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An Efficient Approach for Side Channel Attack in Cloud Computing

  • Aakash
  • , Vivek Kumar Pandey
  • , Shiv Prakash*
  • , Sudhakar Singh
  • , Tiansheng Yang
  • , Rajkumar Singh Rathore
  • *Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygl Cynhadleddadolygiad gan gymheiriaid

2 Dyfyniadau (Scopus)

Crynodeb

The delivery of information technology services via the internet is referred to as cloud computing where instead of storing data on a local computer or server, it is stored on remote servers that can be accessed from anywhere with an internet connection. Cloud computing provides a cost-effective resources to develop machine learning and deep learning algorithms faster. Attacks using side-channels in virtualized computing are a risk to the safeguarding of confidential data stored in cloud. These attacks exploit the physical and implementation-related aspects of cloud computing environments, such as power consumption, electromagnetic emissions, and timing behaviours, to leverage secret data, such as cryptographic keys and passwords. With the growing prevalence of cloud computing, it is essential to comprehend and address the vulnerabilities presented by Side-Channel Attacks (SCAs). In response to these hazards, Cloud Service Providers (CSPs) and security researchers are exploring new methods for detecting and preventing these attacks. These methods include hardware-based security solutions, such as Trusted Platform Modules (TPMs), and software-based countermeasures, such as randomizing encryption keys and using secure coding practices. The partnership among CSPs and security researchers is vital to guarantee the confidentiality and integrity of cloud-based data and applications and prevent SCAs in cloud computing.
Iaith wreiddiolSaesneg
Tudalennau (o-i)1404-1413
Nifer y tudalennau10
CyfnodolynProcedia Computer Science
Cyfrol258
Dyddiad ar-lein cynnar10 Mai 2025
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 10 Mai 2025
Digwyddiad3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024 - Dehradun, India
Hyd: 28 Tach 202429 Tach 2024

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