has to strong
• Data collected for AI need to be protected
51
53. Too Much of Data
• The global median time from compromise to discovery has
dropped significantly from 146 days in 2015 to 99 days 2016,
but it is still not good enough.
• So it still takes 99 days to fix a critical vulnerability.
• Big Data is differentiated from traditional technologies in three
ways: the amount of data (volume), the rate of data generation
and transmission (velocity), and the types of structured and
unstructured data (variety)
https://www.fireeye.com/current-threats/annual-threat-report/mtrends.html
53
54. Security Detection
• 1st Generation IDS
• Layered security
• Prevention impossible
• 2nd Generation Security Information & Event Management
(SIEM)
• Present actionable information to security analyst
• Correlate alerts from different IDS sensors
• 3rd Generation Security Analytics for Security
• Contextual security analytics
• Long-term correlation
https://www.infosecurity-magazine.com/opinions/big-data-security-privacy/
54
57. Security Analytics Summary
• Time to Respond
• Key Arsenal for Incident Responders
• Integrate with existing security products
• Continuous feedback
• Know to reduce False Positives
57
59. Advantages
• User Behavior Analysis (UBA)
• Data Theft
• Prediction of Threats
• Risk Assessment
• Management of data
• Anomaly Detection
• Better Incident Response
59
http://www.securityweek.com/role-artificial-intelligence-cyber-security
60. Disadvantages
• Limitations of Data (CIA)
• AI driven ransomware/malware
• Privacy of Data
• Legal aspects of data analysis
• Unable to predict the possibilities
60
61. Recommendation
• The four critical steps they can take to do this are:
• Define a clear strategy on the expectations and value from the
AI systems. This should be clear and approved by the board.
• Perform a risk assessment that highlights the financial,
regulatory, brand reputation implications from a malfunction in
the AI system.
• Recognize clearly that the Business requires a strategic Security
& Privacy posture for the AI system to fully transform the
business.
• Intelligent systems have to be Cyber resilient to support the
intelligent systems, without which there is more potential for a
negative impact as opposed to the intended positive value from
AI systems. 61
62. Conclusion
• Synergy. Program computers to do the grunt work and leave
humans to the decision-making, incident management and
follow-up.
• AI is not a silver bullet. Experts suggest using it to automate
mundane and repetitive tasks, not as a replacement for human
judgment.
• Hackers are still using old standbys—stealing passwords, simple
malware, social engineering, etc. AI-generated attacks in the wild
aren’t (yet) common.
62
https://youtu.be/TnUYcTuZJpM