scholarly journals Machine Learning Attacks and Countermeasures on Hardware Binary Edwards Curve Scalar Multipliers

2021 ◽  
Vol 10 (3) ◽  
pp. 56
Author(s):  
Charis Dimopoulos ◽  
Apostolos P. Fournaris ◽  
Odysseas Koufopavlou
Keyword(s):  
Machine Learning ◽  
Design Strategy ◽  
Machine Learning Techniques ◽  
Huge Number ◽  
Embedded Devices ◽  
Learning Techniques ◽  
Channel Analysis ◽  
Edwards Curve ◽  
Template Attacks ◽  
High Level

Machine Learning techniques have proven effective in Side Channel Analysis (SCA), enabling multiple improvements over the already-established profiling process of Template Attacks. Focusing on the need to mitigate their impact on embedded devices, a design model and strategy is proposed that can effectively be used as a backbone for introducing SCA countermeasures on Elliptic Curve Cryptography (ECC) scalar multipliers. The proposed design strategy is based on the decomposition of the round calculations of the Montgomery Power Ladder (MPL) algorithm and the Scalar Multiplication (SM) algorithm into the underlined finite field operations, and their restructuring into parallel-processed operation sets. Having as a basis the proposed design strategy, we showcase how advanced SCA countermeasures can be easily introduced, focusing on randomizing the projective coordinates of the MPL round’s ECC point results. To evaluate the design approach and its SCA countermeasures, several simple ML-based SCAs are performed, and an attack roadmap is provided. The proposed roadmap assumes attackers that do not have access to a huge number of leakage traces, and that have limited resources with which to mount Deep Learning attacks. The trained models’ performance reveals a high level of resistance against ML-based SCAs when including SCA countermeasures in the proposed design strategy.

Mining for Creativity: Determining the Creativity of Ideas Through Data Mining Techniques

2017 ◽  
Author(s):  
Christine A. Toh ◽  
Elizabeth M. Starkey ◽  
Conrad S. Tucker ◽  
Scarlett R. Miller
Keyword(s):  
Machine Learning ◽  
Design Research ◽  
Machine Learning Techniques ◽  
Numeric Model ◽  
Design Creativity ◽  
Large Sets ◽  
Learning Techniques ◽  
Ideation Methods ◽  
High Level ◽  
Design Ideas

The emergence of ideation methods that generate large volumes of early-phase ideas has led to a need for reliable and efficient metrics for measuring the creativity of these ideas. However, existing methods of human judgment-based creativity assessments, as well as numeric model-based creativity assessment approaches suffer from low reliability and prohibitive computational burdens on human raters due to the high level of human input needed to calculate creativity scores. In addition, there is a need for an efficient method of computing the creativity of large sets of design ideas typically generated during the design process. This paper focuses on developing and empirically testing a machine learning approach for computing design creativity of large sets of design ideas to increase the efficiency and reliability of creativity evaluation methods in design research. The results of this study show that machine learning techniques can predict creativity of ideas with relatively high accuracy and sensitivity. These findings show that machine learning has the potential to be used for rating the creativity of ideas generated based on their descriptions.

scholarly journals Evaluation of Classical Machine Learning Techniques towards Urban Sound Recognitionon Embedded Systems

2019 ◽  
Vol 9 (18) ◽  
pp. 3885 ◽  
Author(s):  
Bruno da Silva ◽  
Axel W. Happi ◽  
An Braeken ◽  
Abdellah Touhafi
Keyword(s):  
Machine Learning ◽  
Embedded Systems ◽  
Real Time ◽  
Urban Environments ◽  
Machine Learning Techniques ◽  
Embedded Devices ◽  
Sound Classification ◽  
Learning Techniques ◽  
Urban Monitoring ◽  
Constrained Devices

Automatic urban sound classification is a desirable capability for urban monitoring systems, allowing real-time monitoring of urban environments and recognition of events. Current embedded systems provide enough computational power to perform real-time urban audio recognition. Using such devices for the edge computation when acting as nodes of Wireless Sensor Networks (WSN) drastically alleviates the required bandwidth consumption. In this paper, we evaluate classical Machine Learning (ML) techniques for urban sound classification on embedded devices with respect to accuracy and execution time. This evaluation provides a real estimation of what can be expected when performing urban sound classification on such constrained devices. In addition, a cascade approach is also proposed to combine ML techniques by exploiting embedded characteristics such as pipeline or multi-thread execution present in current embedded devices. The accuracy of this approach is similar to the traditional solutions, but provides in addition more flexibility to prioritize accuracy or timing.

