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DUMP.py

+import joblib
+import re
+import nltk
+nltk.download('stopwords')
+from nltk.corpus import stopwords
+from nltk.stem.porter import PorterStemmer
+from tkinter import messagebox
+
+# Load the dumped models
+classifier = joblib.load('naive_bayes_model.joblib')
+cv = joblib.load('count_vectorizer.joblib')
+
+# Function to Clean the texts
+def preprocess_text(text):
+    log = re.sub('[^a-zA-Z0-9]', ' ', text)
+    log = log.lower()
+    log = log.split()
+    ps = PorterStemmer()
+    log = [ps.stem(word) for word in log if not word in set(stopwords.words('english'))]
+    log = ' '.join(log)
+    return log
+
+# New log insert
+new_text = input("Enter the traffic info: ")
+preprocessed_text = preprocess_text(new_text)
+
+# Use the loaded CountVectorizer object to transform the preprocessed text into a bag of words representation
+new_text_bow = cv.transform([preprocessed_text]).toarray()
+
+# Use the loaded GaussianNB object to make predictions on the new log data
+predicted_class = classifier.predict(new_text_bow)
+
+# Display a pop-up message based on the predicted class
+if predicted_class:
+    messagebox.showinfo("Anomaly Detected", "Anomaly detected in the traffic!")
+else:
+    messagebox.showinfo("No Anomaly", "No anomaly detected in the traffic.")
+    
+# Non Malicious Packet Info
+# Adjacency Message (Syn)
+# 19717  >  6068 [ACK] Seq=1285 Ack=785 Win=3344 Len=0
+
+# Malicious Packet Info
+# 80  >  51451 [ACK] Seq=214260 Ack=712 Win=16128 Len=1460 [TCP segment of a reassembled PDU]
+# 80  >  49234 [PSH, ACK] Seq=500760 Ack=325 Win=64240 Len=1448 [TCP segment of a reassembled PDU]