編輯距離可用於詐欺偵測系統，將使用者輸入的資料（例如姓名、地址或電子郵件）與現有資料進行比較，以識別相似但可能具有詐騙的條目。
這是將此功能整合到 Django 專案中的逐步指南。
詐欺偵測系統可以比較：
使用 Django 的訊號在註冊或更新時檢查新使用者資料。
整合函式庫來計算 Levenshtein 距離或使用以下 Python 函數：
from django.db.models import Q
from .models import User # Assume User is your user model
def levenshtein_distance(a, b):
n, m = len(a), len(b)
if n > m:
a, b = b, a
n, m = m, n
current_row = range(n + 1) # Keep current and previous row
for i in range(1, m + 1):
previous_row, current_row = current_row, [i] + [0] * n
for j in range(1, n + 1):
add, delete, change = previous_row[j] + 1, current_row[j - 1] + 1, previous_row[j - 1]
if a[j - 1] != b[i - 1]:
change += 1
current_row[j] = min(add, delete, change)
return current_row[n]
在您的訊號或中介軟體中，將輸入的資料與資料庫中的資料進行比較，以查找相似的條目。
from django.db.models import Q
from .models import User # Assume User is your user model
def detect_similar_entries(email, threshold=2):
users = User.objects.filter(~Q(email=email)) # Exclure l'utilisateur actuel
similar_users = []
for user in users:
distance = levenshtein_distance(email, user.email)
if distance <= threshold:
similar_users.append((user, distance))
return similar_users
在使用者註冊或更新後使用 post_save 訊號執行此檢查：
from django.db.models.signals import post_save
from django.dispatch import receiver
from .models import User
from .utils import detect_similar_entries # Import your function
@receiver(post_save, sender=User)
def check_for_fraud(sender, instance, **kwargs):
similar_users = detect_similar_entries(instance.email)
if similar_users:
print(f"Potential fraud detected for {instance.email}:")
for user, distance in similar_users:
print(f" - Similar email: {user.email}, Distance: {distance}")
要追蹤可疑的詐騙行為，您可以建立 FraudLog 模型：
from django.db import models
from django.contrib.auth.models import User
class FraudLog(models.Model):
suspicious_user = models.ForeignKey(User, related_name='suspicious_logs', on_delete=models.CASCADE)
similar_user = models.ForeignKey(User, related_name='similar_logs', on_delete=models.CASCADE)
distance = models.IntegerField()
created_at = models.DateTimeField(auto_now_add=True)
在此範本中儲存可疑符合項目：
from django.db.models import Q
from .models import User # Assume User is your user model
def levenshtein_distance(a, b):
n, m = len(a), len(b)
if n > m:
a, b = b, a
n, m = m, n
current_row = range(n + 1) # Keep current and previous row
for i in range(1, m + 1):
previous_row, current_row = current_row, [i] + [0] * n
for j in range(1, n + 1):
add, delete, change = previous_row[j] + 1, current_row[j - 1] + 1, previous_row[j - 1]
if a[j - 1] != b[i - 1]:
change += 1
current_row[j] = min(add, delete, change)
return current_row[n]
透過這種方法，您已經實現了基於編輯距離的詐欺偵測系統。它有助於識別相似的條目，降低建立詐欺帳戶或重複資料的風險。該系統是可擴展的，可以進行調整以滿足您專案的特定需求。
以上是在 Django 專案中實現具有 Levenshtein Distance 的詐欺偵測系統的詳細內容。更多資訊請關注PHP中文網其他相關文章！
