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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Security risk models against attacks in smart grid using big data and artificial intelligence.

Yazeed Yasin Ghadi1, Tehseen Mazhar2, Khursheed Aurangzeb3

  • 1Computer Science and Software Engineering Department, Al Ain University, Abu Dhabi, United Arab Emirates.

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Summary
This summary is machine-generated.

Smart grids require advanced cybersecurity. Artificial intelligence and big data offer flexible solutions to detect new threats and improve security against cyber attacks.

Keywords:
Artificial intelligenceAutomated distribution networkBig dataBlockchainCybersecurityCybersecurity risksDeep learningMachine learningMethodsSmart grid

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Cybersecurity

Background:

  • The modernization of electrical infrastructure necessitates the development of smart grids (SG).
  • Existing security technologies struggle to meet the stringent cybersecurity demands of smart grids.
  • Effective defense against evolving cyber threats requires novel methods and techniques.

Purpose of the Study:

  • To explore the application of artificial intelligence (AI), machine learning (ML), and deep learning (DL) for enhancing smart grid cybersecurity.
  • To identify and analyze various cyber attack vectors targeting smart grids.
  • To discuss the challenges and potential of AI and big data in smart grid security.

Main Methods:

  • Utilizing AI, ML, and DL for flexible data assessment and risk identification.
  • Employing machine learning models with adaptable behavior to detect novel and unexpected attacks.
  • Integrating new and existing datasets with machine learning and predictive analytics for enhanced security.

Main Results:

  • AI and big data enable a deeper understanding of the current cybersecurity landscape in smart grids.
  • Machine learning facilitates the recognition of new and unforeseen cyber threats.
  • Combining big data with AI provides a viable solution for smart grid security challenges.

Conclusions:

  • Artificial intelligence and big data are crucial for addressing the complex cybersecurity issues in smart grids.
  • Further research into AI and big data applications can significantly bolster smart grid resilience.
  • The study highlights the potential of these technologies beyond smart grids, including in healthcare.