Applications of Artificial Intelligence in the Realm of Business Intelligence

2021 ◽  
pp. 358-386
Author(s):  
Prakhar Mehrotra
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Time Series ◽  
Natural Language Processing ◽  
Language Processing ◽  
Business Intelligence ◽  
Machine Learning Techniques ◽  
Current State ◽  
Learning Techniques ◽  
High Level

The objective of this chapter is to discuss the integration of advancements made in the field of artificial intelligence into the existing business intelligence tools. Specifically, it discusses how the business intelligence tool can integrate time series analysis, supervised and unsupervised machine learning techniques and natural language processing in it and unlock deeper insights, make predictions, and execute strategic business action from within the tool itself. This chapter also provides a high-level overview of current state of the art AI techniques and provides examples in the realm of business intelligence. The eventual goal of this chapter is to leave readers thinking about what the future of business intelligence would look like and how enterprise can benefit by integrating AI in it.

scholarly journals Machine Learning confronted with the operational constraints of detection systems

2019 ◽  
Vol 1 (1) ◽  
pp. 1-7
Author(s):  
Sridarala Ramu ◽  
Daniel Osaku
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Academic Research ◽  
Intrusion Detection Systems ◽  
Machine Learning Techniques ◽  
Detection Model ◽  
Detection Systems ◽  
Learning Techniques ◽  
High Level ◽  
Operational Constraints

Intrusion detection systems, traditionally based on signatures, have not escaped the recent appeal of machine learning techniques. While the results presented in academic research articles are often excellent, security experts still have many reservations about the use of Machine Learning in intrusion detection systems. They generally fear an inadequacy of these techniques to operational constraints, in particular because of a high level of expertise required, or a large number of false positives. In this article, we show that Machine Learning can be compatible with the operational constraints of detection systems. We explain how to build a detection model and present good practices to validate it before it goes into production. The methodology is illustrated by a case study on the detection of malicious PDF files and we offer a free tool, SecuML, to implement it.

scholarly journals Python the game changer in the field of Machine Learning, Data Science and IoT: A Review

2021 ◽  
Vol 9 (8) ◽  
pp. 1827-1837
Author(s):  
Prithwish Parial
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Learning Experience ◽  
Object Oriented Programming ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
High Level ◽  
Learning Data ◽  
Game Changer ◽  
Oriented Programming Language

Abstract: Python is the finest, easily adoptable object-oriented programming language developed by Guido van Rossum, and first released on February 20, 1991 It is a powerful high-level language in the recent software world. In this paper, our discussion will be an introduction to the various Python tools applicable for Machine learning techniques, Data Science and IoT. Then describe the packages that are in demand of Data science and Machine learning communities, for example- Pandas, SciPy, TensorFlow, Theano, Matplotlib, etc. After that, we will move to show the significance of python for building IoT applications. We will share different codes throughout an example. To assistance, the learning experience, execute the following examples contained in this paper interactively using the Jupiter notebooks. Keywords: Machine learning, Real world programming, Data Science, IOT, Tools, Different packages, Languages- Python.

Application of Optimization and Machine Learning for Sentiment Analysis

2021 ◽  
pp. 1-7
Author(s):  
Manitosh Chourasiya ◽  
Prof. Devendra Singh Rathod
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Learning Algorithm ◽  
Sentiment Classification ◽  
Machine Learning Techniques ◽  
Assessment Scores ◽  
Opinion Extraction ◽  
Learning Techniques ◽  
High Level

Sentiment analysis is called detecting emotions extracted from text features and is known as one of the most important parts of opinion extraction. Through this process, we can determine if a script is positive, negative or neutral. In this research, sentiment analysis is performed with textual data. A text feeling analyzer combines natural language processing (NLP) and machine learning techniques to assign weighted assessment scores to entities, subjects, subjects, and categories within a sentence or phrase. In expressing mood, the polarity of text reviews could be graded on a negative to positive scale using a learning algorithm. The current decade has seen significant developments in artificial intelligence, and the machine learning revolution has changed the entire AI industry. After all, machine learning techniques have become an integral part of any model in today's computing world. However, the ensemble to learning techniques is promise a high level of automation with the extraction of generalized rules for text and sentiment classification activities. This thesis aims to design and implement an optimized functionality matrix using to the ensemble learning for the sentiment classification and its applications.

