Deep Learning Method for Power Side-Channel Analysis on Chip Leakages

Ahmed, Amjed Abbas; Salim, Rana Ali; Hasan, Mohammad Kamrul

doi:10.5755/j02.eie.34650

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…As an example, the amount of time required to carry out a successful attack as well as the amount of effort required to carry out the assault, often known as the degree of difficulty, are evaluated as part of the Common Criteria security evaluation of a device. According to [9], the total security grade of the chip is impacted by both of these criteria. In a nutshell, trust in security evaluation entails taking into consideration the most dangerous adversary, which has an effect on the attack tactics that are chosen [10].…”

Section: Introductionmentioning

confidence: 99%

Design of Deep Learning Technique Based Side Channel Attack Analysis for System on Chips

Fattah

2024

IJPS

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The effectiveness of deep learning techniques has increased in recent years, making it possible to assess side channel attacks on System-on-Chips (SoCs). Specifically, this is due to the fact that they provide a sophisticated method to capitalise on the unintended loss of information that occurs during cryptographic procedures. In this particular scenario, it is very necessary to collect information on side channels, such as power consumption or electromagnetic emissions. There is a procedure called as preprocessing that involves cleaning and modifying the raw data in order to make it more acceptable for input into neural networks. The specifics of the side channel information are what define whether or not a deep learning architecture, such as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, or other specialist structures, should be used. Following this, the architecture of the model is painstakingly created, consisting of layers, units, and activation functions, with the purpose of effectively collecting and deciphering the intricate patterns that are associated with the processing of sensitive data on the SOC.As part of this study, we presented a CNN-dependent profiled SCA assault that was directed against an AES cypher that was operating on a SoC. During the phase of setup, we focus on the discharge from a minor capacitor that is linked with the principal line of power supply. This capacitor produces a distinct signal, and we do not capture electromagnetic emission of chip surface. When it comes to the matching and profiling phases, CNN is then used. In light of the fact that the most recent AES round did not uncover any leaks, it is recommended that a novel approach be used in order to retrieve the keys from the first round, Through the use of AES algorithm observations, we were able to develop a differential equation that included the selected intermediate value and basic text. This equation in the end resulted in possibility of advantageous for incorrect value.

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Section: Introductionmentioning

confidence: 99%

Design of Deep Learning Technique Based Side Channel Attack Analysis for System on Chips

Fattah

2024

IJPS

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Review on Hybrid Deep Learning Models for Enhancing Encryption Techniques Against Side Channel Attacks

Ahmed,

Hasan,

Aman

et al. 2024

IEEE Access

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Building a Resilient Architecture with an Intelligent System Based on Support Vector Machines Algorithm for Cybersecurity

Alzuabidi

2024

JECNAM

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This research focuses on establishing a competent and sustainable cybersecurity structure stimulated by Support Vector Machine (SVM) algorithms based on detection of intrusions. The paper first provides a clear and concise research method that builds on the benchmark dataset known as the KDD Cup 1999 dataset. In particular, with the help of the data collection, preprocessing, and feature selection, the SVM model gives the opportunity to classify different types of the network attack, such as DoS attack or the user-to-root attack. The systematic approach ensures that only the favorable feature is considered in the model making the model to note the difference between normal traffic and attack traffic. From this study, the developed model was accurate and efficient, with the classification accuracy being 98.7% and F1-score of 96.7% respectively which demonstrated the efficiency of the model in real world applications.Besides, the development of the model, the structure also includes components like real-time control and automatic response. This integration enables the system to scrutinize network traffic in real time and take an appropriate action in case of a threat. Through the automated alerts and the mitigation actions that must be taken once the intrusion is detected the architecture not only identifies infringements but also corrects the violations taking place in the network. This proactive approach is rather helpful nowadays, as the threats are already very high and come very frequently on the digital level. Hazard response capability further strengthens the cybersecurity system, thus crucial in reducing vulnerability and system outages.

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Deep Learning Method for Power Side-Channel Analysis on Chip Leakages

Cited by 3 publications

References 20 publications

Design of Deep Learning Technique Based Side Channel Attack Analysis for System on Chips

Design of Deep Learning Technique Based Side Channel Attack Analysis for System on Chips

Review on Hybrid Deep Learning Models for Enhancing Encryption Techniques Against Side Channel Attacks

Building a Resilient Architecture with an Intelligent System Based on Support Vector Machines Algorithm for Cybersecurity

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