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The Role of Weather in Tennis Matches
Amit-Jain-22/Malware-Detection<|file_sep|>/README.md
# Malware-Detection The repository contains work done in the course CS-523 (Machine Learning) at IIT Bombay ## Dataset The dataset was taken from [kaggle](https://www.kaggle.com/). ## Code `CNN.ipynb` - CNN implementation (using Tensorflow) for malware detection `RNN.ipynb` - RNN implementation (using Keras) for malware detection `train.py` - Contains code for training CNN model (using Tensorflow) ## Results ### CNN The model achieved an accuracy score of ~92% after training for about an hour. ### RNN The model achieved an accuracy score of ~96% after training for about an hour. ## Future Work * Experiment with hyperparameter tuning
* Try other ML/DL techniques like SVMs <|file_sep># coding: utf-8
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.layers import Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.utils import plot_model # convert integer sequence into a one-hot vector sequence
def one_hot_encode(x):
return [np.eye(256)[x[i]] for i in range(len(x))] # Convert sequence back from one-hot vectors into integers again
def decode_one_hot(y):
return [np.argmax(y[i]) for i in range(len(y))] def load_data():
print('Loading data...') # Load training data
with open('data/train-labels.csv', 'r') as f:
labels = f.read().split('n')[1:-1]
labels = np.array([int(i) for i in labels])
print(labels.shape)
print(labels[0:10]) with open('data/train', 'rb') as f:
x_train = f.read()
x_train = np.frombuffer(x_train[: len(labels)], dtype=np.uint8)
x_train = x_train.reshape((len(labels), -1))
print(x_train.shape) # One-hot encode input sequences
x_train = np.array([one_hot_encode(x_train[i]) for i in range(len(x_train))])
print(x_train.shape) # Split sequences into individual samples
samples = []
lengths = []
curr_len = len(x_train[0])
curr_sample = [] # Create samples from sequences
for i in range(len(x_train)):
curr_sample.extend(x_train[i])
if len(curr_sample) >= curr_len:
samples.append(curr_sample[:curr_len])
curr_sample = curr_sample[curr_len:]
lengths.append(curr_len) x_train = np.array(samples)
lengths = np.array(lengths)
print(x_train.shape) # Load testing data
with open('data/test-labels.csv', 'r') as f:
test_labels = f.read().split('n')[1:-1]
test_labels = np.array([int(i) for i in test_labels])
print(test_labels.shape)
print(test_labels[0:10]) with open('data/test', 'rb') as f:
x_test = f.read()
x_test = np.frombuffer(x_test[: len(test_labels)], dtype=np.uint8)
x_test = x_test.reshape((len(test_labels), -1))
print(x_test.shape) # One-hot encode input sequences
x_test = np.array([one_hot_encode(x_test[i]) for i in range(len(x_test))])
print(x_test.shape) # Split sequences into individual samples
samples = []
lengths = []
curr_len = len(x_test[0])
curr_sample = [] # Create samples from sequences
for i in range(len(x_test)):
curr_sample.extend(x_test[i])
if len(curr_sample) >= curr_len:
samples.append(curr_sample[:curr_len])
curr_sample = curr_sample[curr_len:]
lengths.append(curr_len) x_test = np.array(samples)
lengths = np.array(lengths)
print(x_test.shape) return (x_train, labels), (x_test, test_labels) # Training model using LSTM layers using Keras Sequential API if __name__ == '__main__':
# load data
(trainX, trainY), (testX,testY) = load_data() print("Training data shape:", trainX.shape)
print("Testing data shape:", testX.shape) model=Sequential()
model.add(LSTM(32,input_shape=(trainX.shape[1],trainX.shape[2]),return_sequences=True))
model.add(Dropout(0.5))
model.add(LSTM(16))
model.add(Dropout(0.5))
model.add(Dense(1)) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(trainX,
trainY,
epochs=20,
batch_size=64,
validation_data=(testX,testY),
verbose=1) print(model.summary()) plot_model(model,to_file='model.png') <|repo_name|>Amit-Jain-22/Malware-Detection<|file_sep USA Cybersecurity Challenge Final Project - Malware Detection using Machine Learning Team:
Amit Jain (2015CSB1039)
Gaurav Jangid (2015CSB1006)
Gurkirat Singh (2015CSB1007) Project Description:
Malware is software that is designed to damage computers by destroying files or collecting personal information without consent. Malware includes computer viruses,
worms and Trojan horses among other malicious programs. Detection & Prevention:
Malware detection methods can be divided into two categories: signature-based detection methods & anomaly-based detection methods.
Signature-based detection methods look for known malware signatures which are stored in a database.
Anomaly-based detection methods look at user behaviour patterns & deviations from those patterns. In this project we have implemented an anomaly-based detection system that detects malware using machine learning algorithms. Data:
The dataset used here has been taken from Kaggle.com
It contains two files:
1) Train file which contains training data
a) train file: Contains raw bytes representing executable files
b) train-labels file: Contains label denoting whether corresponding executable is malicious or benign 2) Test file which contains testing data
a) test file: Contains raw bytes representing executable files
b) test-labels file: Contains label denoting whether corresponding executable is malicious or benign Pre-processing:
For pre-processing we first convert raw byte values into one-hot encoded vectors.
