Machine learning and artificial intelligence techniques are increasingly being used in cyber security to detect threats like malware, fraud, and intrusions. By analyzing large amounts of data, machine learning algorithms can learn patterns of both normal and anomalous behavior and make predictions about new or unseen data. This allows threats to be identified more accurately and in real-time without being explicitly programmed. Some key benefits of machine learning for cyber security include improved spam filtering, malware detection, identifying advanced threats, and detecting insider threats and data leaks. It is helping to address challenges of data overload, speed of threats, and unknown threats that traditional rule-based detection was unable to handle effectively.
A technical seminar delivered on Machine learning in cybersecurity. Machine learning is trending and desired subject this presentation demonstrates how machine learning can be used to protect IT infrastructure
With the increasingly connected world revolving around the revolution of internet and new technologies like mobiles, smartphones, and tablets, and with the wide usage of wireless technologies, the information security risks have increased. Both individuals and organizations are under regular attacks for commercial or non-commercial gains. The objectives of such attacks may be to take revenge, malign the reputation of a competitor organization, understand the strategies and sensitive information about the competitor, simply have fun of exploiting the vulnerabilities. Hence, the need to protect information assets and ensure information security receives adequate attention.
In this session, I will discuss how AI and Machine Learning can be applied in detecting, predicting and preventing cyber security/information security vulnerabilities and what are the benefits of using Machine Learning and AI. We also touch upon some of the tools available to perform the same.
Overview of Artificial Intelligence in CybersecurityOlivier Busolini
If you are interested in understsanding a bit more the potential of Artifical Intelligence in Cybersecurity, you might want to have a look at this overview.
Written from my CISO -and non AI expert- point of view, for fellow security professional to navigate the AI hype, and (hopefully!) make better, informed decisions :-)
All feedback welcome !
A technical seminar delivered on Machine learning in cybersecurity. Machine learning is trending and desired subject this presentation demonstrates how machine learning can be used to protect IT infrastructure
With the increasingly connected world revolving around the revolution of internet and new technologies like mobiles, smartphones, and tablets, and with the wide usage of wireless technologies, the information security risks have increased. Both individuals and organizations are under regular attacks for commercial or non-commercial gains. The objectives of such attacks may be to take revenge, malign the reputation of a competitor organization, understand the strategies and sensitive information about the competitor, simply have fun of exploiting the vulnerabilities. Hence, the need to protect information assets and ensure information security receives adequate attention.
In this session, I will discuss how AI and Machine Learning can be applied in detecting, predicting and preventing cyber security/information security vulnerabilities and what are the benefits of using Machine Learning and AI. We also touch upon some of the tools available to perform the same.
Overview of Artificial Intelligence in CybersecurityOlivier Busolini
If you are interested in understsanding a bit more the potential of Artifical Intelligence in Cybersecurity, you might want to have a look at this overview.
Written from my CISO -and non AI expert- point of view, for fellow security professional to navigate the AI hype, and (hopefully!) make better, informed decisions :-)
All feedback welcome !
The growth of embedded systems connecting to the Internet or "Internet of Things" (IoT) increases year by year. Thus, the IoT ecosystems become new targets of the attackers. This presentation will talk about the basic principle of information security, why we need to secure IoT ecosystems, and also the vulnerabilities and solutions from OWASP.
