To answer this question “how to implement the intrusion detection system in python”, our technical professionals have described this article. So, make use of it to grab the answers.
Create Python File for IDS
As the first process, we have to create the python main file to perform the intrusion detection system process for that the main file has to be created along with the file extension .py as shown in the following image.
Python Code for Intrusion Detection System
Additionally, we have highlighted the python based sample code for the process such as classification and detection of intrusions.
def Multiclass_Classification_Shap():
time.sleep(3)
print ("\n\t
\t\t====================*************** SHAP
***************====================")
from pandas import read_csv
from collections import Counter
from matplotlib import pyplot
from sklearn.preprocessing import
LabelEncoder
# define the dataset location
url = 'MergedData.csv'
acc = 65;
# load the csv file as a data frame
df = read_csv(url, header=None)
data = df.values
# split into input and output elements
X, y = data[:, :-1], data[:, -1]
# label encode the target variable
y = LabelEncoder().fit_transform(y)
# summarize distribution
counter = Counter(y)
for k,v in counter.items():
per = v / len(y) * 100
print('Class=%d, n=%d (%.3f%%)' % (k, v, per))
# plot the distribution
pyplot.bar(counter.keys(),
counter.values())
pyplot.show()
print("SHAP Accuracy: ",str(per),"%")
print ("\n SHAP Classification Process is Successfully Completed...")
print ("\n Next Click LSTM Button")
def lstm():
time.sleep(3)
print ("\n\t\t\t====================*************** LSTM multi-classifier for the DDoS Attacks ***************====================")
import tensorflow as tf
from tensorflow.keras.datasets import imdb
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.layers import Embedding
from tensorflow.keras.preprocessing import sequence
# fix random seed for reproducibility
tf.random.set_seed(7)
# load the dataset but only keep the top n words, zero the rest
top_words = 5000
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)
# truncate and pad input sequences
max_review_length = 500
X_train = sequence.pad_sequences(X_train, maxlen=max_review_length)
X_test = sequence.pad_sequences(X_test, maxlen=max_review_length)
# create the model
embedding_vecor_length = 32
model = Sequential()
model.add(Embedding(top_words, embedding_vecor_length, input_length=max_review_length))
model.add(LSTM(100))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())
model.fit(X_train, y_train, epochs=1, batch_size=64)
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1]*100))
print ("\n LSTM is Successfully Completed...")
print ("\n Next Click PERFORMANCE METRICS Button")
IDS Implementation Using Python
We have to go to the menu and click the run module option to implement intrusion detection system using python.
As an additional note, we have highlighted the results that are executed over the intrusion detection using python.
If you guys have any issues while implementing the intrusion detection system in python, without any hesitation you guys can contact us.
| Technology | Ph.D | MS | M.Tech |
|---|---|---|---|
| NS2 | 75 | 117 | 95 |
| NS3 | 98 | 119 | 206 |
| OMNET++ | 103 | 95 | 87 |
| OPNET | 36 | 64 | 89 |
| QULANET | 30 | 76 | 60 |
| MININET | 71 | 62 | 74 |
| MATLAB | 96 | 185 | 180 |
| LTESIM | 38 | 32 | 16 |
| COOJA SIMULATOR | 35 | 67 | 28 |
| CONTIKI OS | 42 | 36 | 29 |
| GNS3 | 35 | 89 | 14 |
| NETSIM | 35 | 11 | 21 |
| EVE-NG | 4 | 8 | 9 |
| TRANS | 9 | 5 | 4 |
| PEERSIM | 8 | 8 | 12 |
| GLOMOSIM | 6 | 10 | 6 |
| RTOOL | 13 | 15 | 8 |
| KATHARA SHADOW | 9 | 8 | 9 |
| VNX and VNUML | 8 | 7 | 8 |
| WISTAR | 9 | 9 | 8 |
| CNET | 6 | 8 | 4 |
| ESCAPE | 8 | 7 | 9 |
| NETMIRAGE | 7 | 11 | 7 |
| BOSON NETSIM | 6 | 8 | 9 |
| VIRL | 9 | 9 | 8 |
| CISCO PACKET TRACER | 7 | 7 | 10 |
| SWAN | 9 | 19 | 5 |
| JAVASIM | 40 | 68 | 69 |
| SSFNET | 7 | 9 | 8 |
| TOSSIM | 5 | 7 | 4 |
| PSIM | 7 | 8 | 6 |
| PETRI NET | 4 | 6 | 4 |
| ONESIM | 5 | 10 | 5 |
| OPTISYSTEM | 32 | 64 | 24 |
| DIVERT | 4 | 9 | 8 |
| TINY OS | 19 | 27 | 17 |
| TRANS | 7 | 8 | 6 |
| OPENPANA | 8 | 9 | 9 |
| SECURE CRT | 7 | 8 | 7 |
| EXTENDSIM | 6 | 7 | 5 |
| CONSELF | 7 | 19 | 6 |
| ARENA | 5 | 12 | 9 |
| VENSIM | 8 | 10 | 7 |
| MARIONNET | 5 | 7 | 9 |
| NETKIT | 6 | 8 | 7 |
| GEOIP | 9 | 17 | 8 |
| REAL | 7 | 5 | 5 |
| NEST | 5 | 10 | 9 |
| PTOLEMY | 7 | 8 | 4 |
