How to Compare Selections of Points of Interest for Side-Channel Distinguishers in Practice?

Abstract—Currently, one of the most powerful side channel attacks (SCA) is profiled attack. Machine learning algorithms, for example support vector machine (SVM), are currently used to improve the effectiveness of the attack. One issue of using SVM-based profiled attack is extracting points of interest (POIs), or features from power traces. Our work proposes a novel method for POIs selection of power traces based on the combining variational mode decomposition (VMD) and Gram-Schmidt orthogonalization (GSO). VMD is used to decompose the power traces into sub-signals (modes) and POIs selection process based on GSO is conducted on these sub-signals. As a result, the selected POIs are used for SVM classifier to conduct profiled attack. This attack method outperforms other profiled attacks in the same attack scenario. Experiments were performed on a trace data set collected from the Atmega8515 smart card with AES-128 run on the Sakura-G/W side channel evaluation board and the DPA Contest v4 dataset to verify the effectiveness of our method in reducing number of power traces for the attacks, especially with noisy power traces.Tóm tắt—Hiện nay, tấn công mẫu được xem là một trong những tấn công kênh kề (SCA) mạnh. Các thuật toán học máy, ví dụ như máy vector hỗ trợ (SVM), thường được sử dụng để nâng cao hiệu quả của tấn công mẫu. Một thách thức đối với tấn công mẫu sử dụng SVM là cần phải tìm được các điểm thích hợp (POI) hay các đặc trưng từ vết điện năng tiêu thụ. Công trình nghiên cứu này đề xuất một phương pháp mới đề tìm POI của vết điện năng tiêu thụ bằng cách kết hợp kỹ thuật phân tích mode biến phân (VMD) và quá trình trực giao hóa Gram-Schmidt (GSO). Trong đó, VMD được sử dụng để phân tách vết điện năng tiêu thụ thành các tín hiệu con còn gọi là VMD mode và việc lựa chọn POIs trên VMD mode này được thực hiện dựa trên quá trình GSO. Dựa trên phương pháp lựa chọn POIs này, chúng tôi đề xuất phương pháp tấn công mẫu sử dụng SVM có hiệu quả tốt hơn các tấn công mẫu khác ở cùng kịch bản tấn công. Các thí nghiệm tấn công được thực hiện trên tập dữ liệu được thu thập từ thẻ thông minh Atmega8515 cài đặt AES-128 chạy trên nền tảng thiết bị tấn công kênh kề Sakura-G/W và tập dữ liệu DPA Contest v4, để chứng minh tính hiệu quả của phương pháp của chúng tôi, trong việc giảm số lượng vết điện năng tiêu thụ cần cho cuộc tấn công, đặc biệt trong trường hợp các điện năng tiêu thụ có nhiễu.

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EFFICIENT CNN-BASED PROFILED SIDE CHANNEL ATTACKS

Journal of Computer Science and Cybernetics ◽

10.15625/1813-9663/37/1/15418 ◽

2021 ◽

Vol 37 (1) ◽

pp. 1-22

Author(s):

Ngoc Quy Tran ◽

Hong Quang Nguyen

Keyword(s):

Neural Network ◽

Deep Learning ◽

Side Channel ◽

Grey Wolf ◽

Side Channel Attacks ◽

Grey Wolf Optimization ◽

Points Of Interest ◽

Public Dataset ◽

The Right ◽

Dpa Contest

Profiled side-channel attacks are now considered as powerful forms of attacks used to break the security of cryptographic devices. A recent line of research has investigated a new profiled attack based on deep learning and many of them have used convolution neural network (CNN) as deep learning architecture for the attack. The effectiveness of the attack is greatly influenced by the CNN architecture. However, the CNN architecture used for current profiled attacks have often been based on image recognition fields, and choosing the right CNN architectures and parameters for adaption to profiled attacks is still challenging. In this paper, we propose an efficient profiled attack for on unprotected and masking-protected cryptographic devices based on two CNN architectures, called CNNn, CNNd respectively. Both of CNN architecture parameters proposed in this paper are based on the property of points of interest on the power trace and further determined by the Grey Wolf Optimization (GWO) algorithm. To verify the proposed attacks, experiments were performed on a trace set collected from an Atmega8515 smart card when it performs AES-128 encryption, a DPA contest v4 dataset and the ASCAD public dataset

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Theory and Errors in Stereo Electron Microscopy

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100101426 ◽

1968 ◽

Vol 26 ◽

pp. 336-337

Author(s):

J. M. Pankratz

Keyword(s):

Electron Microscopy ◽

Transmission Electron Microscopy ◽

Plane Wave ◽

Focal Length ◽

Foil Thickness ◽

Points Of Interest ◽

Transmission Electron ◽

Vertical Spacing ◽

Illuminating System

It is often desirable in transmission electron microscopy to know the vertical spacing of points of interest within a specimen. However, in order to measure a stereo effect, one must have two pictures of the same area taken from different angles, and one must have also a formula for converting measured differences between corresponding points (parallax) into a height differential.Assume (a) that the impinging beam of electrons can be considered as a plane wave and (b) that the magnification is the same at the top and bottom of the specimen. The first assumption is good when the illuminating system is overfocused. The second assumption (the so-called “perspective error”) is good when the focal length is large (3 x 107Å) in relation to foil thickness (∼103 Å).

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Small Computer System for Micrograph Analysis

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100097879 ◽

1981 ◽

Vol 39 ◽

pp. 270-271

Author(s):

J. F. Hainfeld ◽

J. S. Wall

Keyword(s):

Large Range ◽

Small Computer ◽

Black And White ◽

Contour Maps ◽

Points Of Interest ◽

Computer Aided Analysis ◽

Specialized Hardware ◽

Additional Memory ◽

Selection Of ◽

Publication Quality

Cost reduction and availability of specialized hardware for image processing have made it reasonable to purchase a stand-alone interactive work station for computer aided analysis of micrographs. Some features of such a system are: 1) Ease of selection of points of interest on the micrograph. A cursor can be quickly positioned and coordinates entered with a switch. 2) The image can be nondestructively zoomed to a higher magnification for closer examination and roaming (panning) can be done around the picture. 3) Contrast and brightness of the picture can be varied over a very large range by changing the display look-up tables. 4) Marking items of interest can be done by drawing circles, vectors or alphanumerics on an additional memory plane so that the picture data remains intact. 5) Color pictures can easily be produced. Since the human eye can detect many more colors than gray levels, often a color encoded micrograph reveals many features not readily apparent with a black and white display. Colors can be used to construct contour maps of objects of interest. 6) Publication quality prints can easily be produced by taking pictures with a standard camera of the T.V. monitor screen.

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