Seminar Presentation | Network Intrusion Detection using Supervised Machine L...Jowin John Chemban
By:
Jowin John Chemban (jowinchemban@gmail.com)
HGW16CS022 (2016-2020 Batch)
S7 B.Tech Computer Science Engineering
Holy Grace Academy of Engineering, Mala
Date : September 2019
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
Artificial Intelligence And Machine Learning PowerPoint Presentation Slides C...SlideTeam
Artificial Intelligence And Machine Learning PowerPoint Presentation Slides arrange insightful data using industry-best design practices. Highlight the differences between machine intelligence, machine learning, and deep learning through our PPT format. Utilize this PowerPoint slideshow to present advantages, disadvantages, learning techniques, and types of supervised machine learning. Further, cover the merits, demerits, and types of unsupervised machine learning. Communicate important details concerning reinforcement learning. Familiarize your viewers with the expert system in artificial intelligence. Outline examples, characteristics, constituents, uses, advantages, drawbacks, and other aspects of the expert system. Compile the deep learning process, recurrent neural networks, and convolutional neural networks through this PowerPoint theme. Present an impactful introduction to artificial intelligence. Introduce kinds, algorithms, trends, and use cases of artificial intelligence. This presentation is not only easy-to-follow but also very convenient to edit, even if you have no prior design experience. Smash the download button and start instant personalization. Our Artificial Intelligence And Machine Learning PowerPoint Presentation Slides Complete Deck are explicit and effective. They combine clarity and concise expression. https://bit.ly/3hKg7PV
What Is Machine Learning? | What Is Machine Learning And How Does It Work? | ...Simplilearn
This presentation on Machine Learning will help you understand what is Machine Learning, Artificial Intelligence vs Machine Learning vs Deep Learning, how does Machine Learning work, types of Machine Learning, Machine Learning pre-requisites and applications of Machine Learning. Machine learning is a core sub-area of artificial intelligence. Machine Learning is a technique which uses statistical methods enabling machines to learn from their past data. it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. While the concept of machine learning has been around for a long time, the ability to apply complex mathematical calculations to big data has been gaining momentum over the last several years. Now, let us get started and understand the concept of Machine Learning in detail.
Below topics are explained in this "What is Machine Learning?" presentation:
1. Machine Learning
- What is Machine Learning
2. Artificial intelligence vs Machine Learning vs Deep Learning
3. How does Machine Learning work?
4. Types of Machine Learning
5. Machine Learning pre-requisites
6. Applications of Machine Learning
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modelling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbours, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers
2. Information Architects
3. Analytics Professionals
4. Graduates
Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course
Machine Learning. What is machine learning. Normal computer vs ML. Types of Machine Learning. Some ML Object detection methods. Faster CNN, RCNN, YOLO, SSD. Real Life ML Applications. Best Programming Languages for ML. Difference Between Machine Learning And Artificial Intelligence. Advantages of Machine Learning. Disadvantages of Machine Learning
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.
The growth of embedded systems connecting to the Internet or "Internet of Things" (IoT) increases year by year. Thus, the IoT ecosystems become new targets of the attackers. This presentation will talk about the basic principle of information security, why we need to secure IoT ecosystems, and also the vulnerabilities and solutions from OWASP.
Seminar Presentation | Network Intrusion Detection using Supervised Machine L...Jowin John Chemban
By:
Jowin John Chemban (jowinchemban@gmail.com)
HGW16CS022 (2016-2020 Batch)
S7 B.Tech Computer Science Engineering
Holy Grace Academy of Engineering, Mala
Date : September 2019
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
Artificial Intelligence And Machine Learning PowerPoint Presentation Slides C...SlideTeam
Artificial Intelligence And Machine Learning PowerPoint Presentation Slides arrange insightful data using industry-best design practices. Highlight the differences between machine intelligence, machine learning, and deep learning through our PPT format. Utilize this PowerPoint slideshow to present advantages, disadvantages, learning techniques, and types of supervised machine learning. Further, cover the merits, demerits, and types of unsupervised machine learning. Communicate important details concerning reinforcement learning. Familiarize your viewers with the expert system in artificial intelligence. Outline examples, characteristics, constituents, uses, advantages, drawbacks, and other aspects of the expert system. Compile the deep learning process, recurrent neural networks, and convolutional neural networks through this PowerPoint theme. Present an impactful introduction to artificial intelligence. Introduce kinds, algorithms, trends, and use cases of artificial intelligence. This presentation is not only easy-to-follow but also very convenient to edit, even if you have no prior design experience. Smash the download button and start instant personalization. Our Artificial Intelligence And Machine Learning PowerPoint Presentation Slides Complete Deck are explicit and effective. They combine clarity and concise expression. https://bit.ly/3hKg7PV
What Is Machine Learning? | What Is Machine Learning And How Does It Work? | ...Simplilearn
This presentation on Machine Learning will help you understand what is Machine Learning, Artificial Intelligence vs Machine Learning vs Deep Learning, how does Machine Learning work, types of Machine Learning, Machine Learning pre-requisites and applications of Machine Learning. Machine learning is a core sub-area of artificial intelligence. Machine Learning is a technique which uses statistical methods enabling machines to learn from their past data. it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. While the concept of machine learning has been around for a long time, the ability to apply complex mathematical calculations to big data has been gaining momentum over the last several years. Now, let us get started and understand the concept of Machine Learning in detail.
