使用K最近邻居进行异常检测？

时间：2014-01-28 20:52:10

标签： python scikit-learn knn outliers

我想基于19个功能的流量统计制作网络入侵检测系统。我已成功尝试了One Class SVM算法，但听说k最近邻居也可以执行此任务。同样地，我有一个异常的免费训练数据集和一个带有一些异常和相关标签的测试数据集（1表示正常，-1表示异常）。

training_samples.csv （200个第一个样本，完整文件包含~1200）

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7, 3, 2, 1, 2, 0, 2, 5, 5, 1, 1, 106, 250.571428571, 52252.244898, 612, 0, 0, 0, 0
6, 3, 2, 1, 2, 0, 3, 3, 3, 1, 1, 106, 359.0, 64009.0, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 3, 4, 4, 1, 1, 106, 322.857142857, 62702.6938776, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 2, 5, 5, 1, 1, 106, 250.571428571, 52252.244898, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 3, 4, 4, 1, 1, 106, 322.857142857, 62702.6938776, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 3, 4, 4, 1, 1, 106, 322.857142857, 62702.6938776, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 2, 5, 5, 1, 1, 106, 250.571428571, 52252.244898, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 3, 4, 4, 1, 1, 106, 322.857142857, 62702.6938776, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 3, 4, 4, 1, 1, 106, 322.857142857, 62702.6938776, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 2, 5, 5, 1, 1, 106, 250.571428571, 52252.244898, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 3, 4, 4, 1, 1, 106, 322.857142857, 62702.6938776, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 3, 4, 4, 1, 1, 106, 322.857142857, 62702.6938776, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 2, 5, 5, 1, 1, 106, 250.571428571, 52252.244898, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 2, 5, 5, 1, 1, 106, 250.571428571, 52252.244898, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 3, 4, 4, 1, 1, 106, 322.857142857, 62702.6938776, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 3, 4, 4, 1, 1, 106, 322.857142857, 62702.6938776, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 2, 5, 5, 1, 1, 106, 250.571428571, 52252.244898, 612, 0, 0, 0, 0
6, 3, 2, 1, 2, 0, 3, 3, 3, 1, 1, 106, 359.0, 64009.0, 612, 0, 0, 0, 0
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7, 3, 2, 1, 2, 0, 2, 5, 5, 1, 1, 106, 250.571428571, 52252.244898, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 3, 4, 4, 1, 1, 106, 322.857142857, 62702.6938776, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 3, 4, 4, 1, 1, 106, 322.857142857, 62702.6938776, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 2, 5, 5, 1, 1, 106, 250.571428571, 52252.244898, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 3, 4, 4, 1, 1, 106, 322.857142857, 62702.6938776, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 3, 4, 4, 1, 1, 106, 322.857142857, 62702.6938776, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 2, 5, 5, 1, 1, 106, 250.571428571, 52252.244898, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 3, 4, 4, 1, 1, 106, 322.857142857, 62702.6938776, 612, 0, 0, 0, 0
6, 3, 2, 1, 2, 0, 2, 4, 4, 1, 1, 106, 274.666666667, 56896.8888889, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 3, 4, 4, 1, 1, 106, 322.857142857, 62702.6938776, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 2, 5, 5, 1, 1, 106, 250.571428571, 52252.244898, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 3, 4, 4, 1, 1, 106, 322.857142857, 62702.6938776, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 3, 4, 4, 1, 1, 106, 322.857142857, 62702.6938776, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 2, 5, 5, 1, 1, 106, 250.571428571, 52252.244898, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 3, 4, 4, 1, 1, 106, 322.857142857, 62702.6938776, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 3, 4, 4, 1, 1, 106, 322.857142857, 62702.6938776, 612, 0, 0, 0, 0
7, 3, 2, 1, 2, 0, 2, 5, 5, 1, 1, 106, 250.571428571, 52252.244898, 612, 0, 0, 0, 0

