IoT Security Expert Training

 >>  IoT Security Expert Training

IoT Security Expert Training


 (4.9) | 220 Ratings


Introduction


IoT Security Expert Training Details
Track Regular Track Weekend Track Fast Track
Course Duration 45 Hrs 8 Weekends 5 Days
Hours 1hr/day 2 Hours a day 6 Hours a day
Training Mode Online Classroom Online Classroom Online Classroom
Delivery Instructor Led-Live Instructor Led-Live Instructor Led-Live


Course Curriculum

Course Objectives:

  • Python Overview
  • Advance
  • Iot Training & Certification Program

Python overview

  • Syntax and structure
  • Comparisons to other languages (C, C++, Java, etc)
  • Available Python Resources
  • Whitespace, Indentation and program formatting
  • Variables and Naming Conventions
  • Operators
  • Statement structure
  • Comments
  • Program Construction

Data Types

  • Built-in Types
  • Strings and Numbers
  • Formatting Data, Numbers, Dates
  • Using Lists/Arrays
  • Tuples
  • Dictionaries
  • Understanding Dynamic Typing
  • Working with Functions
  • Python Code Execution
  • Basic Input / Output
  • String Operations
  • Working with Tuples and Lists
  • Introducing Control Flow Statements

Functions

  • Variable Scope
  • Variable Parameters
  • Default Values
  • Positional Parameters
  • Keyword Parameters
  • Introducing Lambdas
  • Exception Handling

Classes in Python

  • Creating Classes in Python
  • Classes are Namespaces
  • Constructors
  • Self and Instances
  • Class Variables
  • List Comprehensions
  • Advance Python Modules
  • Default Values
  • Positional Parameters
  • Keyword Parameters
  • Introducing Lambdas
  • Exception Handling

Classes in Python

  • Creating Classes in Python
  • Classes are Namespaces
  • Constructors
  • Self and Instances
  • Class Variables
  • List Comprehensions
  • Advance Python Modules

 

ADVANCE IOT TRAINING & CERTIFICATION PROGRAM

Learning outcome/Objective:

Participants will have:

  • Expert level of knowledge of IoT technology, tools and trends.
  • Sound understanding of core concepts, background technologies and sub-domains of IoT.
  • Knowledge and skills of sensors, microcontrollers, and communication interfaces to design and build IoT devices.
  • Knowledge and skills to design and build network based on client-server and publish-subscribe to connect, collect data, monitor and manage assets.
  • Knowledge and skill to write device, gateway and server side scripts and apps to aggregate and analyze sensor data
  • Knowledge and skills to select application layer protocols and web services architectures for seamless integration of various components of an IoT ecosystem.
  • Knowledge of standard development initiatives and reference architectures.
  • Understanding of deploying various types of analytics on machine data to define context, find faults, ensure quality, and extract actionable insights.
  • Understanding of cloud infrastructure, services, APIs, and architectures of commercial and industrial cloud platforms.
  • Understanding of prevalent computing architectures – distributed, centralized, edge and Fog.