Below topics are explained in this "What is Machine Learning?" presentation:
1. Machine Learning
- What is Machine Learning
2. Artificial intelligence vs Machine Learning vs Deep Learning
3. How does Machine Learning work?
4. Types of Machine Learning
5. Machine Learning pre-requisites
6. Applications of Machine Learning
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modelling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbours, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers
2. Information Architects
3. Analytics Professionals
4. Graduates
Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course
Machine Learning. What is machine learning. Normal computer vs ML. Types of Machine Learning. Some ML Object detection methods. Faster CNN, RCNN, YOLO, SSD. Real Life ML Applications. Best Programming Languages for ML. Difference Between Machine Learning And Artificial Intelligence. Advantages of Machine Learning. Disadvantages of Machine Learning
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.
Fraud detection is a topic which is applicable to many industries including banking and financial sectors, insurances, government agencies, and low enforcement and more.Through the use of sophisticeted use of data mining tools, millions of transactions can be searched to spot patterns and detect fraudulent transactions.
Its a process of identifying fraudulent transaction.
This technique used to recognize fraudulent creddit card transactions so that customers are not charged for items that they did not purchases
Credit Card Fraud Detection Using ML In DatabricksDatabricks
In the Credit Card Companies, illegitimate credit card usage is a serious problem which results in a need to accurately detect fraudulent transactions vs non-fraudulent transactions. All organizations can be hugely impacted by fraud and fraudulent activities, especially those in financial services. The threat can originate from internal or external, but the effects can be devastating – including loss of consumer confidence, incarceration for those involved, even up to downfall of a corporation. Despite regular fraud prevention measures, these are constantly being put to the test in an attempt to beat the system.
Fraud detection is a task of predicting whether a card has been used by the cardholder. One of the methods to recognize fraud card usage is to leverage Machine Learning (ML) models. In order to more dynamically detect fraudulent transactions, one can train ML models on a set of dataset including credit card transaction information as well as card and demographic information of the owner of the account. This will be our goal of the project while leveraging Databricks.
Looks at the different AI approaches and provides some practical categorisation and case studies. Then talks about the data fabric you need to put in place to improve model accuracy and deployment. Covers: supervised, unsupervised, machine learning, deep learning, RPA, etc. Finishes with how to create successful AI projects.
The combination of analytic technology and fraud analytics techniques with human interaction which will help to detect the possible improper transactions like fraud or bribery either before the transaction is done or after the transaction is done
Exploring the Differences Data Mining vs. Machine LearningAndrew Leo
In today's data-driven world, understanding the nuances between data mining and machine learning is crucial. While often used interchangeably, they serve distinct purposes in the realm of analytics and AI. Check out our latest post to delve into the disparities between these two technologies and how they shape decision-making processes.
Ready to harness the power of data? Contact us today for expert insights and solutions!
Learn the advantages and disadvantages of machine learning algorithms versus traditional statistical modelling approaches to solve complex business problems.
Transforming Insurance Analytics with Big Data and Automated Machine Learning Cloudera, Inc.
3 Things to Learn About:
*How to create a next generation data platform and why it is important
*How to monetize this data using predictive modeling and machine learning
*Automated machine learning as a sustainable, cost-effective and efficient solution
During this webinar you will learn:
How new advanced fraud detection models, including clustering, data/text mining, machine learning and network analysis can detect more suspicious transactions and behaviours
How workflow decision learning will make your system smarter by learning based on previous decisions and interactions
How batch file attachments can be used to attach invoices, receipts and other documentation to alerts for proper record keeping during investigations
Our new search feature that allows organizations to search alerts, work items, cases, regulatory reports, comments and attachments, as well as data from outside sources, to look for potential risks (for example, searching Export Control Lists to screen for export controlled goods)
How Concur users can now open original images of receipts directly in CaseWare Monitor, making investigations easier
In the past few years, India has witnessed exponential growth in the sector of Data Science. With the advent of digital transformation in businesses, the demand for data scientists is boosting every day with a ton of job opportunities machine learning course in mumbai’machine learning course in mumbais lying in their path. Boston Institute of Analytics provides data science courses in Mumbai. They train students under experienced industry professionals and make them industry ready. To know more about their courses check out their website https://www.biaclassroom.com/courses.
