{"id":148,"date":"2025-08-11T17:13:25","date_gmt":"2025-08-11T09:13:25","guid":{"rendered":"http:\/\/blog.which778.cn\/?p=148"},"modified":"2025-08-12T16:18:05","modified_gmt":"2025-08-12T08:18:05","slug":"2025%e6%b1%9f%e8%8b%8f%e6%95%b0%e6%8d%ae%e5%ae%89%e5%85%a8%e7%ab%9e%e8%b5%9b%e5%88%9d%e8%b5%9bwp","status":"publish","type":"post","link":"http:\/\/blog.which778.cn\/?p=148","title":{"rendered":"\u6c5f\u82cf\u7701\u7b2c\u4e09\u5c4a\u6570\u636e\u5b89\u5168\u804c\u4e1a\u6280\u80fd\u7ade\u8d5b\u521d\u8d5bwp"},"content":{"rendered":"

\u6c5f\u82cf\u7701\u7b2c\u4e09\u5c4a\u6570\u636e\u5b89\u5168\u6280\u672f\u5e94\u7528\u804c\u4e1a\u6280\u80fd\u7ade\u8d5b\u521d\u8d5bwp<\/h1>\n

\u901a\u8fc7\u7f51\u76d8\u5206\u4eab\u7684\u6587\u4ef6\uff1a\u6c5f\u82cf\u7701\u7b2c\u4e09\u5c4a\u6570\u636e\u5b89\u5168\u804c\u4e1a\u6280\u80fd\u7ade\u8d5b\u521d\u8d5b\u9644\u4ef6<\/strong>
\n\u94fe\u63a5: https:\/\/pan.baidu.com\/s\/1K3bbqlQejtRBbXI-9vsiJw?pwd=8yu7<\/a> \u63d0\u53d6\u7801: 8yu7<\/strong>
\n--\u6765\u81ea\u767e\u5ea6\u7f51\u76d8\u8d85\u7ea7\u4f1a\u5458v6\u7684\u5206\u4eab<\/strong><\/p>\n

\u6570\u636e\u5b89\u5168(500pt)<\/h2>\n

1.ds_re_j02(100pt)<\/h3>\n

base32\u52a0\u5bc6<\/p>\n

\"\"<\/p>\n

799090202130037769<\/code><\/pre>\n
2.wm(100pt)<\/h3>\n
\u9898\u76ee\u63cf\u8ff0\uff1a\u4e0b\u8f7d\u9644\u4ef6\u5e76\u5206\u6790\u83b7\u53d6\u6c34\u5370\u4fe1\u606f\uff0c\u63d0\u4ea4\u5c0f\u5199md5(\u6c34\u5370)\uff0c\u5982\u6c34\u5370\u4e3a123-abc\uff0c\u5219\u63d0\u4ea49f4e93a0beb91038f711f7e73e909398\u3002<\/code><\/pre>\n
\u89e3\u538b\u53d1\u73b0\u662fsql\u6587\u4ef6\u8fdb\u884c\u5206\u6790<\/p>\n
\"\"<\/p>\n
\u53d1\u73b0\u662fbase32\uff0c\u63d0\u53d6\u540e\u9762\u7684\u6570\u636e\u8fdb\u884c\u89e3\u5bc6\uff0c\u53ef\u4ee5\u770b\u5230\u662f\u4e71\u7801\u53ef\u4ee5\u770b\u5230\u4e8c\u8fdb\u5236\u5e94\u8be5\u662f0\u5f00\u5934<\/p>\n
\"\"<\/p>\n
\u6211\u4eec\u52a0\u4e0a0\u8fdb\u884c\u89e3\u5bc6<\/p>\n
\"\"<\/p>\n
6b8cd6a63b9a8a66db7886dc4905de24<\/code><\/pre>\n
3.\u5947\u602a\u7684\u52a0\u5bc6\u6587\u4ef6(100pt)<\/h3>\n
