Proof of Reverse Engineering Barrier: SEM Image Analysis on Covert Gates

2021 ◽  
Author(s):  
Tasnuva Farheen ◽  
Ulbert Botero ◽  
Nitin Varshney ◽  
Damon L. Woodard ◽  
Mark Tehranipoor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Reverse Engineering ◽  
Contact Structures ◽  
Imaging Analysis ◽  
Sem Image ◽  
Device Structures ◽  
Microscope Imaging ◽  
Sem Imaging ◽  
The Difference ◽  
Voltage Contrast

Abstract IC camouflaging has been proposed as a promising countermeasure against malicious reverse engineering. Camouflaged gates contain multiple functional device structures, but appear as one single layout under microscope imaging, thereby hiding the real circuit functionality from adversaries. The recent covert gate camouflaging design comes with a significantly reduced overhead cost, allowing numerous camouflaged gates in circuits and thus being resilient against various invasive and semi-invasive attacks. Dummy inputs are used in the design, but SEM imaging analysis was only performed on simplified dummy contact structures in prior work. Whether the e-beam during SEM imaging will charge differently on different contacts and further reveal the different structures or not requires extended research. In this study, we fabricated real and dummy contacts in various structures and performed a systematic SEM imaging analysis to investigate the possible charging and the consequent passive voltage contrast on contacts. In addition, machine-learning based pattern recognition was also employed to examine the possibility of differentiating real and dummy contacts. Based on our experimental results, we found that the difference between real and dummy contacts is insignificant in SEM imaging, which effectively prevents adversarial SEM-based reverse engineering. Index Terms—Reverse Engineering, IC Camouflaging, Scanning Electron Microscopy, Machine Learning, Countermeasure.

Trojan Scanner: Detecting Hardware Trojans with Rapid SEM Imaging Combined with Image Processing and Machine Learning

2018 ◽  
Author(s):  
Nidish Vashistha ◽  
Hangwei Lu ◽  
Qihang Shi ◽  
M Tanjidur Rahman ◽  
Haoting Shen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Integrated Circuits ◽  
Reverse Engineering ◽  
Physical Structure ◽  
Supervised Machine Learning ◽  
Hardware Trojan ◽  
Hardware Trojans ◽  
Sem Images ◽  
Sem Image ◽  
Machine Learning Model

Abstract Hardware Trojans are malicious changes to the design of integrated circuits (ICs) at different stages of the design and fabrication processes. Different approaches have been developed to detect Trojans namely non-destructive (electrical tests like run-time monitoring, functional and structural tests) and destructive (full chip reverse engineering). However, these methods cannot detect all types of Trojans and they suffer from a number of disadvantages such as slow speed of detection and lack of confidence in detecting all types of Trojans. Majority of hardware Trojans implemented in an IC will leave a footprint at the doping (active) layer. In this paper, we introduce a new version of our previously developed “Trojan Scanner” [1] framework for the untrusted foundry threat model, where a trusted GDSII layout (golden layout) is available. Advanced computer vision algorithms in combination with the supervised machine-learning model are used to classify different features of the golden layout and SEM images from an IC under authentication, as a unique descriptor for each type of gates. These descriptors are compared with each other to detect any subtle changes on the active region, which can raise the flag for the existence of a potential hardware Trojan. The descriptors can differentiate variation due to fabrication process, defects, and common SEM image distortions to rule out the possibility of false detection. Our results demonstrate that Trojan Scanner is more reliable than electrical testing and faster than full chip reverse engineering. Trojan Scanner does not rely on the functionality of the circuit rather focuses on the real physical structure to detect malicious changes inserted by the untrusted foundry.

