Automatic Reverse Engineering of CAN Bus Data Using Machine Learning Techniques

Insofar as planning mediates between the order of what is and the question of what might be, it is not only a matter of philosophy but also one of engineering. Particularly at a time when routines of financial speculation and pattern recognition have colonized the making of futures, planning has become a process of creating architectural opportunities from scattered corpuses of extracted data. Mindful of the importance of machine learning in such processes, this article critically grapples with the proposition that techniques of reverse engineering offer a means of cracking these future making routines and turning them toward projects of social and political ameli oration. I argue that technical practices of reverse engineering need to articulate to radical political projects and modes of organization. Drawing on computer science studies of adversarial machine learning, I also consider the question of whether reverse engineering of machine learning techniques is technically possible. Ultimately, the article contrasts political claims for reverse engineering with what I call the reverse of engineering, or a program that entails the subordination of data to futures rather than planning processes that work from the merely evidential and measurable.

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Reverse engineering smart card malware using side channel analysis with machine learning techniques

2016 IEEE International Conference on Big Data (Big Data) ◽

10.1109/bigdata.2016.7841039 ◽

2016 ◽

Cited By ~ 2

Author(s):

Hippolyte Djonon Tsague ◽

Bheki Twala

Keyword(s):

Machine Learning ◽

Reverse Engineering ◽

Smart Card ◽

Machine Learning Techniques ◽

Side Channel ◽

Side Channel Analysis ◽

Learning Techniques ◽

Channel Analysis

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Machine Learning Techniques to address classification issues in Reverse Engineering

Lecture Notes in Mechanical Engineering - Advances on Mechanics, Design Engineering and Manufacturing ◽

10.1007/978-3-319-45781-9_83 ◽

2016 ◽

pp. 829-839

Author(s):

Jonathan Dekhtiar ◽

Alexandre Durupt ◽

Dimitris Kiritsis ◽

Matthieu Bricogne ◽

Harvey Rowson ◽

...

Keyword(s):

Machine Learning ◽

Reverse Engineering ◽

Machine Learning Techniques ◽

Learning Techniques

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Using machine learning techniques to reduce data annotation time

PsycEXTRA Dataset ◽

10.1037/e577762012-020 ◽

2006 ◽

Author(s):

Christopher Schreiner ◽

Kari Torkkola ◽

Mike Gardner ◽

Keshu Zhang

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Data Annotation ◽

Learning Techniques

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Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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Blind Spoofing Detection for Multi-Antenna Snapshot Receivers using Machine-Learning Techniques

Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020) ◽

10.33012/2020.17564 ◽

2020 ◽

Author(s):

J. Rossouw van der Merwe ◽

Ana Nikolikj ◽

Sebastian Kram ◽

Ivana Lukcin ◽

Gorjan Nadzinski ◽

...

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Spoofing Detection

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389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽

10.2337/db20-389-p ◽

2020 ◽

Vol 69 (Supplement 1) ◽

pp. 389-P

Author(s):

SATORU KODAMA ◽

MAYUKO H. YAMADA ◽

YUTA YAGUCHI ◽

MASARU KITAZAWA ◽

MASANORI KANEKO ◽

...

Keyword(s):

Machine Learning ◽

Meta Analysis ◽

Machine Learning Techniques ◽

Learning Techniques

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A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0177 ◽

2017 ◽

Vol 7 (7) ◽

pp. 172

Author(s):

Anantvir Singh Romana

Keyword(s):

Machine Learning ◽

Subsequent Treatment ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Disease Prediction ◽

Classification Problems ◽

Learning Techniques ◽

Neural Network Classifiers ◽

Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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