Cognitive automation with machine learning in cyber securityRishi Kant
These slides deck is from # HITBGSEC Singapore 2018, It consists of integration of cognitive automation and machine learning in different cyber security processes
These are slides from local security chapters meetup, Here I tried to explain the challenges in appsec and complete framework for different life cycle of secure software development cycle
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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
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
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.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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/
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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
2. Machine Learning
What Is It? Why now?
Why is it useful?
Machine
Learning
Artificial
Intelligence
Data
Mining
Statistics
• Machine Learning is an application of
Artificial Intelligence (AI) that allows
computers to learn without being
explicitly programmed to do so.
• Machine learning is the modern
science of finding patterns and
making predictions from data based
on work in multivariate statistics, data
mining, pattern recognition and
advanced/ predictive analytics.
Ex: when detecting fraud in the millisecond it takes to swipe a
credit card, machine learning rules not only on information
associated with the transaction, such as value and location, but
also by leveraging historical and social network data for accurate
evaluation of potential fraud
• Manage your team instead of the data.
Innovation
• Discover hidden patterns
• Adaptability
• Predictive analysis
• With falling profit margins, increasing
End Users expectations and
increasing competition from
competitors which need to cut costs
and improve their offering.
• The ability to extract value from such
vast amounts of data has never been
cheaper or more effective.
3. Machine Learning: How does it learn?
Machine Learning algorithms are categorised as being supervised or unsupervised. The former can apply what has been learned in the past to new data.
The latter can draw inferences from datasets.
Feedback
Training Data
Collect and prepare relevant data to support
analysis. If the learning objective includes
“expert” judgment, also collect the historical
“right answers.”
Algorithms
Algorithms learn to recognise patterns in training
data. Teach the programme how to know when it
is doing well or poorly, and how to self-correct in
the future.
Trained Machine
Machine is now trained and ready to spot
patterns in real world examples in order to
drive business value
Supervised Learning
What? Output variable specified. Algorithm learns mapping
function from input to output
Why? To make predictions
Example: Predicting credit default risk
Unsupervised Learning
What? Output variable unspecified so algorithm looks for
structure in data
Why? To describe hidden distribution or structure of data
Example: Customer segmentation and product targeting
Determine Objective
Decide what you would like the machine
to handle that has previously been done
based on expert knowledge or intuition.
OR
5. How Machine Learning benefits Cyber Security?
Traditionally Cyber Security
Deals problems were aided by Mathematical model.
e.g. – Data transformation[cryptography]
Modern Cyber Security
Deals with abstract threats which cannot be solved only by using mathematical models.
E.g. - Malware detection,
Intrusion detection,
Data leakage
SPAM mitigation
etc
Solution of Modern Cyber Security
6. How Machine Learning benefits Cyber Security?
* A Perfect example of Utilization of ML in Spam Filtering
7. Machine Learning Improved Some of the Areas
• Spam Mitigation
• Malware Detection
• Mitigation the Denial of Service Attacks
• Reputation in Cyber Space
• User Identification
• Detecting Identity Theft
• Information Leakage Detection & Prevention
• Social Network Security
• Detecting Advanced Persisted Threats
• Detecting Hidden Channels
8. Cyber Risk Analytics with Machine Learning
• Data Overload
• Disconnected & low quality data
• High false positive alerts
• Unknown unknowns- No Baseline
• Slow & manual Investigation processes
KEY CHALLENGES
• Focused Insight from Big Data
• Managing & rationalizing data
• Machine Learning identifies hidden patterns
• Diagnostics for understanding ‘normal’
• Targeted alerts based on anomalies
SOULTIONS
9. Threat Analytics
Areas
•Cyber Security refresh rate
•Custom payloads from
attackers
•Servers not the target
•Speed with volume
Why We need
Analytics ?