Content

    • Introduction to Internet of Things
    • Concept and definitions
    • Embedded Systems, Computer Networks, M2M (Machine to Machine Communication), Internet of Everything (IoE), Machine Learning, Distributed Computing, Artificial Intelligence, Industrial automation
    • Interoperability, Identification, localization, Communication, Software Defined Assets
    • Understanding IT and OT convergence: Evolution of IIoT & Industrie 0
    • IoT Adoption
    • Market statistics, Early adopters, Roadmap
    • Business opportunities: Product + Service model
    • Development, deployment and monetization of applications as service
    • Use cases
    • Concept of Data, Information, Knowledge and Wisdom
    • Knowledge discovery process
    • DIKW pyramid and relevance with IoT
    • Microcontrollers: cost, performance, and power consumption
    • Commercial microcontroller based development boards
    • Selection criteria and tradeoffs
    • Industrial networks, M2M networks
    • Sensor Data Mining and Analytics
    • Transducer: Sensor and Actuator
    • Sensors – Types of sensors, sampling, analog to digital conversion, selection criteria of sensor and ADC
    • Data acquisition, storage and analytics
    • Signals and systems
    • Signal processing, systems classification, sampling theorem, ensuring quality and consistency of data
    • Real time analytics
    • Understanding fundamental nuances between IoT and Big data
    • Usage of IoT data in various business domains to gain operational efficiency
    • Edge analytics
    • Data Aggregation on Edge gateway
    • Wireless Sensor Area Networks (WSAN): Evolution of M2M and IoT networks and technologies
    • Sensor nodes
    • Sensor node architecture
    • WSN/M2M communication technologies
    • Bluetooth, Zigbee and WiFi communication technologies
    • Cellular communication and LPWAN (LoRa and LoRaWAN) technologies
    • Topologies
    • Applications
    • Design and Development of IoT systems
    • IoT reference architectures
    • Standardization initiatives
    • Interoperability issues
    • IoT design considerations
    • Architectures Device, Network and Cloud
    • Centralized vs distributed architectures
    • Networks, communication technologies and protocols
    • Smart asset management: Connectivity, Visibility, Analytics, Alerts
    • Cloud computing and platforms
    • Public, Private and Hybrid cloud platforms and deployment strategy
    • Industrial Gateways
    • Commercial Gateways solutions from various vendors
    • Cloud based Gateway solutions
    • IaaS, SaaS, PaaS models
    • Cloud components and services
    • Device Management, Databases, Visualization, Reporting, Notification/Alarm management, Security management, Cloud resource monitoring and management
    • Example platforms: ThingSpeak, Pubnub, AWS IoT
    • AWS IoT Services
  • Device Registry
  • Authentication And Authorization
  • Device Gateway
  • Rules Engine
  • Device Shadow
  • IoT security
  • Standards and Best practices
  • Common vulnerabilities
  • Attack surfaces
  • Hardware and Software solutions
  • Open source initiatives
  • Analytics
  • Descriptive, Diagnostic, Predictive and Prescriptive
  • Analytics using Python advance packages: Numpy, Scipy, Matplotlib, Pandas and Sci-kit learn
  • Case studies and roadmap
  • Cold chain monitoring
  • Asset tracking using RFID and GPRS/GPS

Hands-on/Practical exercises:

  • Programming microcontrollers (Arduino, NodeMCU)
  • Building HTTP and MQTT based M2M networks
  • Interfacing Analog and Digital sensors with microcontroller to learn real-time data acquisition, storage and analysis on IoT endpoints and edges
  • Interfacing SD card with microcontroller for data logging on IoT end devices using SPI protocol
  • Interfacing Real-time clock module with microcontrollers for time and date stamping using I2C protocol
  • Python exercises to check quality of acquired data
  • developing microcontroller based applications to understand event based real time processing and in- memory computations
  • Setting up Raspberry Pi as Gateway to aggregate data from thin clients
  • Python programming on Raspberry Pi to analyze collected data
  • GPIO programming using Python and remote monitoring /control
  • Pushing collected data to cloud platforms
  • Designing sensor nodes to collect multiple parameters (Temperature, Humidity etc)
  • Uploading data on local gateway as cache
  • Uploading data on cloud platforms
  • Monitoring and controlling devices using android user apps and Bluetooth interfaces
  • Building wireless sensor networks using WiFi
  • Sensor data uploading on cloud using GSM/GPRS
  • Device to device communication using LoRa modules
  • Remote controlling machines using cloud based apps
  • Remote controlling machines using device based apps through cloud as an intermediate node
  • Interfacing Raspberry Pi with AWS IoT Gateway service to exchange messages
  • Interfacing Raspberry Pi with PUBNUB cloud to understand publish/subscribe architecture and MQTT protocol
  • Data cleaning, sub setting and visualization
  • Set of python exercises to demonstrate descriptive and predictive analytics
  • Case study/Use case:
  • Environment Monitoring
  • Health monitoring (Wearable)
  • Asset performance monitoring

INTERNET OF THINGS SECURITY

  • Introduction to IOT
  • IoT Architecture
  • Securing the IoT
  • IoT Vulnerabilities
  • Awareness of Attacks
  • IoT Security Challenges
  • Secure Communications
  • Fundamentals of Cryptography
  • IoT Authentication and Authorization
  • IoT Data Integrity
  • IoT Security Standards
  • Emerging Technologies for IoT Security
  • Possibilities for Hackers on IoT devices
  • Protection for the Device
  • Protection for Data
  • Security Management
  • Analyzing the Risks
  • Device Firmware Exploitation
  • Public key cryptography
  • Digital Signature
  • Authentication, Authorization & Integrity
  • Implement Technical Countermeasures – 1 hour
  • NIST Cybersecurity Framework – 1 hour
  • NERC-CIP security standards – 1 hour
  • IEEE P1619 encryption of data on fixed and removable storage devices – 1 hour
  • IEEE P2600 – 1 hour
  • IEEE 1ae – 1 hour
  • IEEE 1x – 1.5 hours
  • WiFi Vulnerabilities – 4 hours
  • Classic Bluetooth Security – 2 hours
  • BR/EDR Security – 2 hours
  • BLE Security – 3 hours
  • BLE Vulnerabilities – 1 hour
  • ZigBee Vulnerabilities – 2 hours

