Machine learning for network resilience

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

Resilience is taking networks a step further beyond security. Security is one of the main concerns facing the improvement of new networking and communications systems. Another important challenge is verifying whether or not a system is working exactly as specified, hence ensuring its consistency. We argue that a resilient network is both a secure and consistent one. It is from this point that we start our thesis research. 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 solutions that may be applicable to different network environments. Another objective is to investigate and leverage the state-of-the-art AI techniques to enhance network performance in general and resiliency in particular. Being able to describe a specific network as consistent is a large step towards resiliency. Next to the importance of security lies the necessity of consistency verification. Attackers are currently focusing on targeting small and crucial goals such as network configurations or ow tables. These types of attacks would defy the whole purpose of a security system when built on top of an inconsistent network. Another important goal of our work is to propose a new AI-based consistency verification system, which

روابط وملفات

التعريف والنوع

رقم الوثيقة

ff05d3cd-2a01-46c4-b9bb-a6d78ea327a3

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل ماجيستير

نوع الملف

pdf text

أسماء الملفات

2025221_1.pdf

بيانات النشر

ألقاب المؤلفين

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اللغة

English

المصدر والدورية

اسم المصدر

Machine learning for network resilience

المحتوى والصفحات

عدد الصفحات

0

كلمات الباحثين

Computer networks -- Security measures. Artificial intelligence. Machine learning.

إشراف وإعداد

الإشراف

Ali Chehab , Ayman Kayssi

الإعداد

Ali Imad Hussein

الاقتباسات الببليوغرافية