Smart grid and cyber defences

By: Vajratiya Vajrobol, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan, vvajratiya@gmail.com

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Smart grids are electrical systems that improve the sustainability and efficiency of energy supply through the use of digital technologies. To monitor and regulate the flow of power from generation sources to end users, these grids are equipped with complex communication and control systems.To ensure the integrity of smart grids, cybersecurity is essential. There are some concern regarding cyber defence and smart grids:

1. Cybersecurity Challenges

Sensors, communication networks, and control systems are just a few of the interconnected technologies that smart grids rely on. Because of the increasing connectedness, there are new weaknesses that could be used by bad actors [1].

2. Data Security

A lot of data is generated and processed by smart grids. It is essential to safeguard this data from illegal access, alteration, or disclosure in order to preserve the integrity of grid operations and guarantee consumer privacy.

3. Communication Security

To guard against attacks like eavesdropping, man-in-the-middle, and denial-of-service, smart grids’ communication infrastructure, which includes networks for data transmission between various components, needs to be safe.

4. Device Security

All smart grid-connected devices, such as sensors and control systems, need to have security mechanisms in place. This entails security against physical manipulation, frequent software updates, and secure device authentication [4].

5. Regulatory Compliance

It’s imperative to follow cybersecurity laws and guidelines. Numerous nations have set security requirements for vital infrastructure, and utilities that run smart grids are frequently bound by these rules [1].

7. Cooperation and Information Sharing

To exchange information about new risks and best practices, the utility sector, governmental institutions, and cybersecurity groups must work together. Using a cooperative strategy makes it easier to stay ahead of changing cyberthreats [5].

8. Employee Training

It’s critical to teach staff members cybersecurity best practices. This includes educating staff members on how to spot phishing efforts, encouraging strong password hygiene, and keeping an eye out for any security risks [5].

While there are many advantages to integrating smart grid technology in terms of sustainability and efficiency, protecting these systems from cyberattacks is crucial to guaranteeing the safe and dependable distribution of power. To create a strong defence against cyberattacks.

References

  1. Hasan, M. K., Habib, A. A., Shukur, Z., Ibrahim, F., Islam, S., & Razzaque, M. A. (2023). Review on cyber-physical and cyber-security system in smart grid: Standards, protocols, constraints, and recommendations. Journal of Network and Computer Applications, 209, 103540.
  2. Khan, A. A., Laghari, A. A., Rashid, M., Li, H., Javed, A. R., & Gadekallu, T. R. (2023). Artificial intelligence and blockchain technology for secure smart grid and power distribution Automation: A State-of-the-Art Review. Sustainable Energy Technologies and Assessments, 57, 103282.
  3. Abdulsalam, K. A., Adebisi, J., Emezirinwune, M., & Babatunde, O. (2023). An overview and multicriteria analysis of communication technologies for smart grid applications. e-Prime-Advances in Electrical Engineering, Electronics and Energy, 3, 100121.
  4. Mazhar, T., Irfan, H. M., Haq, I., Ullah, I., Ashraf, M., Shloul, T. A., … & Elkamchouchi, D. H. (2023). Analysis of Challenges and Solutions of IoT in Smart Grids Using AI and Machine Learning Techniques: A Review. Electronics, 12(1), 242.
  5. Jha, R. K. (2023). Cybersecurity and Confidentiality in Smart Grid for Enhancing Sustainability and Reliability. Recent Research Reviews Journal, 2(2), 215-241.
  6. Zamzami, I. F., Pathoee, K., Gupta, B. B., Mishra, A., Rawat, D., & Alhalabi, W. (2022). Machine learning algorithms for smart and intelligent healthcare system in Society 5.0. International Journal of Intelligent Systems37(12), 11742-11763.
  7. Chui, K. T., Gupta, B. B., Torres-Ruiz, M., Arya, V., Alhalabi, W., & Zamzami, I. F. (2023). A Convolutional Neural Network-Based Feature Extraction and Weighted Twin Support Vector Machine Algorithm for Context-Aware Human Activity Recognition. Electronics12(8), 1915.
  8. Chaudhary, P., Gupta, B., & Singh, A. K. (2022). Implementing attack detection system using filter-based feature selection methods for fog-enabled IoT networks. Telecommunication Systems81(1), 23-39.
  9. Colace, F., Guida, C. G., Gupta, B., Lorusso, A., Marongiu, F., & Santaniello, D. (2022, August). A BIM-based approach for decision support system in smart buildings. In Proceedings of Seventh International Congress on Information and Communication Technology: ICICT 2022, London, Volume 1 (pp. 471-481). Singapore: Springer Nature Singapore.
  10. Gupta, B. B., & Sheng, Q. Z. (Eds.). (2019). Machine learning for computer and cyber security: principle, algorithms, and practices. CRC Press.

Cite As:

Vajrobol V. (2024) Clustering applications in healthcare domain, Insights2Techinfo, pp.1

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