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Course Curriculum

Course Objectives:

  • Python Overview
  • Advance
  • Iot Training & Certification Program

Python overview

  • Syntax and structure
  • Comparisons to other languages (C, C++, Java, etc)
  • Available Python Resources
  • Whitespace, Indentation and program formatting
  • Variables and Naming Conventions
  • Operators
  • Statement structure
  • Comments
  • Program Construction

Data Types

  • Built-in Types
  • Strings and Numbers
  • Formatting Data, Numbers, Dates
  • Using Lists/Arrays
  • Tuples
  • Dictionaries
  • Understanding Dynamic Typing
  • Working with Functions
  • Python Code Execution
  • Basic Input / Output
  • String Operations
  • Working with Tuples and Lists
  • Introducing Control Flow Statements

Functions

  • Variable Scope
  • Variable Parameters
  • Default Values
  • Positional Parameters
  • Keyword Parameters
  • Introducing Lambdas
  • Exception Handling

Classes in Python

  • Creating Classes in Python
  • Classes are Namespaces
  • Constructors
  • Self and Instances
  • Class Variables
  • List Comprehensions
  • Advance Python Modules
  • Default Values
  • Positional Parameters
  • Keyword Parameters
  • Introducing Lambdas
  • Exception Handling

Classes in Python

  • Creating Classes in Python
  • Classes are Namespaces
  • Constructors
  • Self and Instances
  • Class Variables
  • List Comprehensions
  • Advance Python Modules

 

ADVANCE IOT TRAINING & CERTIFICATION PROGRAM

Learning outcome/Objective:

Participants will have:

  • Expert level of knowledge of IoT technology, tools and trends.
  • Sound understanding of core concepts, background technologies and sub-domains of IoT.
  • Knowledge and skills of sensors, microcontrollers, and communication interfaces to design and build IoT devices.
  • Knowledge and skills to design and build network based on client-server and publish-subscribe to connect, collect data, monitor and manage assets.
  • Knowledge and skill to write device, gateway and server side scripts and apps to aggregate and analyze sensor data
  • Knowledge and skills to select application layer protocols and web services architectures for seamless integration of various components of an IoT ecosystem.
  • Knowledge of standard development initiatives and reference architectures.
  • Understanding of deploying various types of analytics on machine data to define context, find faults, ensure quality, and extract actionable insights.
  • Understanding of cloud infrastructure, services, APIs, and architectures of commercial and industrial cloud platforms.
  • Understanding of prevalent computing architectures – distributed, centralized, edge and Fog.

Content

    • Introduction to Internet of Things
    • Concept and definitions
    • Embedded Systems, Computer Networks, M2M (Machine to Machine Communication), Internet of Everything (IoE), Machine Learning, Distributed Computing, Artificial Intelligence, Industrial automation
    • Interoperability, Identification, localization, Communication, Software Defined Assets
    • Understanding IT and OT convergence: Evolution of IIoT & Industrie 0
    • IoT Adoption
    • Market statistics, Early adopters, Roadmap
    • Business opportunities: Product + Service model
    • Development, deployment and monetization of applications as service
    • Use cases
    • Concept of Data, Information, Knowledge and Wisdom
    • Knowledge discovery process
    • DIKW pyramid and relevance with IoT
    • Microcontrollers: cost, performance, and power consumption
    • Commercial microcontroller based development boards
    • Selection criteria and tradeoffs
    • Industrial networks, M2M networks
    • Sensor Data Mining and Analytics
    • Transducer: Sensor and Actuator
    • Sensors – Types of sensors, sampling, analog to digital conversion, selection criteria of sensor and ADC
    • Data acquisition, storage and analytics
    • Signals and systems
    • Signal processing, systems classification, sampling theorem, ensuring quality and consistency of data
    • Real time analytics
    • Understanding fundamental nuances between IoT and Big data
    • Usage of IoT data in various business domains to gain operational efficiency
    • Edge analytics
    • Data Aggregation on Edge gateway
    • Wireless Sensor Area Networks (WSAN): Evolution of M2M and IoT networks and technologies
    • Sensor nodes
    • Sensor node architecture
    • WSN/M2M communication technologies
    • Bluetooth, Zigbee and WiFi communication technologies
    • Cellular communication and LPWAN (LoRa and LoRaWAN) technologies
    • Topologies
    • Applications
    • Design and Development of IoT systems
    • IoT reference architectures
    • Standardization initiatives
    • Interoperability issues
    • IoT design considerations
    • Architectures Device, Network and Cloud
    • Centralized vs distributed architectures
    • Networks, communication technologies and protocols
    • Smart asset management: Connectivity, Visibility, Analytics, Alerts
    • Cloud computing and platforms
    • Public, Private and Hybrid cloud platforms and deployment strategy
    • Industrial Gateways
    • Commercial Gateways solutions from various vendors
    • Cloud based Gateway solutions
    • IaaS, SaaS, PaaS models
    • Cloud components and services
    • Device Management, Databases, Visualization, Reporting, Notification/Alarm management, Security management, Cloud resource monitoring and management
    • Example platforms: ThingSpeak, Pubnub, AWS IoT
    • AWS IoT Services
  • Device Registry
  • Authentication And Authorization
  • Device Gateway
  • Rules Engine
  • Device Shadow
  • IoT security
  • Standards and Best practices
  • Common vulnerabilities
  • Attack surfaces
  • Hardware and Software solutions
  • Open source initiatives
  • Analytics
  • Descriptive, Diagnostic, Predictive and Prescriptive
  • Analytics using Python advance packages: Numpy, Scipy, Matplotlib, Pandas and Sci-kit learn
  • Case studies and roadmap
  • Cold chain monitoring
  • Asset tracking using RFID and GPRS/GPS

