Almeida, G.; Vasconcelos, F. Self-Healing Networks: Adaptive Responses to Ransomware Attacks. Preprints2023, 2023121538. https://doi.org/10.20944/preprints202312.1538.v1
APA Style
Almeida, G., & Vasconcelos, F. (2023). Self-Healing Networks: Adaptive Responses to Ransomware Attacks. Preprints. https://doi.org/10.20944/preprints202312.1538.v1
Chicago/Turabian Style
Almeida, G. and Felipe Vasconcelos. 2023 "Self-Healing Networks: Adaptive Responses to Ransomware Attacks" Preprints. https://doi.org/10.20944/preprints202312.1538.v1
Abstract
This study presents an in-depth analysis and evaluation of self-healing networks as an innovative solution to combat the escalating threat of ransomware attacks. Recognizing the limitations of traditional network security methods in the face of advanced cyber threats, the research focuses on the development and implementation of self-healing networks, characterized by their dynamic, intelligent response systems. Utilizing a combination of artificial intelligence and machine learning algorithms, these networks demonstrate remarkable adaptability and resilience. Through rigorous simulations, the study examines the efficacy of self-healing networks in detecting, isolating, and recovering from ransomware attacks, with an emphasis on their ability to learn and evolve from each incident. The research highlights the strengths and limitations of these networks and discusses their potential applications in various sectors. The findings suggest that self-healing networks, with their proactive and adaptive defense mechanisms, represent a significant advancement in cybersecurity strategies, offering a robust solution against a range of cyber threats. Future work is recommended to further enhance the capabilities of self-healing networks, particularly in the areas of speed, detection accuracy, and quantum-resistant security measures.
Computer Science and Mathematics, Computer Networks and Communications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
