Yasaswita

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2025 SpringBoardMentor193s
+Copyright (c) 2025 yasaswitaraja
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
@@ -9,13 +9,4 @@ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 copies of the Software, and to permit persons to whom the Software is
 furnished to do so, subject to the following conditions:
 
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+[Full MIT License Text here or link to https://opensource.org/licenses/MIT]

README.md

@@ -1,2 +1,11 @@
-# SentinelNet
-The goal of this project is to develop an AI-powered Network Intrusion Detection System (NIDS) capable of identifying malicious network trafic and cyber-attacks in real time. By leveraging machine learning techniques, the system will classify trafic as normal or suspicious based on historical data. 
+# SentinelNet Project
+This project is for AI-powered Network Intrusion Detection System. SentinelNet is a project that analyzes network intrusion datasets to help detect cyber attacks. It’s designed to explore, visualize, and categorize network traffic into different types of attacks, giving a clear picture of which attacks are more frequent and how balanced the dataset is.
+
+The project focuses on two widely used datasets:
+
+NSL-KDD – A classic network intrusion detection dataset with labels for attacks like DoS (Denial of Service), Probe, R2L (Remote to Local), U2R (User to Root), and Normal traffic.
+
+CICIDS2017 – A modern network dataset with real traffic captured over an afternoon, including multiple attack types like DDoS, PortScan, and more.
+
+Run the main script:
+'''python scripts/main.py'''

app.py

@@ -0,0 +1,111 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, ConfusionMatrixDisplay
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
+from sklearn.tree import DecisionTreeClassifier
+import matplotlib.pyplot as plt
+
+# -------------------- Sidebar --------------------
+st.sidebar.title("⚡ SentinelNet IDS")
+st.sidebar.subheader("Upload & Configure")
+
+# Upload dataset
+uploaded_file = st.sidebar.file_uploader("📂 Upload CSV Dataset", type=["csv"])
+
+# Select model
+model_choice = st.sidebar.radio(
+    "🤖 Choose ML Model",
+    ["Logistic Regression", "Random Forest", "Decision Tree", "Gradient Boosting"]
+)
+
+# Button to trigger training
+train_button = st.sidebar.button("🚀 Run Intrusion Detection")
+
+# -------------------- Main Page --------------------
+st.title("🔐 Network Intrusion Detection System")
+st.write("Upload a dataset, choose a model from the sidebar, and detect intrusions.")
+
+if uploaded_file is not None:
+    # Load dataset
+    df = pd.read_csv(uploaded_file)
+    st.success(f"✅ Dataset Loaded: {df.shape[0]} rows, {df.shape[1]} columns")
+    st.dataframe(df.head())
+
+    if train_button:
+        try:
+            # -------------------- Data Preprocessing --------------------
+            # Assume last column is target
+            X = df.iloc[:, :-1].copy()
+            y = df.iloc[:, -1].copy()
+
+            # If target is continuous → convert to binary
+            if pd.api.types.is_numeric_dtype(y):
+                # Threshold = median
+                y_binary = (y > y.median()).astype(int)
+                y = y_binary
+                st.info("ℹ️ Target column was continuous → converted to binary classes (0/1).")
+
+            # If target is categorical text → encode
+            elif y.dtype == 'object':
+                le_target = LabelEncoder()
+                y = le_target.fit_transform(y)
+
+            # Encode categorical features
+            for col in X.columns:
+                if X[col].dtype == 'object':
+                    le = LabelEncoder()
+                    X[col] = le.fit_transform(X[col])
+
+            # Train/test split
+            X_train, X_test, y_train, y_test = train_test_split(
+                X, y, test_size=0.2, random_state=42, stratify=y
+            )
+
+            # Scale features
+            scaler = StandardScaler()
+            X_train_scaled = scaler.fit_transform(X_train)
+            X_test_scaled = scaler.transform(X_test)
+
+            # -------------------- Model Selection --------------------
+            if model_choice == "Logistic Regression":
+                model = LogisticRegression(max_iter=1000)
+            elif model_choice == "Random Forest":
+                model = RandomForestClassifier(n_estimators=100, random_state=42)
+            elif model_choice == "Decision Tree":
+                model = DecisionTreeClassifier(random_state=42)
+            elif model_choice == "Gradient Boosting":
+                model = GradientBoostingClassifier(random_state=42)
+
+            # -------------------- Training --------------------
+            model.fit(X_train_scaled, y_train)
+            y_pred = model.predict(X_test_scaled)
+
+            # -------------------- Metrics --------------------
+            acc = accuracy_score(y_test, y_pred)
+            prec = precision_score(y_test, y_pred, average='weighted', zero_division=0)
+            rec = recall_score(y_test, y_pred, average='weighted', zero_division=0)
+            f1 = f1_score(y_test, y_pred, average='weighted', zero_division=0)
+
+            st.subheader("📊 Evaluation Metrics")
+            st.write(f"**Model Used:** {model_choice}")
+            st.write(f"**Accuracy:** {acc:.4f}")
+            st.write(f"**Precision:** {prec:.4f}")
+            st.write(f"**Recall:** {rec:.4f}")
+            st.write(f"**F1 Score:** {f1:.4f}")
+
+            # -------------------- Confusion Matrix --------------------
+            cm = confusion_matrix(y_test, y_pred)
+            fig, ax = plt.subplots()
+            disp = ConfusionMatrixDisplay(confusion_matrix=cm)
+            disp.plot(ax=ax, cmap="Blues", colorbar=False)
+            st.pyplot(fig)
+
+        except Exception as e:
+            st.error(f"❌ Error during training: {e}")
+
+else:
+    st.warning("⚠️ Please upload a dataset to proceed.")