Machine learning for network resilience: The start of a journey

• Published in: 2018 Fifth International Conference on Software Defined Systems (SDS) pp. 59 - 66
• Main Authors: Hussein, Ali, Chehab, Ali, Kayssi, Ayman, Elhajj, Imad H.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2018
• Subjects: Artificial intelligence; Machine learning; Network Resilience; Neurons; Real-time systems; Resilience; Robustness; Security; Software; Software defined networking; Testing
• DOI: 10.1109/SDS.2018.8370423

Security is one of the main concerns facing the development of new projects in networking and communications. Another challenge is to verify that a system is working exactly as specified. On the other hand, advances in Artificial Intelligence (AI) technology have opened up new markets and opportunities for progress in critical areas such as network resiliency, health, education, energy, economic inclusion, social welfare, and the environment. AI is expected to play an increasing role in defensive and offensive measures to provide a rapid response to react to the landscape of evolving threats. Software Defined Networking (SDN), being centralized by nature, provides a global view of the network. It is the flexibility and robustness offered by programmable networking that lead us to consider the integration of these two concepts, SDN and AI. Inspired by the fascinating tactics of the human immunity system, we aim to design a general hybrid Artificial Intelligence Resiliency System (ARS) that strikes a good balance between centralized and distributed security systems that may be applicable to different network environments. In addition, we aim to investigate and leverage the latest AI techniques to improve network performance in general and resiliency in particular.