Multimodal Intrusion Detection System for Cyber Physical Systems

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

Cyber-Physical Systems (CPS) are deployed to control critical infrastructure in many fields, including industry and manufacturing. In recent years, CPS have been affected by cyberattacks due to the increased connectivity of these systems to the Internet. This work aims to develop a deep learning-based Intrusion Detection System (IDS) for detecting cyberattacks on CPS using multimodal learning techniques. This thesis reports the design, implementation, and evaluation of two IDS solutions based on different deep learning networks: Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). For the first IDS, Gramian Angular Field (GAF) is used to convert CPS time-series data to images that are fed to a 3D CNN to train the attack detection classifier. The second IDS uses RNN with a multimodal attention approach for training the attack detector. Both solutions utilize CPS process data and networkdata to improve the attack detection accuracy. The performance of the proposed approaches is evaluated on SWaT datasets collected from a testbed that represents real world CPS. Experimental results demonstrate that both IDSs achieved improved performance and higher detection capability compared to related work.

روابط وملفات

التعريف والنوع

رقم الوثيقة

08b821ea-bddc-4114-ab67-4c24b6a234f0

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل ماجيستير

نوع الملف

pdf text

أسماء الملفات

2017795_1.pdf

بيانات النشر

ألقاب المؤلفين

[{"name_ar":"سهيلة صالح Eltanbouly","title_ar":"اعداد","title_en":"Preparation"},{"name_ar":"عبدالكريم إرادي","title_ar":"اشراف","title_en":"Supervision"}]

اللغة

English

المصدر والدورية

اسم المصدر

Multimodal Intrusion Detection System for Cyber Physical Systems

المحتوى والصفحات

عدد الصفحات

0

إشراف وإعداد

الإشراف

عبدالكريم إرادي

الإعداد

سهيلة صالح Eltanbouly

الاقتباسات الببليوغرافية