Freelancer logo How It Works Browse Jobs Log In Sign Up Post a Project Profile cover photoundefined
You're now following .
Error following user.
This user does not allow users to follow them.
You are already following this user.
Your membership plan only allows 0 follows. Upgrade here.
Successfully unfollowed user.
Error unfollowing user.
You have successfully recommended
Error recommending user.
Email successfully verified.
User Avatar
$40 USD / hour
Flag of UZBEKISTAN
tashkent, uzbekistan
$40 USD / hour
It's currently 3:27 AM here
Joined November 27, 2021
1 Recommendation

Samidullo A.

@Samidullo

annual-level-three.svg
5.0 (7 reviews)
3.5
3.5
$40 USD / hour
Flag of UZBEKISTAN
tashkent, uzbekistan
$40 USD / hour
100%
Jobs Completed
76%
On Budget
85%
On Time
N/A
Repeat Hire Rate

Data scientist/Django Expert/Cryptography Expert

Welcome to my Freelancer Profile! I am a Machine learning Engineer whose focus is in Multi / Logistic/Linear Regression. I enjoy developing new and novel models as well as creating infrastructure for automation and ease-of-use. Data of all types interest me. I have experience developing Machine learning applications for a variety of hardware architectures, including mobile devices.. Working with me you will get best practice in source code management, version control, and security access to your code base. My technology stack is listed in long-form below. Core: - Linux/Ubuntu - Python 3 - Markdown Data Manipulation: - Numpy - Pandas - OpenCV - PIL/Pillow Machine Learning - Scikit-learn - NLTK -SciPy Deep Learning: - Tensorflow - Keras - PyTorch Data mining : -Weka Data Visualization: -Tableau - Matplotlib - Seaborn - Bokeh - Plotly Thanks for your consideration!
Freelancer Python Developers Uzbekistan

Contact Samidullo A. about your job

Log in to discuss any details over chat.

Portfolio Items

A5/1 is the strong version of the encryption algorithm used by about 130 million GSM customers in Europe to protect the over-the-air privacy of their cellular voice and data communication. The best published attacks against it require between 240 and 245 steps. This level of security makes it vulnerable to hardware-based attacks by large organizations, but not to software-based attacks on multiple targets by hackers.

In this paper we describe new attacks on A5/1, which are based on subtle flaws in the tap structure of the registers, their noninvertible clocking mechanism, and their frequent resets. After a 248 parallelizable data preparation stage (which has to be carried out only once), the actual attacks can be carried out in real time on a single PC.
A5/1 Cryptography algorithm
A5/1 is the strong version of the encryption algorithm used by about 130 million GSM customers in Europe to protect the over-the-air privacy of their cellular voice and data communication. The best published attacks against it require between 240 and 245 steps. This level of security makes it vulnerable to hardware-based attacks by large organizations, but not to software-based attacks on multiple targets by hackers.