Applications of Artificial Intelligence in the Realm of Business Intelligence

2019 ◽  
pp. 1-38
Author(s):  
Prakhar Mehrotra
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Time Series ◽  
Natural Language Processing ◽  
Language Processing ◽  
Business Intelligence ◽  
Machine Learning Techniques ◽  
Current State ◽  
Learning Techniques ◽  
High Level

The objective of this chapter is to discuss the integration of advancements made in the field of artificial intelligence into the existing business intelligence tools. Specifically, it discusses how the business intelligence tool can integrate time series analysis, supervised and unsupervised machine learning techniques and natural language processing in it and unlock deeper insights, make predictions, and execute strategic business action from within the tool itself. This chapter also provides a high-level overview of current state of the art AI techniques and provides examples in the realm of business intelligence. The eventual goal of this chapter is to leave readers thinking about what the future of business intelligence would look like and how enterprise can benefit by integrating AI in it.

Fundamentals of Machine Learning

2019 ◽  
Author(s):  
Thomas P. Trappenberg
Keyword(s):  
Machine Learning ◽  
Industrial Applications ◽  
Machine Learning Techniques ◽  
Comprehensive Overview ◽  
Bayesian Approaches ◽  
Mathematical Arguments ◽  
Learning Techniques ◽  
High Level ◽  
The Impact ◽  
Probabilistic Context

Machine learning is exploding, both in research and for industrial applications. This book aims to be a brief introduction to this area given the importance of this topic in many disciplines, from sciences to engineering, and even for its broader impact on our society. This book tries to contribute with a style that keeps a balance between brevity of explanations, the rigor of mathematical arguments, and outlining principle ideas. At the same time, this book tries to give some comprehensive overview of a variety of methods to see their relation on specialization within this area. This includes some introduction to Bayesian approaches to modeling as well as deep learning. Writing small programs to apply machine learning techniques is made easy today by the availability of high-level programming systems. This book offers examples in Python with the machine learning libraries sklearn and Keras. The first four chapters concentrate largely on the practical side of applying machine learning techniques. The book then discusses more fundamental concepts and includes their formulation in a probabilistic context. This is followed by chapters on advanced models, that of recurrent neural networks and that of reinforcement learning. The book closes with a brief discussion on the impact of machine learning and AI on our society.

Machine Learning Techniques for Code Smells Detection: A Systematic Mapping Study

2019 ◽  
Vol 29 (02) ◽  
pp. 285-316 ◽  
Author(s):  
Frederico Luiz Caram ◽  
Bruno Rafael De Oliveira Rodrigues ◽  
Amadeu Silveira Campanelli ◽  
Fernando Silva Parreiras
Keyword(s):  
Machine Learning ◽  
Empirical Studies ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Systematic Mapping Study ◽  
Code Smells ◽  
Mapping Study ◽  
Learning Techniques ◽  
Wide Range ◽  
High Level

Code smells or bad smells are an accepted approach to identify design flaws in the source code. Although it has been explored by researchers, the interpretation of programmers is rather subjective. One way to deal with this subjectivity is to use machine learning techniques. This paper provides the reader with an overview of machine learning techniques and code smells found in the literature, aiming at determining which methods and practices are used when applying machine learning for code smells identification and which machine learning techniques have been used for code smells identification. A mapping study was used to identify the techniques used for each smell. We found that the Bloaters was the main kind of smell studied, addressed by 35% of the papers. The most commonly used technique was Genetic Algorithms (GA), used by 22.22% of the papers. Regarding the smells addressed by each technique, there was a high level of redundancy, in a way that the smells are covered by a wide range of algorithms. Nevertheless, Feature Envy stood out, being targeted by 63% of the techniques. When it comes to performance, the best average was provided by Decision Tree, followed by Random Forest, Semi-supervised and Support Vector Machine Classifier techniques. 5 out of the 25 analyzed smells were not handled by any machine learning techniques. Most of them focus on several code smells and in general there is no outperforming technique, except for a few specific smells. We also found a lack of comparable results due to the heterogeneity of the data sources and of the provided results. We recommend the pursuit of further empirical studies to assess the performance of these techniques in a standardized dataset to improve the comparison reliability and replicability.

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.

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