Machine Learning Based and Reconfigurable Architecture with a Countermeasure for Side Channel Attacks

2022 ◽  
pp. 175-187
Author(s):  
Babu Illuri ◽  
Deepa Jose ◽  
Shiela David ◽  
M. Nagarjuan
Keyword(s):  
Machine Learning ◽  
Reconfigurable Architecture ◽  
Side Channel ◽  
Side Channel Attacks

scholarly journals Real-Time Detection for Cache Side Channel Attack using Performance Counter Monitor

2020 ◽  
Vol 10 (3) ◽  
pp. 984 ◽  
Author(s):  
Jonghyeon Cho ◽  
Taehun Kim ◽  
Soojin Kim ◽  
Miok Im ◽  
Taehyun Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Detection Method ◽  
Machine Learning Algorithms ◽  
Side Channel ◽  
Side Channel Attacks ◽  
Detection Techniques ◽  
Performance Counter ◽  
Secret Information ◽  
Real Time Detection

Cache side channel attacks extract secret information by monitoring the cache behavior of a victim. Normally, this attack targets an L3 cache, which is shared between a spy and a victim. Hence, a spy can obtain secret information without alerting the victim. To resist this attack, many detection techniques have been proposed. However, these approaches have limitations as they do not operate in real time. This article proposes a real-time detection method against cache side channel attacks. The proposed technique performs the detection of cache side channel attacks immediately after observing a variation of the CPU counters. For this, Intel PCM (Performance Counter Monitor) and machine learning algorithms are used to measure the value of the CPU counters. Throughout the experiment, several PCM counters recorded changes during the attack. From these observations, a detecting program was implemented by using these counters. The experimental results show that the proposed detection technique displays good performance for real-time detection in various environments.

scholarly journals Side-Channel Countermeasures’ Dissection and the Limits of Closed Source Security Evaluations

2020 ◽  
pp. 1-25 ◽  
Author(s):  
Olivier Bronchain ◽  
François-Xavier Standaert
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Side Channel ◽  
Learning Tools ◽  
Side Channel Attacks ◽  
Data Dependencies ◽  
Security Evaluations ◽  
Systèmes D’Information ◽  
Closed Source ◽  
Leakage Assessment

We take advantage of a recently published open source implementation of the AES protected with a mix of countermeasures against side-channel attacks to discuss both the challenges in protecting COTS devices against such attacks and the limitations of closed source security evaluations. The target implementation has been proposed by the French ANSSI (Agence Nationale de la Sécurité des Systèmes d’Information) to stimulate research on the design and evaluation of side-channel secure implementations. It combines additive and multiplicative secret sharings into an affine masking scheme that is additionally mixed with a shuffled execution. Its preliminary leakage assessment did not detect data dependencies with up to 100,000 measurements. We first exhibit the gap between such a preliminary leakage assessment and advanced attacks by demonstrating how a countermeasures’ dissection exploiting a mix of dimensionality reduction, multivariate information extraction and key enumeration can recover the full key with less than 2,000 measurements. We then discuss the relevance of open source evaluations to analyze such implementations efficiently, by pointing out that certain steps of the attack are hard to automate without implementation knowledge (even with machine learning tools), while performing them manually is straightforward. Our findings are not due to design flaws but from the general difficulty to prevent side-channel attacks in COTS devices with limited noise. We anticipate that high security on such devices requires significantly more shares.

scholarly journals It Started with Templates: The Future of Profiling in Side-Channel Analysis

2021 ◽  
pp. 133-145
Author(s):  
Lejla Batina ◽  
Milena Djukanovic ◽  
Annelie Heuser ◽  
Stjepan Picek
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Side Channel ◽  
Side Channel Attacks ◽  
Worst Case ◽  
Learning Techniques ◽  
The Future ◽  
First Results ◽  
Channel Analysis

AbstractSide-channel attacks (SCAs) are powerful attacks based on the information obtained from the implementation of cryptographic devices. Profiling side-channel attacks has received a lot of attention in recent years due to the fact that this type of attack defines the worst-case security assumptions. The SCA community realized that the same approach is actually used in other domains in the form of supervised machine learning. Consequently, some researchers started experimenting with different machine learning techniques and evaluating their effectiveness in the SCA context. More recently, we are witnessing an increase in the use of deep learning techniques in the SCA community with strong first results in side-channel analyses, even in the presence of countermeasures. In this chapter, we consider the evolution of profiling attacks, and subsequently we discuss the impacts they have made in the data preprocessing, feature engineering, and classification phases. We also speculate on the future directions and the best-case consequences for the security of small devices.

Victims Can Be Saviors

2021 ◽  
Vol 17 (2) ◽  
pp. 1-31
Author(s):  
Manaar Alam ◽  
Sarani Bhattacharya ◽  
Debdeep Mukhopadhyay
Keyword(s):  
Machine Learning ◽  
Security Analysis ◽  
False Positives ◽  
Machine Learning Techniques ◽  
Side Channel ◽  
Detection Accuracy ◽  
Secret Key ◽  
Side Channel Attacks ◽  
Detection Mechanism ◽  
Novel Approach

Micro-architectural side-channel attacks are major threats to the most mathematically sophisticated encryption algorithms. In spite of the fact that there exist several defense techniques, the overhead of implementing the countermeasures remains a matter of concern. A promising strategy is to develop online detection and prevention methods for these attacks. Though some recent studies have devised online prevention mechanisms for some categories of these attacks, still other classes remain undetected. Moreover, to detect these side-channel attacks with minimal False Positives is a challenging effort because of the similarity of their behavior with computationally intensive applications. This article presents a generalized machine learning--based multi-layer detection technique that targets these micro-architectural side-channel attacks, while not restricting its attention only on a single category of attacks. The proposed mechanism gathers low-level system information by profiling performance counter events using Linux perf tool and then applies machine learning techniques to analyze the data. A novel approach using time-series analysis of the data is implemented to find out the correlation of the execution trace of the attack process with the secret key of encryption, which helps in dealing with False-Positives and unknown attacks. This article also provides a detailed theoretical analysis of the detection mechanism of the proposed model along with its security analysis. The experimental results show that the proposed method is superior to the state-of-the-art reported techniques with high detection accuracy, low False Positives, and low implementation overhead while being able to detect before the completion of the attack.

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