In this paper we describe new attacks on A5/1, which are based on subtle flaws in the tap structure of the registers, their noninvertible clocking mechanism, and their frequent resets. After a 248 parallelizable data preparation stage (which has to be carried out only once), the actual attacks can be carried out in real time on a single PC.
A5/1 Cryptography algorithm
Data encryption standard (DES) has been found vulnerable against very powerful attacks and therefore, the popularity of DES has been found slightly on the decline.
DES is a block cipher and encrypts data in blocks of size of 64 bits each, which means 64 bits of plain text goes as the input to DES, which produces 64 bits of ciphertext. The same algorithm and key are used for encryption and decryption, with minor differences. The key length is 56 bits. The basic idea is shown in the figure.
Data encryption standard (DES)
Data encryption standard (DES) has been found vulnerable against very powerful attacks and therefore, the popularity of DES has been found slightly on the decline.
DES is a block cipher and encrypts data in blocks of size of 64 bits each, which means 64 bits of plain text goes as the input to DES, which produces 64 bits of ciphertext. The same algorithm and key are used for encryption and decryption, with minor differences. The key length is 56 bits. The basic idea is shown in the figure.
Data encryption standard (DES)
A US shop is trying to forecast future traffic and sales based on historical data gathered over the years. The initial data provided is split into two different datasets, the traffic one contains data from roughly 2015 to 2018 while the sales csv file presents more data points starting from 2013 and also finishing in 2018.
Successfully predicting future income and affluence can be extremely important for companies in order to come up with effective strategies encompassing internal logistics and marketing initiatives.
Given the nature of the two datasets, it seemed like time series analysis was the most appropriate choice for this type of problem.
An external dataset containing federal US holidays was also merged with the two initial ones in order to provide some additional insights. The external dataset can be found on Kaggle. It simply states all the federal holidays from 1966 to 2020. This dataset was combined with the other two only for EDA purposes but not for modelling ones.
Traffic and Sales Analysis for US shop
A US shop is trying to forecast future traffic and sales based on historical data gathered over the years. The initial data provided is split into two different datasets, the traffic one contains data from roughly 2015 to 2018 while the sales csv file presents more data points starting from 2013 and also finishing in 2018.
Successfully predicting future income and affluence can be extremely important for companies in order to come up with effective strategies encompassing internal logistics and marketing initiatives.
Given the nature of the two datasets, it seemed like time series analysis was the most appropriate choice for this type of problem.
An external dataset containing federal US holidays was also merged with the two initial ones in order to provide some additional insights. The external dataset can be found on Kaggle. It simply states all the federal holidays from 1966 to 2020. This dataset was combined with the other two only for EDA purposes but not for modelling ones.
Traffic and Sales Analysis for US shop
A US shop is trying to forecast future traffic and sales based on historical data gathered over the years. The initial data provided is split into two different datasets, the traffic one contains data from roughly 2015 to 2018 while the sales csv file presents more data points starting from 2013 and also finishing in 2018.
Successfully predicting future income and affluence can be extremely important for companies in order to come up with effective strategies encompassing internal logistics and marketing initiatives.
Given the nature of the two datasets, it seemed like time series analysis was the most appropriate choice for this type of problem.
An external dataset containing federal US holidays was also merged with the two initial ones in order to provide some additional insights. The external dataset can be found on Kaggle. It simply states all the federal holidays from 1966 to 2020. This dataset was combined with the other two only for EDA purposes but not for modelling ones.
Traffic and Sales Analysis for US shop
A US shop is trying to forecast future traffic and sales based on historical data gathered over the years. The initial data provided is split into two different datasets, the traffic one contains data from roughly 2015 to 2018 while the sales csv file presents more data points starting from 2013 and also finishing in 2018.
Successfully predicting future income and affluence can be extremely important for companies in order to come up with effective strategies encompassing internal logistics and marketing initiatives.
Given the nature of the two datasets, it seemed like time series analysis was the most appropriate choice for this type of problem.
An external dataset containing federal US holidays was also merged with the two initial ones in order to provide some additional insights. The external dataset can be found on Kaggle. It simply states all the federal holidays from 1966 to 2020. This dataset was combined with the other two only for EDA purposes but not for modelling ones.
Traffic and Sales Analysis for US shop
Task is to write a program in any programming language supported on our Linux CSE machines
that will decrypt as much of the message as possible using the fact that Alice and Bob reused their
one-time pad for all of the six messages that Eve stored. This will simply involve reading in the file
appropriately (i.e., using two hexadecimal values for each encrypted character) and then applying
some techniques to decrypt the ciphertexts. You may assume that you have six ciphertexts, each with
60 characters expressed using their hexadecimal ASCII values (i.e., 2 hexadecimal values or 8 bits).
The one-time pad used to create each ciphertext is exactly the length of the plaintext message (in
bytes, that is, 120 bytes). Also, the original plaintexts contain only upper- and lowercase alphabetic
characters with spaces (i.e., no special characters or punctuation).
Decryption One-Time-Pad encrypted code without Passwork
Task is to write a program in any programming language supported on our Linux CSE machines
that will decrypt as much of the message as possible using the fact that Alice and Bob reused their
one-time pad for all of the six messages that Eve stored. This will simply involve reading in the file
appropriately (i.e., using two hexadecimal values for each encrypted character) and then applying
some techniques to decrypt the ciphertexts. You may assume that you have six ciphertexts, each with
60 characters expressed using their hexadecimal ASCII values (i.e., 2 hexadecimal values or 8 bits).
The one-time pad used to create each ciphertext is exactly the length of the plaintext message (in
bytes, that is, 120 bytes). Also, the original plaintexts contain only upper- and lowercase alphabetic
characters with spaces (i.e., no special characters or punctuation).
Decryption One-Time-Pad encrypted code without Passwork
Task is to write a program in any programming language supported on our Linux CSE machines
that will decrypt as much of the message as possible using the fact that Alice and Bob reused their
one-time pad for all of the six messages that Eve stored. This will simply involve reading in the file
appropriately (i.e., using two hexadecimal values for each encrypted character) and then applying
some techniques to decrypt the ciphertexts. You may assume that you have six ciphertexts, each with
60 characters expressed using their hexadecimal ASCII values (i.e., 2 hexadecimal values or 8 bits).
The one-time pad used to create each ciphertext is exactly the length of the plaintext message (in
bytes, that is, 120 bytes). Also, the original plaintexts contain only upper- and lowercase alphabetic
characters with spaces (i.e., no special characters or punctuation).
Decryption One-Time-Pad encrypted code without Passwork
Working with ML models : J48 Tree, Naive Bayes Simple, RBF Network, IB1
Using Weka knowledge flow
K-Means is implicitly based on pairwise Euclidean distances between data points, because the sum of squared deviations from centroid is equal to the sum of pairwise squared Euclidean distances divided by the number of points. The term "centroid" is itself from Euclidean geometry.
Unsupervised Kmeans algorithm used to clusted Iris data
K-Means is implicitly based on pairwise Euclidean distances between data points, because the sum of squared deviations from centroid is equal to the sum of pairwise squared Euclidean distances divided by the number of points. The term "centroid" is itself from Euclidean geometry.
Unsupervised Kmeans algorithm used to clusted Iris data
K-Means is implicitly based on pairwise Euclidean distances between data points, because the sum of squared deviations from centroid is equal to the sum of pairwise squared Euclidean distances divided by the number of points. The term "centroid" is itself from Euclidean geometry.
Unsupervised Kmeans algorithm used to clusted Iris data