testing_samples.csv （100个第一个样本，完整文件包含193个）

7, 3, 2, 1, 2, 0, 2, 5, 5, 1, 1, 106, 250.571428571, 52252.244898, 612, 0, 0, 0, 0
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12, 4, 3, 2, 2, 0, 4, 6, 6, 1, 1, 92, 272.333333333, 57711.2222222, 612, 0, 0, 0, 0
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1660, 3, 3, 3, 2, 174, 1652, 1652, 1652, 1, 1, 57, 57.2108433735, 9.40132820438, 106, 0, 0, 0, 0
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12, 3, 2, 1, 2, 0, 4, 8, 8, 1, 1, 106, 274.666666667, 56896.8888889, 612, 0, 0, 0, 0
12, 3, 2, 1, 2, 0, 5, 7, 7, 1, 1, 106, 316.833333333, 62230.9722222, 612, 0, 0, 0, 0
12, 3, 2, 1, 2, 0, 4, 8, 8, 1, 1, 106, 274.666666667, 56896.8888889, 612, 0, 0, 0, 0
12, 3, 2, 1, 2, 0, 6, 6, 6, 1, 1, 106, 359.0, 64009.0, 612, 0, 0, 0, 0
10, 3, 2, 1, 2, 0, 4, 6, 6, 1, 1, 106, 308.4, 61448.64, 612, 0, 0, 0, 0
12, 3, 2, 1, 2, 0, 4, 8, 8, 1, 1, 106, 274.666666667, 56896.8888889, 612, 0, 0, 0, 0
34, 4, 6, 2, 4, 0, 13, 8, 6, 1, 1, 42, 144.882352941, 38046.633218, 612, 1, 0, 0, 0
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12, 3, 2, 1, 2, 0, 5, 7, 7, 1, 1, 106, 316.833333333, 62230.9722222, 612, 0, 0, 0, 0
12, 3, 2, 1, 2, 0, 6, 6, 6, 1, 1, 106, 359.0, 64009.0, 612, 0, 0, 0, 0
12, 3, 2, 1, 2, 0, 4, 8, 8, 1, 1, 106, 274.666666667, 56896.8888889, 612, 0, 0, 0, 0
10, 3, 2, 1, 2, 0, 4, 6, 6, 1, 1, 106, 308.4, 61448.64, 612, 0, 0, 0, 0
12, 3, 2, 1, 2, 0, 5, 7, 7, 1, 1, 106, 316.833333333, 62230.9722222, 612, 0, 0, 0, 0
12, 3, 2, 1, 2, 0, 4, 8, 8, 1, 1, 106, 274.666666667, 56896.8888889, 612, 0, 0, 0, 0
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10, 3, 2, 1, 2, 0, 4, 6, 6, 1, 1, 106, 308.4, 61448.64, 612, 0, 0, 0, 0
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12, 3, 2, 1, 2, 0, 4, 8, 8, 1, 2, 60, 182.666666667, 37147.5555556, 612, 0, 0, 0, 0
10, 2, 2, 1, 2, 0, 3, 7, 7, 1, 1, 106, 257.8, 53767.56, 612, 0, 0, 0, 0
10, 2, 2, 1, 2, 0, 2, 8, 8, 1, 1, 106, 207.2, 40965.76, 612, 0, 0, 0, 0
10, 2, 2, 1, 2, 0, 2, 8, 8, 1, 1, 106, 207.2, 40965.76, 612, 0, 0, 0, 0
12, 4, 3, 2, 3, 0, 5, 7, 7, 1, 2, 60, 185.833333333, 36478.3055556, 612, 0, 0, 0, 0
10, 3, 2, 1, 2, 0, 3, 7, 7, 1, 2, 60, 202.6, 42087.24, 612, 0, 0, 0, 0
10, 2, 2, 1, 2, 0, 2, 8, 8, 1, 1, 106, 207.2, 40965.76, 612, 0, 0, 0, 0
14, 3, 2, 1, 3, 0, 6, 8, 8, 1, 1, 106, 322.857142857, 62702.6938776, 612, 0, 0, 0, 0
12, 3, 2, 1, 2, 0, 4, 8, 8, 1, 1, 106, 274.666666667, 56896.8888889, 612, 0, 0, 0, 0
12, 3, 2, 1, 2, 0, 4, 8, 8, 1, 1, 106, 274.666666667, 56896.8888889, 612, 0, 0, 0, 0
12, 3, 2, 1, 2, 0, 5, 7, 7, 1, 1, 106, 316.833333333, 62230.9722222, 612, 0, 0, 0, 0
12, 3, 2, 1, 2, 0, 6, 6, 6, 1, 1, 106, 359.0, 64009.0, 612, 0, 0, 0, 0
12, 3, 2, 1, 2, 0, 4, 8, 8, 1, 1, 106, 274.666666667, 56896.8888889, 612, 0, 0, 0, 0
10, 3, 2, 1, 2, 0, 4, 6, 6, 1, 1, 106, 308.4, 61448.64, 612, 0, 0, 0, 0
12, 3, 2, 1, 2, 0, 5, 7, 7, 1, 1, 106, 316.833333333, 62230.9722222, 612, 0, 0, 0, 0
12, 3, 2, 1, 2, 0, 4, 8, 8, 1, 1, 106, 274.666666667, 56896.8888889, 612, 0, 0, 0, 0
32, 3, 3, 2, 3, 0, 21, 20, 7, 1, 10, 42, 74.9375, 9984.49609375, 612, 0, 0, 0, 0
12, 3, 2, 1, 3, 0, 5, 7, 7, 1, 1, 106, 316.833333333, 62230.9722222, 612, 0, 0, 0, 0
12, 3, 2, 1, 2, 0, 4, 8, 8, 1, 1, 106, 274.666666667, 56896.8888889, 612, 0, 0, 0, 0
12, 3, 2, 1, 2, 0, 5, 7, 7, 1, 1, 106, 316.833333333, 62230.9722222, 612, 0, 0, 0, 0
12, 3, 2, 1, 2, 0, 4, 8, 8, 1, 1, 106, 274.666666667, 56896.8888889, 612, 0, 0, 0, 0
10, 3, 2, 1, 2, 0, 4, 6, 6, 1, 1, 106, 308.4, 61448.64, 612, 0, 0, 0, 0

