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Communication Dans Un Congrès Année : 2018

Machine Learning For Security: The Case of Side-Channel Attack Detection at Run-time

Maria Mushtaq
Muhammad Khurram Bhatti
  • Fonction : Auteur
Maham Chaudhry
  • Fonction : Auteur
Muneeb Yousaf
  • Fonction : Auteur
Umer Farooq
  • Fonction : Auteur
Vianney Lapotre
Guy Gogniat

Résumé

This paper presents experimental evaluation and comparative analysis on the use of various Machine Learning (ML) models for detecting Cache-based Side Channel Attacks (CSCAs) in Intel's x86 architecture. The paper provides performance evaluation of ML models based on run-time detection accuracy, speed, computational overhead, and distribution of error in terms of false positives and false negatives. Experiments are performed using state-of-the-art CSCAs namely; Flush+Reload and Flush+Flush attacks, under realistic load conditions on RSA and AES crypto-systems. The paper provides quantitative & qualitative analysis of at least 12 ML models being used for CSCA detection for the first time.
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Dates et versions

hal-01876792 , version 1 (18-09-2018)

Identifiants

  • HAL Id : hal-01876792 , version 1

Citer

Maria Mushtaq, Ayaz Akram, Muhammad Khurram Bhatti, Maham Chaudhry, Muneeb Yousaf, et al.. Machine Learning For Security: The Case of Side-Channel Attack Detection at Run-time. ICECS-2018, Dec 2018, Bordeaux, France. ⟨hal-01876792⟩
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