Reviews

Changes saved
Showing 1 - 5 out of 7 reviews
Filter reviews by:
5.0
₹8,750.00 INR
Good Work. Thanks! Have a nice day.
Python Software Architecture
V
Flag of Simon A. @VIPCorp
2 months ago
5.0
[SEALED]
Nice freelancer!
User Avatar
Flag of Aliaksandr H. @ahryshkevich
3 months ago
5.0
$45.00 USD
Task was completed in minimum time and with accuracy. Would definitely recommend.
Data Science
User Avatar
Flag of Anish P. @year2021
3 months ago
4.8
₹2,000.00 INR
Good to work with Sam
Python Machine Learning (ML) Data Science Data Analytics Data Analysis
C
Flag of Abhishek M. @C0tech
3 months ago
5.0
₹750.00 INR
Professional in his work. Would highly recommend
Python Machine Learning (ML)
D
Flag of Dany Pulickathadathil B. @DanyBaby
4 months ago

Qualifications

AI engineering

IBM
2021
About this Course This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.

Contact Samidullo A. about your job

Log in to discuss any details over chat.

Verifications

Preferred Freelancer
Identity Verified
Payment Verified
Phone Verified
Email Verified
Facebook Connected

Certifications

preferredfreelancer-1.png Preferred Freelancer Program SLA 1 92%

Top Skills

Python 5 Data Mining 4 Data Science 4 Machine Learning (ML) 3 Computer Security 2

Browse Similar Freelancers

Python Developers in Uzbekistan
Python Developers
Data Mining Experts
Data Science Experts

Browse Similar Showcases

Python
Data Mining
Data Science
Machine Learning (ML)
Previous User
Next User
Invite sent successfully!
Registered Users Total Jobs Posted
Freelancer ® is a registered Trademark of Freelancer Technology Pty Limited (ACN 142 189 759)
Copyright © 2022 Freelancer Technology Pty Limited (ACN 142 189 759)
There is no internet connection
Loading preview