testing_labels.csv （100个第一个标签，完整文件包含193个）

1
1
1
1
1
1
1
-1
-1
1
1
1
1
1
1
1
1
1
-1
-1
1
1
1
1
1
1
1
1
-1
-1
1
1
1
1
1
1
1
1
-1
1
1
1
1
1
1
1
1
-1
-1
1
1
1
1
1
1
1
1
-1
-1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

我正在使用Scikit-Learn的KNeighborsClassifier实现，但所有预测标签都设置为1：

#!/usr/bin/python
import csv, numpy
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import MinMaxScaler

# To save time and avoid attribute reconstruction, we have prebuilt training and testing files 
# where the attributes are presented under CSV format.
# We just need to convert these files into matrices so they can be used directly as input 
# of the machine learning algorithms.

def csv_attributes_to_matrix(csv_file):
    with open(csv_file, 'r') as data:
        rows = csv.reader(data, delimiter=',', quoting=csv.QUOTE_NONNUMERIC)
        return MinMaxScaler().fit_transform(numpy.array(list(rows)))

def csv_labels_to_matrix(csv_file):
    with open(csv_file, 'r') as data:
        rows = csv.reader(data, delimiter=',')
        return [(int(row[0])) for row in rows]

# Create vectors of normal labels for training => all-1-vector
def create_normal_vectors(MATRIX_NORM_length):
    y = list()
    for l in range(MATRIX_NORM_length):
        y.append(1)
    return numpy.array(y)

# Test of KNeighborsClassifier for anomaly detection
def kNN_test(MATRIX_NORM, MATRIX_ANOM, real_labels):
    Y = create_normal_vectors(len(MATRIX_NORM))

    # Parameter grid search    
    for n_neighbors in [1, 2, 3, 5, 10]:
        for weights in ["uniform", "distance"]:
            for algo in ["ball_tree", "kd_tree", "brute"]:
                for p in [1, 5, 10]:
                    for leaf_size in [1, 5, 10] if algo in ["ball_tree", "kd_tree"] else [None]:
                        trained_model = KNeighborsClassifier(n_neighbors, weights, algo, leaf_size, p)
                        trained_model.fit(MATRIX_NORM, Y)
                        predicted_labels = trained_model.predict(MATRIX_ANOM)
                        # Predicted labels are always all set to 1, why ?
                        print (n_neighbors, weights, algo, p, leaf_size), "\n", predicted_labels

# Normal (training) and anomalous (testing) input csv files:
MATRIX_NORM = csv_attributes_to_matrix("training_samples.csv")
MATRIX_ANOM = csv_attributes_to_matrix("testing_samples.csv")
real_labels = csv_labels_to_matrix("testing_labels.csv")

# Launch test
kNN_test(MATRIX_NORM, MATRIX_ANOM, real_labels)

可以使用K最近邻算法（如果不是来自sklearn，来自另一个库的算法）来执行新颖性/离群值检测吗？

1 个答案:

答案 0 :(得分：1)

我会给the wikipedia article一个读数。特别是，

  

对象按其邻居的多数票进行分类，对象被分配给其k个最近邻居中最常见的类（k是正整数，通常很小）。

你create_normal_vectors的训练集说每个点都是“正常的”，所以当一个未标记的点询问它的邻居它属于哪个类时，每个点都会投票给“1级”。

您可以查看scikit learn's个文档，了解其他方法。

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