Abstract

Negative effects of cyber-attacks against telecom operators are imposed not only on the telecom operators but also on their users. Even worse, negative effects could be imposed on national economies and on public safety. This situation happens because telecom operators are the primary communications infrastructure providers used by enterprises and people for their day-to-day operations. In the network-cloud era, telecom operators face cyber threats from both established and new attack sources. In addition, telecom operators deliver numerous services over general-purpose COTS hardware and software for lower COST, which ultimately results in larger surfaces of attack. These challenges require enhanced telecom security that can effectively improve detection, prevention, response, and recovery capabilities against advanced, massive, and patchy threats targeting telecom networks and services.

Although real-time threat detection and forensic investigation can be efficiently performed using state-of-the-art techniques such as big data analytics based on statistics or machine learning models, it is challenging to understand unknown threats. This results in having to deal with an unknown threat, which is more costly than known threats. The recently proposed cloud-based threat intelligence service can fill this gap by providing threat information regarding new attack sources, tactics, methods used, signatures, and patch solutions. Such service can leverage a large telecom security consortium where a group of telecom operators share the information of their security logs and shares the cost of the threat intelligence service, which usually charges COSTs based on the size of ingested logs. The consortium must protect its ingested logs and extracted intelligence in the service from being compromised by users in the cloud.

Keywords

  • Telecom security
  • big data analytics
  • cloud-based threat intelligence
  • cybersecurity
  • network protection
  • real-time threat detection
  • intrusion detection systems (IDS)
  • anomaly detection
  • predictive analytics
  • data-driven security
  • cloud computing
  • scalable security solutions
  • advanced persistent threats (APT)
  • threat intelligence platforms
  • security analytics
  • SIEM
  • telecommunications infrastructure
  • cyber threat mitigation
  • proactive security
  • data protection.

References

  1. 1. Chava, K., Chakilam, C., Suura, S. R., & Recharla, M. (2021). Advancing Healthcare Innovation in 2021: Integrating AI, Digital Health Technologies, and Precision Medicine for Improved Patient Outcomes. Global Journal of Medical Case Reports, 1(1), 29–41. Retrieved from https://www.scipublications.com/journal/index.php/gjmcr/article/view/1294
  2. 2. Nuka, S. T., Annapareddy, V. N., Koppolu, H. K. R., & Kannan, S. (2021). Advancements in Smart Medical and Industrial Devices: Enhancing Efficiency and Connectivity with High-Speed Telecom Networks. Open Journal of Medical Sciences, 1(1), 55–72. Retrieved from https://www.scipublications.com/journal/index.php/ojms/article/view/1295
  3. 3. Avinash Pamisetty. (2021). A comparative study of cloud platforms for scalable infrastructure in food distribution supply chains. Journal of International Crisis and Risk Communication Research , 68–86. Retrieved from https://jicrcr.com/index.php/jicrcr/article/view/2980
  4. 4. Anil Lokesh Gadi. (2021). The Future of Automotive Mobility: Integrating Cloud-Based Connected Services for Sustainable and Autonomous Transportation. International Journal on Recent and Innovation Trends in Computing and Communication, 9(12), 179–187. Retrieved from https://ijritcc.org/index.php/ijritcc/article/view/11557
  5. 5. Balaji Adusupalli. (2021). Multi-Agent Advisory Networks: Redefining Insurance Consulting with Collaborative Agentic AI Systems. Journal of International Crisis and Risk Communication Research , 45–67. Retrieved from https://jicrcr.com/index.php/jicrcr/article/view/2969
  6. 6. Singireddy, J., Dodda, A., Burugulla, J. K. R., Paleti, S., & Challa, K. (2021). Innovative Financial Technologies: Strengthening Compliance, Secure Transactions, and Intelligent Advisory Systems Through AI-Driven Automation and Scalable Data Architectures. Universal Journal of Finance and Economics, 1(1), 123–143. Retrieved from https://www.scipublications.com/journal/index.php/ujfe/article/view/1298
  7. 7. Adusupalli, B., Singireddy, S., Sriram, H. K., Kaulwar, P. K., & Malempati, M. (2021). Revolutionizing Risk Assessment and Financial Ecosystems with Smart Automation, Secure Digital Solutions, and Advanced Analytical Frameworks. Universal Journal of Finance and Economics, 1(1), 101–122. Retrieved from https://www.scipublications.com/journal/index.php/ujfe/article/view/1297
  8. 8. Gadi, A. L., Kannan, S., Nandan, B. P., Komaragiri, V. B., & Singireddy, S. (2021). Advanced Computational Technologies in Vehicle Production, Digital Connectivity, and Sustainable Transportation: Innovations in Intelligent Systems, Eco-Friendly Manufacturing, and Financial Optimization. Universal Journal of Finance and Economics, 1(1), 87–100. Retrieved from https://www.scipublications.com/journal/index.php/ujfe/article/view/1296
  9. 9. Cloud Native Architecture for Scalable Fintech Applications with Real Time Payments. (2021). International Journal of Engineering and Computer Science, 10(12), 25501-25515. https://doi.org/10.18535/ijecs.v10i12.4654
  10. 10. Pallav Kumar Kaulwar. (2021). From Code to Counsel: Deep Learning and Data Engineering Synergy for Intelligent Tax Strategy Generation. Journal of International Crisis and Risk Communication Research , 1–20. Retrieved from https://jicrcr.com/index.php/jicrcr/article/view/2967
  11. 11. Chinta, P. C. R., & Katnapally, N. (2021). Neural Network-Based Risk Assessment for Cybersecurity in Big Data-Oriented ERP Infrastructures. Neural Network-Based Risk Assessment for Cybersecurity in Big Data-Oriented ERP Infrastructures.
  12. 12. Katnapally, N., Chinta, P. C. R., Routhu, K. K., Velaga, V., Bodepudi, V., & Karaka, L. M. (2021). Leveraging Big Data Analytics and Machine Learning Techniques for Sentiment Analysis of Amazon Product Reviews in Business Insights. American Journal of Computing and Engineering, 4(2), 35-51.
  13. 13. Routhu, K., Bodepudi, V., Jha, K. M., & Chinta, P. C. R. (2020). A Deep Learning Architectures for Enhancing Cyber Security Protocols in Big Data Integrated ERP Systems. Available at SSRN 5102662.
  14. 14. Chinta, P. C. R., & Karaka, L. M.(2020). AGENTIC AI AND REINFORCEMENT LEARNING: TOWARDS MORE AUTONOMOUS AND ADAPTIVE AI SYSTEMS.