\u9898\u76ee\u63cf\u8ff0\uff1a\u67d0\u516c\u53f8\u4f7f\u7528\u56fd\u4ea7\u5546\u7528\u5bc6\u7801\u52a0\u5bc6\u4fdd\u62a4\u5458\u5de5\u8003\u52e4\u6570\u636e\uff0c\u56e0\u7ba1\u7406\u5458\u610f\u5916\u79bb\u804c\u672a\u4ea4\u63a5\u5bc6\u7801\uff0c\u5bfc\u81f4\u8003\u52e4\u6587\u4ef6\u65e0\u6cd5\u89e3\u5bc6\u3002\u8fd9\u76f4\u63a5\u5f71\u54cd\u4e86\u516c\u53f8\u8d22\u52a1\u90e8\u5bf9\u5458\u5de5\u7684\u85aa\u8d44\u6838\u7b97\u3002\u516c\u53f8\u9080\u8bf7\u4f60\uff0c\u4e13\u4e1a\u7684\u6570\u636e\u5b89\u5168\u9ad8\u624b\uff0c\u8bf7\u53d1\u6325\u4f60\u7684\u6280\u80fd\u7279\u957f\uff0c\u8fd0\u7528\u5df2\u77e5\u7684\u7ebf\u7d22\u6765\u7834\u89e3\u8fd9\u4e2a\u96be\u9898\uff0c\u89e3\u5bc6\u8003\u52e4\u6587\u4ef6\u83b7\u53d6flag\u3002<\/code><\/pre>\n
\u76f4\u63a5\u62d6\u8fdb\u968f\u6ce2\u9010\u6d41\u53d1\u73b0base64\u76f4\u63a5\u89e3\u7801\u53d1\u73b0key\uff1a02001010120231025 <\/p>\n
\"\"<\/p>\n
\u56fd\u5bc6\u89e3\u7801\uff0civ:0123456789abcdef
\n\"\"<\/p>\n
4.\u516c\u53f8\u673a\u5bc6(100pt)<\/h3>\n
\u9898\u76ee\u63cf\u8ff0\uff1a\u67d0\u4e0a\u5e02\u516c\u53f8CE0\u674e\u603b\uff0c\u60ca\u6089\u516c\u53f8\u91cd\u8981\u5546\u4e1a\u673a\u5bc6\u906d\u5230\u6cc4\u5bc6\uff0c\u8fd9\u53ef\u80fd\u7ed9\u516c\u53f8\u5e26\u6765\u5de8\u5927\u7ecf\u6d4e\u635f\u5931\u548c\u58f0\u8a89\u5371\u673a\u3002\u7ecf\u8fc7\u521d\u6b65\u6392\u67e5\uff0c\u674e\u603b\u6000\u7591\u4e0e\u81ea\u5df1\u7684\u5fc3\u8179\u5c0f\u8d75\u6709\u5173\u3002\u636e\u4e86\u89e3\uff0c\u5c0f\u8d75\u6709\u4e2a\u7279\u6b8a\u7656\u597d\uff0c\u559c\u6b22\u7528\u4e00\u4e9b\u770b\u4f3c\u83ab\u540d\u5176\u5999\u7684\u6587\u4ef6\u521b\u5efa\u65f6\u95f4\u4f5c\u4e3a\u5bc6\u7801\uff0c\u53ef\u5173\u952e\u6587\u4ef6\u76ee\u524d\u5df2\u88ab\u5220\u9664\u3002\u5728\u62a5\u8b66\u540e\uff0c\u8b66\u65b9\u4ecb\u5165\u8c03\u67e5\u540e\uff0c\u8fd0\u7528\u4e13\u4e1a\u7684\u6570\u636e\u6062\u590d\u5de5\u5177\uff0c\u6210\u529f\u627e\u56de\u4e86\u90e8\u5206\u76f8\u5173\u6587\u4ef6\uff0c\u5176\u4e2d\u5305\u542b\u4e00\u5f20\u5947\u602a\u7684\u56fe\u7247\uff0c\u56fe\u7247\u4e0a\u662f\u4e00\u4e9b\u6742\u4e71\u65e0\u7ae0\u7684\u51e0\u4f55\u56fe\u5f62\u548c\u989c\u8272\u8272\u5757\uff0c\u4f46\u6587\u4ef6\u521b\u5efa\u65f6\u95f4\u5374\u96be\u4ee5\u786e\u5b9a\uff0c\u4ec5\u80fd\u63a8\u65ad\u5176\u80af\u5b9a\u65e9\u4e8e\u6587\u4ef6\u4fee\u6539\u65f6\u95f4\u3002\u6b64\u5916\uff0c