•Signature Based
•Anomaly Engines
•Analytics Workbench
•Learning Systems
Dissecting Detection
Systems •Credible / Clean training
data
•Positive & timely feedback
•Picking the right features
•Consistent feature variation
•Consistent data pattern
Benefits of ML
•DNS based detection
•DDos/ Traffic Anomaly
•SPAM Mail filters
•Authentication
•Application modelling
•Threat Intelligence
Improvement done by
ML
11. Fraud Detection
With regulations evolving in response to the financial crisis, and technology developing at an exponential rate, Companies should invest
in the latest software to reduce their exposure to risk.
1
1
Method Human Involvement AccuracySpeed
Machine
Learning
Traditional
Detection
Machine Learning
Summary
Lower fraud losses
Lower operational
costs
Improved customer
service
Reduced
reputational risk
Reduced regulatory
risk
• Algorithms analyse historical transaction data
for each customer to understand their individual
spending patterns. They can therefore spot
subtle anomalies that indicate fraud.
• Algorithms self-learn, meaning they quickly
adapt to new means of fraud, and can stay ahead
of fraudsters.
• Rely on pattern matching against recognised
past fraud types. Transactions then assessed
based on general rules, such as whether the
customer is buying abroad.
• Humans to identify trends and manually update
their models to account for changes in fraudulent
activity.
• Low
• Automatic -humans
to maintain the
algorithmic models.
• High
• Preventive over
corrective, meaning
higher rates of fraud
detection and fewer
false alarms.
• High
• Real-time, automatic
reviews of
transactions using
vast amounts of data
from multiple
sources.
• High
• Requires significant
manual analysis and
review, with regular
updates to fraud
systems.
• Medium
• Often corrective over
preventive with
limited use of data,
meaning lower
detection success
rates.
• Medium
• More human
involvement, often
using audit trails to
identify fraud.
• Less computing
power.
Credit Card Fraud Detection Scenario
12. Improvement of Security Incident
Internet-Scale measurement &
data collection (external)
• Malicious Activities: spam, phishing, scanning
• Network Mismanagement e.g. untrusted HTTPS
• Security Incident Reports: Victims VS Non-Victims
Data processing & feature
extraction
• Alignment in time & space
• Aggregate at the org. level
• 258 features, raw data & 1st/2nd order stats
Advanced data mining &
machine learning
• Classifier training
• Correlational Analysis
Prediction : the likelihood
of a future incident & type
of incident
Understanding causality
among features, security
inter-dependence
Incentive mechanism
design
14. Judgement Based
IT Sector evolving meaning they have a web of overly complex procedures built on multiple legacy platforms. Developments in
Robotics and Machine Learning mean automation of these processes is now more feasible and powerful than ever.
BusinessImpact
Nature Of Work
Rules Based
TransformationalTactical
Foundation
Simple, ad-hoc, project level
automation that can undertake
simple rule-based actions of a
single task within an application
when prompted (e.g. macros).
Robotic Process Automation
Also rule-based, but robots can
respond to external stimuli and have
their functions reprogrammed. They
can open and move structured data
between multiple applications, from
legacy systems to third party APIs
(application program interfaces).
Cognitive Automation
Self-learning, autonomous systems
driven by Machine Learning and
Natural Language Processing (NLP)
that can read and understand
unstructured information and
instruct a computer to act.
Understanding the Automation Landscape
Cognitive Automation
15. Cognitive automation has the power to automate many Business processes, in particular risk and regulatory reporting.
Cognitive Automation In Action – Document Processing Example
1 42 3 5
Open Email Classify according to
type
Comprehend & extract
relevant information
Validate information
against rules
Populate data into
Enterprise Resource
Planning system
Machine Learning
& NLP
Machine Learning
& NLP
Robotics
Machine Learning
& NLP
Robotics
Process&Technology
• Robotics can be thought of as the ‘hand’ work and cognitive the ‘head’ work – together they form a powerful alliance and can automate even
those processes that involve comprehending unstructured text or recognising voices, and making subjective decisions
• Benefits of cognitive automation include:
Reduce headcount and associated operational costs
Decreased cycle times for processes that can operate 24 hours per day (e.g. risk/regulatory reporting)
Improved accuracy – reduction of human error
Cognitive Automation
16. The following purpose, process and location checklist can be used to help you understand whether Machine Learning can be
successfully applied to a process.