Hands-on/Practical exercises:

  • Programming microcontrollers (Arduino, NodeMCU)
  • Building HTTP and MQTT based M2M networks
  • Interfacing Analog and Digital sensors with microcontroller to learn real-time data acquisition, storage and analysis on IoT endpoints and edges
  • Interfacing SD card with microcontroller for data logging on IoT end devices using SPI protocol
  • Interfacing Real-time clock module with microcontrollers for time and date stamping using I2C protocol
  • Python exercises to check quality of acquired data
  • developing microcontroller based applications to understand event based real time processing and in- memory computations
  • Setting up Raspberry Pi as Gateway to aggregate data from thin clients
  • Python programming on Raspberry Pi to analyze collected data
  • GPIO programming using Python and remote monitoring /control
  • Pushing collected data to cloud platforms
  • Designing sensor nodes to collect multiple parameters (Temperature, Humidity etc)
  • Uploading data on local gateway as cache
  • Uploading data on cloud platforms
  • Monitoring and controlling devices using android user apps and Bluetooth interfaces
  • Building wireless sensor networks using WiFi
  • Sensor data uploading on cloud using GSM/GPRS
  • Device to device communication using LoRa modules
  • Remote controlling machines using cloud based apps
  • Remote controlling machines using device based apps through cloud as an intermediate node
  • Interfacing Raspberry Pi with AWS IoT Gateway service to exchange messages
  • Interfacing Raspberry Pi with PUBNUB cloud to understand publish/subscribe architecture and MQTT protocol
  • Data cleaning, sub setting and visualization
  • Set of python exercises to demonstrate descriptive and predictive analytics
  • Case study/Use case:
  • Environment Monitoring
  • Health monitoring (Wearable)
  • Asset performance monitoring

INTERNET OF THINGS SECURITY

  • Introduction to IOT
  • IoT Architecture
  • Securing the IoT
  • IoT Vulnerabilities
  • Awareness of Attacks
  • IoT Security Challenges
  • Secure Communications
  • Fundamentals of Cryptography
  • IoT Authentication and Authorization
  • IoT Data Integrity
  • IoT Security Standards
  • Emerging Technologies for IoT Security
  • Possibilities for Hackers on IoT devices
  • Protection for the Device
  • Protection for Data
  • Security Management
  • Analyzing the Risks
  • Device Firmware Exploitation
  • Public key cryptography
  • Digital Signature
  • Authentication, Authorization & Integrity
  • Implement Technical Countermeasures – 1 hour
  • NIST Cybersecurity Framework – 1 hour
  • NERC-CIP security standards – 1 hour
  • IEEE P1619 encryption of data on fixed and removable storage devices – 1 hour
  • IEEE P2600 – 1 hour
  • IEEE 1ae – 1 hour
  • IEEE 1x – 1.5 hours
  • WiFi Vulnerabilities – 4 hours
  • Classic Bluetooth Security – 2 hours
  • BR/EDR Security – 2 hours
  • BLE Security – 3 hours
  • BLE Vulnerabilities – 1 hour
  • ZigBee Vulnerabilities – 2 hours

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