\u8c03\u67e5\u53d1\u73b0\u6709\u4e2a\u5ba2\u6237\u66fe\u7ed9\u5c0f\u8d75\u53d1\u9001\u8fc7\u4e00\u4e2a\u79d8\u5bc6\u6587\u4ef6\uff0c\u800c\u8be5\u6587\u4ef6\u7684\u5bc6\u7801\u6b63\u662f\u8fd9\u4f4d\u5ba2\u6237\u7684\u540d\n\u5b57\u3002\u73b0\u5728\uff0c\u6446\u5728\u4f60\u9762\u524d\u7684\u4efb\u52a1\u662f\uff1a\u901a\u8fc7\u6280\u672f\u624b\u6bb5\uff0c\u627e\u5230\u6062\u590d\u6587\u4ef6\u4e2d\u7684flag,\u5e2e\u52a9\u516c\u53f8<\/code><\/pre>\n
\"\"<\/p>\n
\u9898\u76ee\u63cf\u8ff0\uff1a\u6587\u4ef6\u521b\u5efa\u65f6\u95f4\u4f5c\u4e3a\u5bc6\u7801<\/p>\n
\"\"<\/p>\n
20250624000000<\/code><\/pre>\n
\"\"<\/p>\n
\u9996\u5148R-Studio\u5de5\u5177\u6062\u590ddisk,\u5bf9business\u8fdb\u884c\u7834\u89e3\u5bc6\u7801\u4e3a\uff1a20250624155509<\/p>\n
\"\"<\/p>\n
\u5728word\u5ba2\u6237\u4fe1\u606f\u6839\u636e\u7535\u8bdd\u53f7\u627e\u5230\u738b\u5c0f\u5929\uff0c\u4f7f\u7528\u62fc\u97f3wangxiaotian\u89e3\u538b\u5f97\u5230flag<\/p>\n
\"\"<\/p>\n
\"\"<\/p>\n
flag{Mr_Wang_is_BOSS}<\/code><\/pre>\n
5.\u4e0d\u5b89\u5168\u63a5\u53e3(100pt)<\/h3>\n
\u9898\u76ee\u63cf\u8ff0\uff1a\u672c\u9898\u6ca1\u6709\u9644\u4ef6\uff01\u65e0\u9700\u4e0b\u8f7d\uff01\u67d0\u516c\u53f8\u5f00\u53d1\u8fc7\u7a0b\u4e2d\u672a\u80fd\u4e25\u683c\u9075\u5faa\u6570\u636e\u5b89\u5168\u89c4\u8303\uff0c\u5bfc\u81f4\u7cfb\u7edf\u5b58\u5728\u4e25\u91cd\u7684\u5b89\u5168\u6f0f\u6d1e\u3002\u8bf7\u83b7\u53d6card\u8fdb\u884c\u63d0\u4ea4\u3002<\/code><\/pre>\n
Admin, \u4fee\u6539\u4e3atrue<\/p>\n
\"\"<\/p>\n
\u7136\u540e\u767b\u5f55\u6846\u6ce8\u5165,sqlmap\u8dd1<\/p>\n
\"\"<\/p>\n
350500198502279894<\/code><\/pre>\n
\u6570\u636e\u5206\u6790(300pt)<\/h2>\n
\u65e5\u5fd7\u5206\u6790(100pt)<\/h3>\n
1.\u627e\u51fa\u4f01\u4e1a\u4e1a\u52a1\u9ad8\u5cf0\u65f6\u95f4\u6bb5<\/h4>\n
\u9898\u76ee\u63cf\u8ff0\uff1a\u4ee5\u5c0f\u65f6\u4e3a\u5355\u4f4d\uff0c\u5206\u6790\u4e00\u5929\u5de5\u4f5c\u65f6\u95f4(9\uff1a00-17\uff1a00)\u5185\u5404\u5c0f\u65f6\u7684\u8bbf\u95ee\u91cf\u5206\u5e03\uff0c\u627e\u51fa\u4f01\u4e1a\u4e1a\u52a1\u9ad8\u5cf0\u65f6\u95f4\u6bb5\u3002\u5176\u4e2d\uff0c\u8bbf\u95ee\u9ad8\u5cf0\u65f6\u6bb5\u5b9a\u4e49\u4e3a\uff1a\u8be5\u5c0f\u65f6\u7684\u8bbf\u95ee\u91cf\u8d85\u8fc79\uff1a00-17\uff1a00\u5168\u