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检测加油站信用卡信息盗取者的移动应用

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根据给定文件信息,以下为详细的知识点解析: ### 标题知识点 **Skimmer_Scanner** 是一个专门针对特定安全威胁设计的移动应用程序。其设计目的主要是为了帮助用户检测在加油站等场所通过蓝牙技术非法复制信用卡信息的装置,这类装置被称为“信用卡撇取者”或“Skimmers”。 ### 描述知识点 1. **技术背景**:信用卡信息的非法复制通常是通过在支付终端上安装一个额外的设备来实现的。这些装置能够复制用户输入的信用卡信息,包括卡号和安全码等。 2. **应用目的**:Skimmer Scanner 应用的主要功能是扫描附近的蓝牙设备,并寻找特定的蓝牙名称(如 HC-05)。这种名称的设备常被用于信用卡信息的非法采集。 3. **检测机制**:应用程序通过搜索蓝牙设备,一旦发现名为 HC-05 的设备,将尝试使用通用密码(如 1234)进行连接。如果连接成功,并且能够收到特定的响应(例如响应字母“M”),则提示用户可能有信用卡撇取者在附近。 4. **操作限制**:虽然能够检测到潜在的信用卡撇取者设备,但该应用程序并不具备从该设备中提取数据的功能,也不能通过网络向执法机构报告发现。这意味着它只是一个检测工具,而非解决方案。 5. **平台与开源**:Skimmer Scanner 作为一款免费的开源应用程序,目前只提供 Android 平台的下载。开发者明确表示需要社区的帮助,特别是在开发 iOS 版本方面。 6. **开发与贡献**:该应用程序由一群特定的开发者(尼克·普尔(Nick Poole)、Nathan Seidle 和 Rob Reynolds)共同创作。他们呼吁社区提供任何可能的帮助,以改进和完善这个应用程序。 ### 标签知识点 **C#**:这是一个非常重要的技术点,表明 Skimmer Scanner 应用程序的开发至少涉及到了 C# 编程语言。C# 是一种由微软开发的面向对象的编程语言,广泛用于 .NET 开发平台。它通常用于开发各种应用程序,包括移动应用程序、桌面应用程序以及服务器端应用程序。 ### 压缩包子文件的知识点 **Skimmer_Scanner-master**:这是文件名称列表中提及的内容,它指向了一个源代码仓库的主目录。在这个目录下,包含了应用程序的源代码(/src-源文件),通常由 C# 语言编写。此目录一般会包含应用程序的主要逻辑、用户界面和功能实现。 ### 综合分析 在给出的文件信息中,我们可以得出以下几点关键结论: - **安全意识**:随着数字化支付方式的普及,针对支付终端的攻击手段也越来越多样化,像 Skimmer Scanner 这样的应用程序有助于提升用户的安全意识。 - **技术细节**:通过寻找特定的蓝牙设备名称和使用默认密码尝试连接的方法,应用程序利用了攻击者使用的一些常见做法,即在未授权的情况下使用默认配置。 - **开源社区**:开源项目允许更多的开发者参与到项目中来,通过集体的智慧和力量,共同促进项目的发展和完善。 - **技术应用**:Skimmer Scanner 应用程序的开发体现了移动应用程序开发在安全领域的应用,同时也展示了移动技术在我们日常生活中扮演的安全角色。 - **平台限制与开发需求**:应用程序目前仅限于 Android 平台,表明市场上对于跨平台解决方案的需求,同时也说明了开源项目中可能存在的资源限制。 - **教育意义**:该项目也具有一定的教育意义,可以让学习编程的开发者了解到真实世界中的应用案例以及编程语言在安全检测中的应用。 在总结以上内容后,我们可以清晰地看到 Skimmer Scanner 应用程序背后所涉及的丰富的技术知识点和现实世界中的应用场景,以及开源文化对于项目成长的重要性。

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import numpy as np import os from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from sklearn.neighbors import KNeighborsClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.svm import SVC from sklearn.linear_model import LogisticRegression import time def load_data(file_path): """加载数据文件""" data = [] with open(file_path, 'r') as f: for line in f: # 处理每行数据 line = line.strip() if line: # 分割逗号分隔的值 values = line.split(',') # 第一个值是标签,其余是特征 label = int(values[0].strip()) features = [float(x.strip()) for x in values[1:]] data.append([label] + features) return data def normalize_data(X): """ Step 1: 数据归一化 使用公式: r_ij = x_ij / sqrt(∑_i x_ij^2) 其中 x_ij 是第i个样本的第j个属性值 """ # 计算每列的平方和 col_square_sum = np.sum(X **2, axis=0) # 避免除以零 col_square_sum[col_square_sum == 0] = 1e-10 # 计算归一化系数 norm_factors = np.sqrt(col_square_sum) # 归一化数据 X_normalized = X / norm_factors return X_normalized def fuzzy_relation(X, B): """ 计算模糊关系 R_B(y,x) = 1 - min(1, √(∑_{k∈B} (x_k - y_k)^2)) 其中B是条件属性子集 """ n_samples = X.shape[0] n_features = len(B) R = np.zeros((n_samples, n_samples)) for i in range(n_samples): for j in range(n_samples): # 计算B中特征的欧氏距离 diff = X[i, B] - X[j, B] distance = np.sqrt(np.sum(diff** 2)) R[i, j] = 1 - min(1, distance) return R def fuzzy_implication(a, b): """模糊蕴含函数 I(a, b) = max(1-a, b)""" return max(1 - a, b) def