Circuit Tracing on Integrated Circuit Using FIB Passive Voltage Contrast Effect

2015 ◽  
Author(s):  
Alexander Sorkin ◽  
Chris Pawlowicz ◽  
Alex Krechmer ◽  
Michael W. Phaneuf
Keyword(s):  
Electron Microscope ◽  
Integrated Circuits ◽  
Integrated Circuit ◽  
Metal Layer ◽  
Database System ◽  
Conventional Procedure ◽  
Sem Imaging ◽  
Voltage Contrast ◽  
Scanning Electron ◽  
Graphic Database

Abstract Competitive circuit analysis of Integrated Circuits (ICs) is one of the most challenging types of analysis. It involves multiple complex IC die de-processing/de-layering steps while keeping precise planarity from metal layer to metal layer. Each step is followed by Scanning Electron Microscope (SEM) imaging together with mosaicking that subsequently passes through an image recognition and Graphic Database System (GDS) conversion process. This conventional procedure is quite time and resource consuming. The current paper discusses and demonstrates a new inventive methodology of circuit tracing on an IC using known FIB Passive Voltage Contrast (PVC) effects [1]. This technique provides significant savings in time and resources.

scholarly journals Machine Learning Model of Dimensionless Numbers to Predict Flow Patterns and Droplet Characteristics for Two-Phase Digital Flows

2021 ◽  
Vol 11 (9) ◽  
pp. 4251
Author(s):  
Jinsong Zhang ◽  
Shuai Zhang ◽  
Jianhua Zhang ◽  
Zhiliang Wang
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Digital Microfluidics ◽  
Flow Patterns ◽  
Machine Learning Algorithms ◽  
Dimensionless Numbers ◽  
Two Phase ◽  
The Difference ◽  
Input Variables ◽  
Digital Microfluidic

In the digital microfluidic experiments, the droplet characteristics and flow patterns are generally identified and predicted by the empirical methods, which are difficult to process a large amount of data mining. In addition, due to the existence of inevitable human invention, the inconsistent judgment standards make the comparison between different experiments cumbersome and almost impossible. In this paper, we tried to use machine learning to build algorithms that could automatically identify, judge, and predict flow patterns and droplet characteristics, so that the empirical judgment was transferred to be an intelligent process. The difference on the usual machine learning algorithms, a generalized variable system was introduced to describe the different geometry configurations of the digital microfluidics. Specifically, Buckingham’s theorem had been adopted to obtain multiple groups of dimensionless numbers as the input variables of machine learning algorithms. Through the verification of the algorithms, the SVM and BPNN algorithms had classified and predicted the different flow patterns and droplet characteristics (the length and frequency) successfully. By comparing with the primitive parameters system, the dimensionless numbers system was superior in the predictive capability. The traditional dimensionless numbers selected for the machine learning algorithms should have physical meanings strongly rather than mathematical meanings. The machine learning algorithms applying the dimensionless numbers had declined the dimensionality of the system and the amount of computation and not lose the information of primitive parameters.

scholarly journals SVM-Based Bearing Anomaly Identification with Self-Tuning Network-Fuzzy Robust Proportional Multi Integral and Smart Autoregressive Model

2021 ◽  
Vol 11 (6) ◽  
pp. 2784
Author(s):  
Shahnaz TayebiHaghighi ◽  
Insoo Koo
Keyword(s):  
Machine Learning ◽  
Variable Structure ◽  
Rolling Element Bearing ◽  
Crack Identification ◽  
Original Signal ◽  
Signal Modeling ◽  
Residual Signal ◽  
Average Accuracy ◽  
Self Tuning ◽  
The Difference

In this paper, the combination of an indirect self-tuning observer, smart signal modeling, and machine learning-based classification is proposed for rolling element bearing (REB) anomaly identification. The proposed scheme has three main stages. In the first stage, the original signal is resampled, and the root mean square (RMS) signal is extracted from it. In the second stage, the normal resampled RMS signal is approximated using the AutoRegressive with eXternal Uncertainty (ARXU) technique. Moreover, the nonlinearity of the bearing signal is solved using the combination of the ARXU and the machine learning-based regression, which is called AMRXU. After signal modeling by AMRXU, the RMS resampled signal is estimated using a combination of the proportional multi-integral (PMI) technique, the variable structure (VS) Lyapunov technique, and a self-tuning network-fuzzy system (SNFS). Finally, in the third stage, the difference between the original signal and the estimated one is calculated to generate the residual signal. A machine learning-based classification technique is utilized to classify the residual signal. The Case Western Reserve University (CWRU) dataset is used to evaluate anomaly identification performance of the proposed scheme. Regarding the experimental results, the average accuracy for REB crack identification is 98.65%, 97.7%, 97.35%, and 97.67%, respectively, when the motor torque loads are 0-hp, 1-hp, 2-hp, and 3-hp.

