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Sensor Network - IOT
❯AI (swarm intelligence) solution for attack detection in IoT environment
Product Design Using AI
For:
Design Engineers, Product Development Team
Goal:
Improved Product Development / R&D
Large Companies
Knowledge representation - Bayesian Network
For:
Process Engineers.
Scope:
Applying Bayesian Networks to root cause analysis of industrial processes.
Goal:
Improve Operation Efficiency
AI-dispatcher (operator) of large-scale distributed energy system infrastructure
For:
Energy companies focused on AI solutions to drive energy production, transition
and distribution in large territories
Scope:
Monitoring, optimization and control of large-scale distributed energy systems
using deep reinforcement learning (gas, oil, power, heat, and water transmission
and distribution infrastructure systems).
Goal:
Improve Operation Efficiency, Increase Revenues, Reduce costs
Large Companies
Operations - Intelligent Monitoring of Infrastructures
For:
Plant Operators
Scope:
Optimization of thermal efficiency of a power plant using Machine Learning.
Goal:
Improve Operation Efficiency, Improved Employee Efficiency, Reduce costs
Reducing Food Recalls With AI-Dri
For:
Food Companies, Retail Stores, Restaurants
Goal:
Anticipate Risks, Improve Operation Efficiency
Predictive analytics for the behaviour and psycho-emotional conditions of eSports players using heterogeneous data and artificial intelligence
For:
End users
Scope:
Prediction of psycho-emotional conditions of eSports players. To form
predictions, we collect physiological data from wearables/video cameras/eye
trackers, game telemetry data from keyboard/mouse/demo files, and
environmental conditions followed by the application of machine learning
methods for the analysis of the collected data.
Goal:
Other
Robot consciousness
Scope:
A robot for museum tours equipped with the main capabilities of functional
consciousness, accepted by and transparent to untrained users.
Goal:
Other
Ontologies for smart buildings
For:
Those that can affect the AI system: since it is under the supervision of a
university, the data exchange with the building is controlled by the networking
team of the university and the person in charge of the security. A university
network is not so open! It is not like the Internet that individuals can publicly
access. A group of persons in charge of the GDPR would also be deployed during
the use case.
Scope:
Renovation of a building, improvement of the quality of life of the residents
(limited to data issues in the building), audience: citizens, public and private
actors, companies involved in the ICT system managing the building. The scope
is not limited to the building management system (BMS). We would like to open
it to data produced by residents, coupled with data coming from the BMS.
Goal:
Other
Unmanned protective vehicle for road works on motorways
Scope:
Unmanned operation of a protective vehicle in order to reduce the risk for road
workers in short-term and mobile road works carried out in moving traffic.
Goal:
Other
AI components for vehicle platooning on public roads
Scope:
Trains of vehicles that drive very close to each other at nearly equal speed
(platoons) on public roads, in particular platooning trucks on motorways.
Goal:
Improve Operation Efficiency
Use of robotic solution for traffic policing and control
Scope:
Robotics-based traffic policing system.
Goal:
Other
Behavioural and sentiment analytics
For:
Organizations, end users, community
Scope:
Ascertain a person's emotional state and goal from their gestures, facial
expression, and actions.
Goal:
Improve Operation Efficiency
From Weeks Down to Hours: How insurers innovate with AI to shorten claims processing time and improve customer experience
For:
- Claims Management
- Customer Experience.
Goal:
Improved Customer Experience
AI (swarm intelligence) solution for attack detection in IoT environment
For:
End users of smart metering, utility companies
Scope:
Anomaly-based attack detection in an IoT environment using swarm
Intelligence.
Goal:
Other
Robotic solution for replacing human labour in hazardous conditions
Scope:
Building an AI-based robotics solution for replacing human labour in hazardous
conditions.
Goal:
Other
Animal Disease Detection
For:
Dairy Farmers, Factory Farmers
Goal:
Anticipate Risks
Ecosystems management from causal relation inference from observational data
For:
Environment, ecosystem
Scope:
Infer important latent variables to control a whole ecosystem using a database including human observation and sensor data.
Goal:
Improved Product Development / R&D, Improve Operation Efficiency
Weather Forecasting in Agriculture
For:
Farmers
Goal:
Anticipate Risks, Improve Operation Efficiency
Robotic vision scene awareness
For:
Customers, 3 rd parties, end users, community
Scope:
Determining the environment the robot is in and which actions are available to
it.
