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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+malware_detection_data.csv filter=lfs diff=lfs merge=lfs -text

LabelEncoder.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:52f9e1b85be8056dd7a6ad73f243aa5acde190065661aee00bc8d399c995d708
+size 1177

app.py

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+import streamlit as st
+from joblib import load
+import numpy as np
+
+# App Title
+st.title("Malware Detection")
+
+# Load Model and Encoder
+model = load('dbscan_model.joblib')
+encoder = load("LabelEncoder.joblib")
+
+# Input Fields
+anomaly_score = st.number_input("Anomaly Score", min_value=0, max_value=100, step=1, help="Score indicating the level of anomaly (0-100).")
+anomaly_score = anomaly_score / 100  # Normalize
+suspicious_ip_count = st.number_input("Suspicious IP Count", min_value=0, max_value=9, help="Number of suspicious IPs detected.")
+malicious_payload_indicator = st.selectbox("Malicious Payload Indicator", options=["Yes", "No"])
+malicious_payload_indicator = 1 if malicious_payload_indicator == "Yes" else 0
+reputation_score = st.number_input("Reputation Score", min_value=0, max_value=100, help="Reputation score of the source (0-100).")
+behavioral_score = st.number_input("Behavioral Score", min_value=0, max_value=100, help="Behavioral score based on activity patterns (0-100).")
+
+attack_type = st.selectbox("Attack Type", options=encoder["attack_type"].classes_, help="Type of attack detected.")
+attack_type = encoder["attack_type"].transform([attack_type])[0]
+
+signature_match = st.selectbox("Signature Match", options=["Yes", "No"], help="Does the payload match any known signatures?")
+signature_match = 1 if signature_match == "Yes" else 0
+
+sandbox_result = st.selectbox("Sandbox Result", options=encoder["sandbox_result"].classes_, help="Result from sandbox testing.")
+sandbox_result = encoder["sandbox_result"].transform([sandbox_result])[0]
+
+heuristic_score = st.number_input("Heuristic Score", min_value=0, max_value=100, help="Score based on heuristic analysis (0-100).")
+traffic_pattern = st.selectbox("Traffic Pattern", options=encoder["traffic_pattern"].classes_, help="Detected traffic pattern.")
+traffic_pattern = encoder["traffic_pattern"].transform([traffic_pattern])[0]
+
+# Combine Features
+values = [
+    anomaly_score, suspicious_ip_count, malicious_payload_indicator,
+    reputation_score, behavioral_score, attack_type, signature_match,
+    sandbox_result, heuristic_score, traffic_pattern
+]
+
+# Prediction
+if st.button("Detect Malware"):
+    try:
+        label = model.fit_predict([values])[0]
+        if label == -1:
+            st.success("Malware Detected")
+        else:
+            st.success("No Malware Detected")
+    except Exception as e:
+        st.error(f"Error: {e}")

dbscan_model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2b62f17af9b51351fe0d711750cfb67b85bb497b81aad7bc89145d033b6880b0
+size 2124077

malware_detection_data.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5549cc13d44bf8f6cc42f1282a9cabb0cebd24c45aefab85e81e120692fe724a
+size 20430279

requirements.txt

@@ -0,0 +1,5 @@
+joblib==1.3.2
+numpy==1.26.4
+pandas==2.2
+scikit-learn==1.4.1.post1
+streamlit==1.32.2