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Final Digital

Created by

Ali
Ali
Bahrain

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Final DigitalOnline version

Final Digital

by Ali
1

import as tf
import time

# Create large matrices for comparison
matrix1 = tf . random . normal ( [ 10000 , 10000 ] ) # 10000x10000 matrix
matrix2 = tf . random . normal ( [ 10000 , 10000 ] ) # 10000x10000 matrix

# Perform matrix multiplication on GPU
start_time = time . time ( )
with tf . device ( ' / GPU : 0' ) :
result_gpu = tf . ( matrix1 , matrix2 )
gpu_time = time . time ( ) - start_time
print ( " Time taken on GPU : " , , " seconds " )

# Perform matrix multiplication on CPU
start_time = time . time ( )
with tf . device ( ' / CPU : 0' ) :
result_cpu = tf . ( matrix1 , matrix2 )
cpu_time = time . time ( ) - start_time
print ( " Time taken on CPU : " , cpu_time , " seconds " )

# Compare performance
print ( " GPU is " , cpu_time / gpu_time , " times faster than CPU ! " )

2

import numpy as

# Packet sizes in bytes captured
packet_sizes = np . ( [ 1500 , 64 , 120 , 1500 , 80 , 1500 , 64 , 1000 , 1500 , 64 ] )


average_packet_size = np . ( packet_sizes )
print ( f " Average packet size : { average_packet_size } bytes " )

# Find the maximum packet size
max_packet_size = np . ( packet_sizes )
print ( f " Maximum packet size : { max_packet_size } bytes " )

# Count the number of large packets
large_packet_threshold =
num_large_packets = np . ( > )
print ( f " Number of large packets : { num_large_packets } " )

# Calculate the percentage of large packets
percentage_large_packets = ( num_large_packets / ( packet_sizes ) ) *
print ( f " Percentage of large packets : { percentage_large_packets : . 2f } % " )

# Example of filtering : Get sizes of small packets ( e . g . , less than 100 bytes )
small_packets = packet_sizes [ packet_sizes < 100 ]
print ( f " Sizes of small packets : { small_packets } " )

# Example of element - wise operation : Add a constant overhead to each packet size
overhead = 20 # Example overhead in bytes
packet_sizes_with_overhead = packet_sizes + overhead
print ( f " Packet sizes with overhead : { packet_sizes_with_overhead } " )

# Detecting anomalies
# Calculate the standard deviation of packet sizes
std_dev = np . ( packet_sizes )

# Define a threshold for anomaly detection
anomaly_threshold = 2 * std_dev

# Find packets with sizes that deviate significantly from the mean
anomalous_packets = packet_sizes [ np . abs ( packet_sizes - average_packet_size ) > anomaly_threshold ]
print ( f " Potentially anomalous packet sizes : { anomalous_packets } " )

3

import as

df = . ( 'dataset1 . csv' )

print ( " First 5 rows : \ n " , df . ( ) )

print ( " Summary Statistics : \ n " , df . ( ) )

print ( " Features : \ n " , df . ( ) )

4

from sklearn . import train_test_split
from sklearn . import RandomForestClassifier from sklearn . import accuracy_score

X = [ [ 0 , 0 ] , [ 1 , 1 ] , [ 2 , 2 ] , [ 3 , 3 ] ]
y = [ 0 , 0 , 1 , 1 ] # 0 : benign , 1 : malicious

X_train , X_test , y_train , y_test = train_test_split ( X , y , test_size = 0 . 25 , random_state = 42 )

model = RandomForestClassifier ( )
model . fit ( X_train , y_train )

# Predict and evaluate
y_pred = model . predict ( X_test )
print ( " Accuracy : " , accuracy_score ( y_test , y_pred ) )

 
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