Goal:
Improve Operation Efficiency
AI to understand adulteration in commonly used food items
For:
Consumers, farmers, health monitoring agencies
Scope:
Understand the patterns in hyperspectral / near infrared (NIR) or visual imaging specifically for adulteration in milk, bananas and mangoes.
Goal:
Improve Operation Efficiency
AI based text to speech services with personal voices for people with speech impairments
For:
People with speech impairments
Scope:
All people who have some sort of speech impairment including but not limited to three basic types: articulation disorders, fluency disorders, and voice disorders.
Goal:
Improved Customer Experience
Large Companies
Manufacturing and Factories – Predictive Maintenance
For:
Operation, R&D
Scope:
Avoiding unplanned shutdowns in Manufacturing using Machine Learning to predict failure states in equipment.
Goal:
Anticipate Risks, Improve Operation Efficiency
Self-driving aircraft towing vehicle
Scope:
Self-Driving towing vehicle for aircrafts, operating on an airfield autonomously.
Goal:
Improve Operation Efficiency
Improving productivity for warehouse operation
For:
Warehouse manager
Scope:
Big data analysis for enhancing productivity.
Goal:
Other
AI (swarm intelligence) solution for attack detection in IoT environment
For:
End users of smart metering, utility companiesGoal:
OtherProblem addressed
Given: Advanced metering infrastructure (AMI) smart meters in smart
buildings in smart cities.
Detect: Detect energy theft / meter tampering by consumers in AMI or
hacking attacks by external agents (man in the middle) in edge computing
security scenarios with intermittent disconnection, near real-time response
without using server or cloud-based analytics.
Scope of use case
Anomaly-based attack detection in an IoT environment using swarm
Intelligence.
Description
This description is an introduction to anomaly-based attack
detection using swarm intelligence.
Motivation: World-wide statistics shows that are IoT
installed base is expected to be 12,86 bill u in the consumer
sector by 2020. In the smart city industry, smart security is
expected to account for 13,5 percent of the global smart city
market. It is expected to be more than one billion devices
installed in smart homes. India is planning one hundred
smart cities to be developed in the next 5 y, and security is of
paramount importance. Securing advanced metering
Infrastructure (AMI) is expected to be the key component for
securing smart city infrastructure. An important aspect of
securing AMI is securing the Smart Energy meters and
detecting attacks on these smart meters. While there are
many traditional solutions for anomaly and intrusion-based
detection based on static preset rules / policies, these
solutions are not effective in detecting future attacks that are
not already known. A more robust and more secure security
solution to detect attacks in edge network is essential. Hence
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a new innovative approach of using swarm intelligence along
with anomaly-based detection has been a technology choice
to solve this problem in a unique way.
Problem statement: Detect energy theft / meter tampering
by consumer in AMI or hacking attack by an external agent
(man in the middle) for edge computing security scenarios
with intermittent disconnection, near real-time response
without using a server or cloud-based analytics.
Current situation: There are many cloud service-based
centralized solutions available using static rules / configured
policies that can detect existing known attacks only.
Processing in a centralized cloud service involves
transferring data from sensors / actuators to the cloud
service, which in itself is a concern in terms of privacy,
security, regulations and compliance for some key industry
verticals.
Solution approach: Swarm Intelligence is a specific branch of
AI. It is a new innovative approach using a swarm
intelligence (AI)-based solution for attack detection. This use
case used the collective behaviour of the decentralized self-
organizing swarm of nodes with simple computational rules,
interacting locally.
Result: Simple collective algorithms for detection of man-in-
the-middle attacks on data / networks.
The following anomaly-based attack detection algorithms
were used:
1. moving average-based;
2. Mahalanobis distance-based;
3. entropy-based.
Use case: Detection of attacks on AMI smart metering
network. It includes two parts:
1. energy theft by consumers;
2. attack launched by external entity (hacker) using a man in-
the-middle attack.
Technology: Swarm intelligence and anomaly-based attack
detection using energy consumption data from smart meters
to detect attacks using consensus-based anomaly detection
algorithms.
The following items are solution steps.
Step 1: Each smart meter node reads its energy consumption
data
Step 2: Node shares energy consumption data with its
neighbouring nodes
Step 3: Node computes anomaly index based on anomaly
detection algorithm
Step 4: Neighbouring nodes detect anomalous node(s) based
on anomaly index by consensus
Step 5: Neighbouring nodes raise alarm indicating attacked /
compromised node
Step 6: Report alarm to back end host
Step 7: Display monitoring status on host UI
Sensor Network - IOT
AI: Perceive