65f6\u6bb5\u5185\u5e73\u5747\u6bcf\u5c0f\u65f6\u8bbf\u95ee\u91cf\u7684\u65f6\u95f4\u6bb5\u3002\n\u3010\u8bc4\u6d4b\u6807\u51c6\u3011\u8bf7\u63d0\u4ea4\u8bbf\u95ee\u9ad8\u5cf0\u65f6\u6bb5\u7684\u5c0f\u65f6\u6570(24\u5c0f\u65f6\u5236)\uff0c\u591a\u4e2a\u5c0f\u65f6\u6309\u7167\u4ece\u5c0f\u5230\u5927\u6392\u5e8f\uff0c\u7528\u82f1\u6587\u9017\u53f7\u5206\u9694\u3002<\/code><\/pre>\n
import pandas as pd\nimport numpy as np\nfrom datetime import datetime\n\ndef analyze_peak_hours():\n    """\n    \u5206\u6790api_logs.csv\u6587\u4ef6\u4e2d9:00-17:00\u65f6\u95f4\u6bb5\u7684\u8bbf\u95ee\u91cf\u5206\u5e03\uff0c\n    \u8bc6\u522b\u8bbf\u95ee\u9ad8\u5cf0\u671f\uff08\u8d85\u8fc7\u5e73\u5747\u8bbf\u95ee\u91cf\u7684\u65f6\u95f4\u6bb5\uff09\n    """\n    print("\u6b63\u5728\u8bfb\u53d6api_logs.csv\u6587\u4ef6...")\n    df = pd.read_csv('\u573a\u666f\u9644\u4ef6\/api_logs.csv')\n\n    df['\u8bbf\u95ee\u65f6\u95f4'] = pd.to_datetime(df['\u8bbf\u95ee\u65f6\u95f4'])\n    df['\u5c0f\u65f6'] = df['\u8bbf\u95ee\u65f6\u95f4'].dt.hour\n\n    working_hours_df = df[(df['\u5c0f\u65f6'] >= 9) & (df['\u5c0f\u65f6'] <= 17)]\n\n    print(f"\u603b\u8bb0\u5f55\u6570: {len(df)}")\n    print(f"\u5de5\u4f5c\u65f6\u95f4\u6bb5(9:00-17:00)\u8bb0\u5f55\u6570: {len(working_hours_df)}")\n    hourly_counts = working_hours_df['\u5c0f\u65f6'].value_counts().sort_index()\n    print("\\n\u5404\u5c0f\u65f6\u8bbf\u95ee\u91cf\u7edf\u8ba1:")\n    for hour, count in hourly_counts.items():\n        print(f"{hour:2d}:00 - {count:4d} \u6b21\u8bbf\u95ee")\n    avg_hourly_visits = hourly_counts.mean()\n    print(f"\\n\u5e73\u5747\u6bcf\u5c0f\u65f6\u8bbf\u95ee\u91cf: {avg_hourly_visits:.2f}")\n    peak_hours = hourly_counts[hourly_counts > avg_hourly_visits].index.tolist()\n    peak_hours.sort()\n\n    print(f"\\n\u8bbf\u95ee\u9ad8\u5cf0\u671f\uff08\u8d85\u8fc7\u5e73\u5747\u8bbf\u95ee\u91cf {avg_hourly_visits:.2f} \u7684\u65f6\u95f4\u6bb5\uff09:")\n    for hour in peak_hours:\n        count = hourly_counts[hour]\n        print(f"{hour:2d}:00 - {count:4d} \u6b21\u8bbf\u95ee (\u8d85\u8fc7\u5e73\u5747\u503c {count - avg_hourly_visits:.1f})")\n    result = ','.join(map(str, peak_hours))\n    