scholarly journals Data mining of coronavirus: SARS-CoV-2, SARS-CoV and MERS-CoV

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jung Eun Huh ◽  
Seunghee Han ◽  
Taeseon Yoon
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
Decision Tree ◽  
Machine Learning Algorithms ◽  
High Similarity ◽  
Incubation Periods ◽  
Initial Question ◽  
The Difference ◽  
Blast Program

Abstract Objective In this study we compare the amino acid and codon sequence of SARS-CoV-2, SARS-CoV and MERS-CoV using different statistics programs to understand their characteristics. Specifically, we are interested in how differences in the amino acid and codon sequence can lead to different incubation periods and outbreak periods. Our initial question was to compare SARS-CoV-2 to different viruses in the coronavirus family using BLAST program of NCBI and machine learning algorithms. Results The result of experiments using BLAST, Apriori and Decision Tree has shown that SARS-CoV-2 had high similarity with SARS-CoV while having comparably low similarity with MERS-CoV. We decided to compare the codons of SARS-CoV-2 and MERS-CoV to see the difference. Though the viruses are very alike according to BLAST and Apriori experiments, SVM proved that they can be effectively classified using non-linear kernels. Decision Tree experiment proved several remarkable properties of SARS-CoV-2 amino acid sequence that cannot be found in MERS-CoV amino acid sequence. The consequential purpose of this paper is to minimize the damage on humanity from SARS-CoV-2. Hence, further studies can be focused on the comparison of SARS-CoV-2 virus with other viruses that also can be transmitted during latent periods.

Non-Destructive Measurements of III-V Semiconductor Device Structure by a Hard X-ray Microprobe

1991 ◽  
Vol 240 ◽  
Author(s):  
F. Uchida ◽  
J. Shigeta ◽  
Y. SUZUKI
Keyword(s):  
Semiconductor Device ◽  
Film Structure ◽  
Device Structure ◽  
X Ray ◽  
Model Sample ◽  
Device Structures ◽  
The Difference ◽  
X Ray Absorption ◽  
Non Destructive ◽  
Non Destructive Characterization

ABSTRACTA non-destructive characterization technique featuring a hard X-ray Microprobe is demonstrated for lll-V semiconductor device structures. A GaAs FET with a 2 μm gate length is measured as a model sample of a thin film structure. X-ray scanning microscopic images of the FET are obtained by diffracted X-ray and fluorescence X-ray detection. Diffracted X-ray detection measures the difference in gate material and source or drain material as a gray level difference on the image due to the X-ray absorption ratio. Ni Ka fluorescence detection, on the other hand, provides imaging of 500 Å thick Ni layers, which are contained only in the source and drain metals, through non-destructive observation.

SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation

2021 ◽  
Author(s):  
Yashas Samaga B L ◽  
Shampa Raghunathan ◽  
U. Deva Priyakumar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Protein Stability ◽  
Data Augmentation ◽  
Stability Prediction ◽  
Structural Environment ◽  
Self Consistent ◽  
The Difference ◽  
Protein Sequence Space ◽  
New Machine

<div>Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protein sequence space thoroughly are laborious. To this end, many machine learning based methods have been developed to predict thermodynamic stability changes upon mutation. These methods have been evaluated for symmetric consistency by testing with hypothetical reverse mutations. In this work, we propose transitive data augmentation, evaluating transitive consistency, and a new machine learning based method, first of its kind, that incorporates both symmetric and transitive properties into the architecture. Our method, called SCONES, is an interpretable neural network that estimates a residue's contributions towards protein stability dG in its local structural environment. The difference between independently predicted contributions of the reference and mutant residues in a missense mutation is reported as dG. We show that this self-consistent machine learning architecture is immune to many common biases in datasets, relies less on data than existing methods, and is robust to overfitting.</div><div><br></div>

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

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