PRISENODE · Privacy- and secuRity-aware solutIons in SoftwarE-defiNed fOg Data cEnter
„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“
- Период
- 2020-01-01 → 2022-12-31
- Финансиране от ЕС
- 275 210 €
- Участници
- 2
- Схема
- MSCA-IF
Линиите свързват координатора с партньорите.
Накратко на български
Модели за машинно обучение се тестват за устойчивост срещу манипулиране на данни, например при разпознаване на зловреден софтуер в Android устройства. Това помага за по-добра защита на информацията и сигурността на мрежовия трафик.
Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.
Резултати накратко
PRISENODE: Privacy- and secuRity-aware solutIons in SoftwarE-defiNed fOg Data cEnter
What is the problem/issue being addressed? We published a paper entitled 'On Defending Against Label Flipping Attacks on Malware Detection Systems' which is published in Springer, NCAA. Specifically, we design a robust machine learning (ML) model to protect the data against the label modifications of each feature (function) in the network traffic flows gathered from Android mobiles. To measure these objectives, the fellow created software using ML metrics, such as correctness and error rate and confirmed its novelty against the literature. Also, we evaluate the effects of data manipulation and how it influences the ML model functionalities. To confirm this, the fellow used several classification algorithms and imposed some data poisoning techniques such as generative adversarial network solution to clue the data and enhance the secrecy of the information that pose an issue for the adversary to detect the correct classification algorithm. It is in line with the milestone 2.1 and Delivery 2.1 of PRISENODE. As a result, It can be the summary of the context and overall objectives of PRISENODE on these three months. Why is it important for society? The final result is a service that includes some trained machine learning model that is robust and resilient against the sudden software changing in the environment data (mainly Android mobile data) and can protect data and routing them as data flow in the network to satisfy CAPEXOPEX and KPI of the network. What are the overall objectives? - Design a robust ML model which is against the label modifications of each feature (function) in the network traffic flows gathered from Android mobiles in line with the D2.1 - How the effects of the data manipulation will affect on the ML model functionalities in line with the D2.1 The project has been terminated after three months due to the family issue raised for the fellow. However, the research activity reached to publishing a journal paper in a related venue.
Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз
Цел на проекта
Major technological trends in information technology such as cloud computing, big data, and mobile computing are based on powerful computing resources. The ever-increasing demand for computing resources has led companies and cloud service providers (CSPs) such as Google, Amazon, and Microsoft to build large warehouse-sized data centers called cloud data centers (CDCs). CSPs incorporate software-defined networking (SDN) and virtualization in their CDCs to ensure full utilization of server resources and reduce the power and electricity that are consumed. Applying SDN in CDCs provides reliable Quality of Services (QoS) and satisfying the user-centric Quality of Experience (QoE) in CDCs that are called software-defined cloud data centers (SDDCs). SDDC faces resource management problem and under threaten of security and privacy issues. To the best of my knowledge, no any practical tool can provide robust solutions to these problems, and further investigation is needed. In this project, I integrate fog technology with SDDCs and design a scalable fog network to manage the cloud service demands as well as providing secure processing and traffic data privacy in SDDC. I named this project PRISENODE: Privacy- and secuRity-aware solutIons in SoftwarE-defiNed fOg Data cEnter. My project targets fog data center (FDC) which consists of SDN-enabled switches that are instantiated on an SDDC server and serving as edge switches (Fog Nodes; FNs). FNs accommodate small-size flows with limited response time and deliver high user QoE. In this way, I design a fundamental tool (open-source software) together with a holistic business model for privacy- and security-aware data traffic passed through SDN-enabled switches FDCs/CDCs. The salient feature of my project is to jointly monitor network traffic, validate network traffic policies, and detect malicious entities in the cloud system as well as introducing related security- and privacy-aware defenses in SDDCs.
Оригинален текст от CORDIS (на английски).
Участници
Връзки
Данни: CORDIS, © Европейски съюз