lower_approximation(R, D): """ 计算模糊下近似 R(D)(y) = inf_{x∈U} I(R(y,x), D(x)) """ n_samples = R.shape[0] approx = np.zeros(n_samples) for y in range(n_samples): min_val = 1.0 # 初始化最小值 for x in range(n_samples): # 计算模糊蕴含 imp_val = fuzzy_implication(R[y, x], D[x]) # 取最小值 if imp_val < min_val: min_val = imp_val approx[y] = min_val return approx def positive_region(X, B, decision_classes): """ 计算正区域 POS_B(D) = ∪_{i=1}^r R_B(D_i) """ # 计算模糊关系 R = fuzzy_relation(X, B) # 对每个决策类计算下近似 n_classes = len(decision_classes) pos_region = np.zeros(X.shape[0]) for i in range(n_classes): # 获取当前决策类的隶属度 D_i = decision_classes[i] # 计算下近似 approx = lower_approximation(R, D_i) # 取并集 (取最大值) pos_region = np.maximum(pos_region, approx) return pos_region def dependence(X, B, decision_classes): """ 计算依赖度 γ_B(D) = |POS_B(D)| / |U| """ pos_region = positive_region(X, B, decision_classes) return np.sum(pos_region) / len(pos_region) def io_fs_algorithm(X, y, delta=0.05): """ Step 2: IO-FS算法实现 输入: 决策表(X, y), 依赖度最小增量δ 输出: 选择的特征子集 算法步骤: 1. 初始化 Result = ∅ 2. 当 A ≠ ∅ 时循环: a. 对每个属性 a ∈ A,计算 γ_Result∪{a}(D) b. 找到使 γ_Result∪{a}(D) 最大的属性 a c. 如果 γ_Result∪{a}(D) - γ_Result(D) ≥ δ: - 将 a 添加到 Result - 从 A 中移除 a d. 否则返回 Result 3. 返回 Result """ # 获取所有特征索引 all_features = list(range(X.shape[1])) # 初始化结果集 result = [] # 获取决策类信息 unique_classes = np.unique(y) decision_classes = [] for cls in unique_classes: # 创建决策类的隶属度向量 D_i = (y == cls).astype(float) decision_classes.append(D_i) # 初始依赖度 (空集时的依赖度) current_gamma = dependence(X, [], decision_classes) print(f"初始依赖度 (空集): {current_gamma:.4f}") # 迭代选择特征 while all_features: best_gain = -1 best_feature = None best_gamma = 0 # 尝试添加每个剩余特征 for feature in all_features: candidate_set = result + [feature] gamma = dependence(X, candidate_set, decision_classes) gain = gamma - current_gamma if gain > best_gain: best_gain = gain best_feature = feature best_gamma = gamma # 检查增益是否大于阈值 if best_gain >= delta: result.append(best_feature) all_features.remove(best_feature) current_gamma = best_gamma print(f"添加特征 {best_feature}, 增益: {best_gain:.4f}, 当前依赖度: {current_gamma:.4f}") else: print(f"增益 {best_gain:.4f} 小于阈值 {delta}, 停止选择") break return result def evaluate_features(X, y, selected_features, test_size=0.3): """评估选择的特征""" if not selected_features: print("没有选择任何特征") return # 准备数据 X_selected = X[:, selected_features] # 划分训练测试集 X_train, X_test, y_train, y_test = train_test_split( X_selected, y, test_size=test_size, random_state=42 ) # 定义分类器 - 保持SVM不变,更新其他分类器 classifiers = { # J48: 使用信息熵的决策树作为J48的替代 'J48': DecisionTreeClassifier(criterion='entropy', max_depth=None, min_samples_split=2), # KNN: 保持KNeighborsClassifier 'KNN': KNeighborsClassifier(), # SVM: 保持原始代码中的SVM不变 'SVM': SVC(), # RIPPER: 使用带L1正则化的逻辑回归作为RIPPER规则学习算法的替代 'RIPPER': LogisticRegression(solver='liblinear', penalty='l1'), # Decision Table: 使用浅层决策树作为决策表的替代 'Decision Table': DecisionTreeClassifier(max_depth=3, min_samples_leaf=5) } # 评估每个分类器 results = {} for name, clf in classifiers.items(): start_time = time.time() clf.fit(X_train, y_train) y_pred = clf.predict(X_test) acc = accuracy_score(y_test, y_pred) train_time = time.time() - start_time results[name] = {'accuracy': acc, 'time': train_time} print(f"{name}: 准确率 = {acc:.4f}, 训练时间 = {train_time:.4f}s") return results # 主程序 if __name__ == "__main__": # 数据路径 - 修改为葡萄酒数据集路径 data_file = r"D:\aaxiazai\pythonProject\张等\wine\wine.data" # 加载数据 print("加载数据...") data = load_data(data_file) # 转换为numpy数组 data_array = np.array(data) y = data_array[:, 0] # 标签 X = data_array[:, 1:] # 特征 print(f"数据形状: {X.shape}, 标签形状: {y.shape}") print(f"类别分布: {np.unique(y, return_counts=True)}") # Step 1: 数据归一化 print("Step 1: 数据归一化...") X_normalized = normalize_data(X) # Step 2: 运行IO-FS算法 print("Step 2: 运行IO-FS算法...") delta = 0.003 # 依赖度增益阈值 selected_features = io_fs_algorithm(X_normalized, y, delta) print(f"\n选择的特征: {sorted(selected_features)}") print(f"选择特征数量: {len(selected_features)}") # 评估选择的特征 print("\n评估选择的特征...") results = evaluate_features(X_normalized, y, selected_features) # 与使用所有特征进行比较 print("\n与使用所有特征进行比较...") all_features_results = evaluate_features(X_normalized, y, list(range(X.shape[1]))) 修改代码。地址变为D:\aaxiazai\pythonProject\张等\spambase,数据集为spambase.data,内部数据集aardvark,1,0,0,1,0,0,1,1,1,1,0,0,4,0,0,1,1 antelope,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1 bass,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4 bear,1,0,0,1,0,0,1,1,1,1,0,0,4,0,0,1,1 boar,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1 buffalo,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1 calf,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,1 carp,0,0,1,0,0,1,0,1,1,0,0,1,0,1,1,0,4 catfish,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4 cavy,1,0,0,1,0,0,0,1,1,1,0,0,4,0,1,0,1 cheetah,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1 chicken,0,1,1,0,1,0,0,0,1,1,0,0,2,1,1,0,2 chub,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4 clam,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,7 crab,0,0,1,0,0,1,1,0,0,0,0,0,4,0,0,0,7 crayfish,0,0,1,0,0,1,1,0,0,0,0,0,6,0,0,0,7 crow,0,1,1,0,1,0,1,0,1,1,0,0,2,1,0,0,2 deer,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1 dogfish,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,1,4 dolphin,0,0,0,1,0,1,1,1,1,1,0,1,0,1,0,1,1 dove,0,1,1,0,1,0,0,0,1,1,0,0,2,1,1,0,2 duck,0,1,1,0,1,1,0,0,1,1,0,0,2,1,0,0,2 elephant,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1 flamingo,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,1,2 flea,0,0,1,0,0,0,0,0,0,1,0,0,6,0,0,0,6 frog,0,0,1,0,0,1,1,1,1,1,0,0,4,0,0,0,5 frog,0,0,1,0,0,1,1,1,1,1,1,0,4,0,0,0,5 fruitbat,1,0,0,1,1,0,0,1,1,1,0,0,2,1,0,0,1 giraffe,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1 girl,1,0,0,1,0,0,1,1,1,1,0,0,2,0,1,1,1 gnat,0,0,1,0,1,0,0,0,0,1,0,0,6,0,0,0,6 goat,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,1 gorilla,1,0,0,1,0,0,0,1,1,1,0,0,2,0,0,1,1 gull,0,1,1,0,1,1,1,0,1,1,0,0,2,1,0,0,2 haddock,0,0,1,0,0,1,0,1,1,0,0,1,0,1,0,0,4 hamster,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,0,1 hare,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,0,1 hawk,0,1,1,0,1,0,1,0,1,1,0,0,2,1,0,0,2 herring,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4 honeybee,1,0,1,0,1,0,0,0,0,1,1,0,6,0,1,0,6 