Then we split these vectors into smaller segments so that they can be fed into our machine learning algorithms. Training:
We use two different machine learning algorithms:
1) Recurrent Neural Network (RNN)
a) This algorithm consists of Long Short Term Memory (LSTM) layers
b) Training was done using Keras library 2) Convolutional Neural Network (CNN)
a) This algorithm consists of convolutional layers followed by max-pooling layers followed by fully connected layers
b) Training was done using Tensorflow library Testing:
Once training was complete we tested both models on our test dataset. Results:
RNN Model: Accuracy ~96%
CNN Model: Accuracy ~92% Future Work:
We would like to experiment with hyperparameter tuning & try other machine learning algorithms such as Support Vector Machines (SVM).<|repo_name|>rosstessier/roslint<|file_sep|>/tests/test_rule.py
#!/usr/bin/env python3 import unittest from roslint.rule import Rule class RuleTest(unittest.TestCase):
def test_is_error(self):
rule = Rule('test_rule', 'This is just a test rule')
self.assertTrue(rule.is_error())
rule.severity = Rule.INFO
self.assertFalse(rule.is_error())
rule.severity = Rule.FATAL
self.assertTrue(rule.is_error()) def test_is_info(self):
rule = Rule('test_rule', 'This is just a test rule')
self.assertFalse(rule.is_info())
rule.severity = Rule.INFO
self.assertTrue(rule.is_info())
rule.severity = Rule.FATAL
self.assertFalse(rule.is_info()) def test_is_warning(self):
rule = Rule('test_rule', 'This is just a test rule')
self.assertTrue(rule.is_warning())
rule.severity = Rule.INFO
self.assertFalse(rule.is_warning())
rule.severity = Rule.FATAL
self.assertTrue(rule.is_warning()) def test_is_fatal(self):
rule = Rule('test_rule', 'This is just a test rule')
self.assertFalse(rule.is_fatal())
rule.severity = Rule.INFO
self.assertFalse(rule.is_fatal())
rule.severity = Rule.FATAL
self.assertTrue(rule.is_fatal()) if __name__ == '__main__':
unittest.main() <|file_sep |===============================================================================
RosLint Project Documentation {#mainpage}
=============================================================================== @page intro Introduction RosLint is tool intended to lint ref ROS "ROS" packages. RosLint will perform static analysis on ref ROS "ROS" packages by running linters against source code files,
and analyzing ref CMake "CMake" configuration files. This documentation will detail how RosLint works internally. @page usage Usage RosLint can be run directly from the command line: @verbatim bash
$ roslint [options] package_name...
@endverbatim RosLint can also be run via CMake: @verbatim cmake -DENABLE_ROSLINT=ON ...
@endverbatim @page config Configuration File Format RosLint uses YAML format configuration files. To specify rules: @code yaml
rules:
- package_name_1:
- rule_1_1_id: severity_1_1
- rule_1_2_id: severity_1_2
- package_name_2:
- rule_2_1_id: severity_2_1
...
@endcode To specify filters: @code yaml
filters:
- package_name_1:
- filter_1_1_id: value_1_1
- package_name_2:
- filter_2_1_id: value_2_1
...
@endcode @page rules Rules RosLint uses rules to determine if there are issues within packages. There are three types of rules: * @ref file_rules "File Rules" determine if there are issues within files.
* @ref cmake_rules "CMake Rules" determine if there are issues within CMake configuration files.
* @ref ros_rules "ROS Rules" determine if there are issues within ROS packages. @section file_rules File Rules File Rules analyze source code files. @subsection clang Clang Static Analyzer Rules The following rules are provided by clang-tidy [@clang_tidy]: * `clang-analyzer-*` See clang-tidy [@clang_tidy] documentation regarding these rules. @section cmake_rules CMake Rules CMake Rules analyze CMake configuration files. @subsection cmake_all CMake All Rules These rules check all CMakeLists.txt files within a ROS package directory. @subsubsection cmake_all_importance Import Statements Should Be At The Top Of A File {#rule_cmake_all_importance} All `import()` statements should appear at the top of CMakeLists.txt files. @subsubsection cmake_all_redundant_includes Redundant Includes Should Be Removed {#rule_cmake_all_redundant_includes} Redundant include statements should be removed from CMakeLists.txt files. For example: @code cmake_lists.txt example before fixup:
find_package(catkin REQUIRED COMPONENTS roscpp)
find_package(catkin REQUIRED COMPONENTS roscpp)
endcode After running RosLint: @code cmake_lists.txt example after fixup:
find_package(catkin REQUIRED COMPONENTS roscpp)
endcode @subsection cmake_package Specific CMake Package Rules These rules check specific CMake packages used by ROS packages. @subsubsection cmake_package_catkin Catkin Package Rules {#rule_cmake_package_catkin} These rules check catkin-specific CMake configuration files. @subsubsection cmake_package_ros_comm RosComm Package Rules {#rule_cmake_package_ros_comm} These rules check ros_comm-specific CMake configuration files. @subsection cmake_custom Custom CMake Rules {#rule_cmake_custom} Custom CMake rules can be added through custom plugins. @section ros_rules ROS Rules ROS Rules analyze ROS packages. @subsection ros_message Messages Should Be In Correct Order {#rule_ros_message_order} Message definition fields should be sorted alphabetically by type name,
with fields having equal type names sorted alphabetically by name. For example: @code msg/Example.msg before fixup:
float32 b;
float32 c;
float32 a;
endcode After running RosLint: @code msg/Example.msg after fixup:
float32 a;
float32 b;
float32 c;
endcode See @ref ros_maintainer_msgs_msgs "ros_maintainer_msgs_msgs" repository [@ros_maintainer_msgs_msgs] for more details. @page plugins Plugins {#plugins_page} RosLint uses plugins to apply rules. There are four types of plugins: * @ref cli