Location: Front, Middle &
Back Office
Purpose: Prediction?
Purpose: Segmentation?
Process: Big Data?
Process: Digital?
Process: Repetitive &
Judgement Based?
Checklist Why?
Supervised learning: Algorithms spot trends in historical data and use this to make
predictions based on new data.
Unsupervised learning: Machine Learning can spot differences and similarities not visible
to the human eye between each data point and make sensible groupings based on these
characteristics.
Processes that involve the use of paper and physical contact between people are not
applicable to Machine Learning.
Algorithms thrive off large datasets, offering better results. They also have the computing
power to analyse big data at speed.
Algorithms learn and improve from each repetition, and the automation of such
processes offers huge cost saving potential.
The advent of tools such as Natural Language Processing and Speech Recognition mean
that Machine Learning can be applied to processes with and without customer/client
interaction.
Cognitive Automation: Process Checklist
Manage your team instead of the data. Machine Learning is based on algorithms that can learn from data without relying on rules-based programming, and its main benefit is the ability to relentlessly analyze data and every combination of variables.
Innovation: Machine Learning is designed to break benchmarks and reset the rules. Agents are not limited to the methods used the previous year, month, or day. Anything goes.
Automatically discover hidden patterns and anomalies within data through a simple visual interface. Instead of reports comprised of static data, get actionable feedback.
Adaptability is the foundation of Machine Learning. Challenges, target metrics and quizzes need to adapt to each individual agent’s pace. Without Machine Learning driving the system, progress is a one-size-fits-all proposition.
Machine Learning is the best model for combining hard science with human behavior. Predictive analysis provides insight into performance plateaus, engagement at work, and loyalty.
A predictive analytics approach to forecasting cyber security incidents. We start from Internet-scale measurement on the security postures of network entities. We also collect security incident reports to use as labels in a supervised learning framework. The collected data then goes through extensive processing and domain-specific feature extraction. Features are then used to train a classifier that generates predictions when we input new features, on the likelihood of a future incident for the entity associated with the input features. We are also actively seeking to understand the causal relationship among different features and the security interdependence among different network entities. Lastly, risk prediction helps us design better incentive mechanisms which is another facet of our research in this domain.
Analysis of data : Data traffic can be analyzed at the packet, connection or session level. The connection represents a bidirectional flow and the session represents multiple connections between the same source and destination. ‘Bro’ can monitor Transmission Control Protocol (TCP), User Datagram Protocol (UDP) and Internet Control Message Protocol (ICMP), and write the analyzed traffic to well-structured, tab-separated files suitable for post-processing. The platform interprets UDP and ICMP connection using flow semantics.
Extraction of features: Log file, for example, contains generic information about each connection, such as the time stamp, connection ID, source IP, source port, destination IP and destination port. This information is not enough. To extract more features from the network traffic, we need to create features and attributes to help us distinguish between normal and harmful traffic.
Selection of unique features: To add more depth to the analysis, we should determine whether the payload contains: Shellcode, JavaScript code, SQL command or SQL injection queries, Command injection or others. Those features can help the machine detect zero-day and web application attacks. To extract all the features, I limit the extraction process to the data sent by the source of the connection. Most features can be extracted using a regular expression or calculated directly from the connection content. Shellcode is a notable exception, because attackers can encrypt, compress or encode it. To solve this problem, at the suggestion of Dr. Ali Hadi, I used malware analysis platform Cuckoo Sandbox. Hadi suggested extracting more features from the traffic, such as the sequence of application program interfaces (APIs).
Creating useful datasets: Now that we Create a good data set with features to detect advanced attacks, we can use it to train the computer to classify new connections
Selecting & classifying features: we selected various important and generic features out of wide to train the computer to recognize the attacks:
Ex: Protocol; Service; Entropy; Number of nonprintable characters; Number of punctuation characters; Contains JavaScript; Contains SQL statement; Contains command injection; and Class.
For the classification, we can use ’ Weka’, a collection of machine learning algorithms for data mining tasks.