housefly,1,0,1,0,1,0,0,0,0,1,0,0,6,0,0,0,6 kiwi,0,1,1,0,0,0,1,0,1,1,0,0,2,1,0,0,2 ladybird,0,0,1,0,1,0,1,0,0,1,0,0,6,0,0,0,6 lark,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,2 leopard,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1 lion,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1 lobster,0,0,1,0,0,1,1,0,0,0,0,0,6,0,0,0,7 lynx,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1 mink,1,0,0,1,0,1,1,1,1,1,0,0,4,1,0,1,1 mole,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,0,1 mongoose,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1 moth,1,0,1,0,1,0,0,0,0,1,0,0,6,0,0,0,6 newt,0,0,1,0,0,1,1,1,1,1,0,0,4,1,0,0,5 octopus,0,0,1,0,0,1,1,0,0,0,0,0,8,0,0,1,7 opossum,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,0,1 oryx,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1 ostrich,0,1,1,0,0,0,0,0,1,1,0,0,2,1,0,1,2 parakeet,0,1,1,0,1,0,0,0,1,1,0,0,2,1,1,0,2 penguin,0,1,1,0,0,1,1,0,1,1,0,0,2,1,0,1,2 pheasant,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,2 pike,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,1,4 piranha,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4 pitviper,0,0,1,0,0,0,1,1,1,1,1,0,0,1,0,0,3 platypus,1,0,1,1,0,1,1,0,1,1,0,0,4,1,0,1,1 polecat,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1 pony,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,1 porpoise,0,0,0,1,0,1,1,1,1,1,0,1,0,1,0,1,1 puma,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1 pussycat,1,0,0,1,0,0,1,1,1,1,0,0,4,1,1,1,1 raccoon,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1 reindeer,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,1 rhea,0,1,1,0,0,0,1,0,1,1,0,0,2,1,0,1,2 scorpion,0,0,0,0,0,0,1,0,0,1,1,0,8,1,0,0,7 seahorse,0,0,1,0,0,1,0,1,1,0,0,1,0,1,0,0,4 seal,1,0,0,1,0,1,1,1,1,1,0,1,0,0,0,1,1 sealion,1,0,0,1,0,1,1,1,1,1,0,1,2,1,0,1,1 seasnake,0,0,0,0,0,1,1,1,1,0,1,0,0,1,0,0,3 seawasp,0,0,1,0,0,1,1,0,0,0,1,0,0,0,0,0,7 skimmer,0,1,1,0,1,1,1,0,1,1,0,0,2,1,0,0,2 skua,0,1,1,0,1,1,1,0,1,1,0,0,2,1,0,0,2 slowworm,0,0,1,0,0,0,1,1,1,1,0,0,0,1,0,0,3 slug,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,7 sole,0,0,1,0,0,1,0,1,1,0,0,1,0,1,0,0,4 sparrow,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,2 squirrel,1,0,0,1,0,0,0,1,1,1,0,0,2,1,0,0,1 starfish,0,0,1,0,0,1,1,0,0,0,0,0,5,0,0,0,7 stingray,0,0,1,0,0,1,1,1,1,0,1,1,0,1,0,1,4 swan,0,1,1,0,1,1,0,0,1,1,0,0,2,1,0,1,2 termite,0,0,1,0,0,0,0,0,0,1,0,0,6,0,0,0,6 toad,0,0,1,0,0,1,0,1,1,1,0,0,4,0,0,0,5 tortoise,0,0,1,0,0,0,0,0,1,1,0,0,4,1,0,1,3 tuatara,0,0,1,0,0,0,1,1,1,1,0,0,4,1,0,0,3 tuna,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,1,4 vampire,1,0,0,1,1,0,0,1,1,1,0,0,2,1,0,0,1 vole,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,0,1 vulture,0,1,1,0,1,0,1,0,1,1,0,0,2,1,0,1,2 wallaby,1,0,0,1,0,0,0,1,1,1,0,0,2,1,0,1,1 wasp,1,0,1,0,1,0,0,0,0,1,1,0,6,0,0,0,6 wolf,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1 worm,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,7 wren,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,2 输出格式不变

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