print(f"\\n\u3010\u7b54\u6848\u3011: {result}")\n\n    return result\n\nif __name__ == "__main__":\n    try:\n        result = analyze_peak_hours()\n        print(f"\\n\u6700\u7ec8\u7b54\u6848: {result}")\n    except Exception as e:\n        print(f"\u5206\u6790\u8fc7\u7a0b\u4e2d\u51fa\u73b0\u9519\u8bef: {e}")\n        import traceback\n        traceback.print_exc() <\/code><\/pre>\n
\"\"<\/p>\n
11,14,16<\/code><\/pre>\n
2.\u627e\u51fa\u8bbf\u95ee\u91cf\u524d\u4e09\u7684\u7cfb\u7edf<\/h4>\n
\u9898\u76ee\u63cf\u8ff0\uff1a\u7edf\u8ba1\u5404\u4e2a\u4e1a\u52a1\u7cfb\u7edf\u5728\u4e00\u5929\u4e2d\u7684\u8bbf\u95ee\u91cf\uff0c\u627e\u51fa\u8bbf\u95ee\u91cf\u524d\u4e09\u7684\u7cfb\u7edf\u3002\n\u3010\u8bc4\u6d4b\u6807\u51c6\u3011\u8bf7\u63d0\u4ea4\u8bbf\u95ee\u91cf\u524d\u4e09\u7684\u7cfb\u7edf\u540d\u79f0\uff0c\u6309\u8bbf\u95ee\u91cf\u4ece\u5927\u5230\u5c0f\uff0c\u4f7f\u7528\u82f1\u6587\u9017\u53f7\u8fde\u63a5\u3002\u8f6c\u6362\u4e3a\u5c0f\u5199md5\u63d0\u4ea4\u3002\u4e3e\u4f8b\u8bf4\u660e\uff0c\u82e5\u7edf\u8ba1\u5230\u524d\u4e09\u8bbf\u95ee\u91cf\u7684\u7cfb\u7edf\uff0c\u8be6\u60c5\u53c2\u8003\u9644\u4ef6\u8d5b\u9898\u8bf4\u660e<\/code><\/pre>\n
import pandas as pd\nimport re\nfrom urllib.parse import urlparse\nimport hashlib\n\ndef extract_system_name(url):\n    """\u4eceURL\u4e2d\u63d0\u53d6\u7cfb\u7edf\u540d\u79f0"""\n    try:\n        parsed = urlparse(url)\n        path_parts = parsed.path.strip('\/').split('\/')\n        if len(path_parts) >= 3:\n            return path_parts[2]\n    except:\n        pass\n    return None\n\ndef analyze_logs():\n    """\u5206\u6790\u65e5\u5fd7\u6587\u4ef6\uff0c\u7edf\u8ba1\u5404\u7cfb\u7edf\u8bbf\u95ee\u91cf"""\n    print("\u6b63\u5728\u8bfb\u53d6\u65e5\u5fd7\u6587\u4ef6...")\n\n    df = pd.read_csv('\u573a\u666f\u9644\u4ef6\/api_logs.csv')\n\n    print(f"\u603b\u5171\u8bfb\u53d6\u4e86 {len(df)} \u6761\u8bb0\u5f55")\n\n    df['system_name'] = df['\u8bf7\u6c42URL'].apply(extract_system_name)\n\n    system_counts = df['system_name'].value_counts()\n\n    print("\\n\u5404\u7cfb\u7edf\u8bbf\u95ee\u91cf\u7edf\u8ba1\uff1a")\n    print(system_counts)\n\n    top_three = system_counts.head(3)\n\n    print(f"\\n\u8bbf\u95ee\u91cf\u524d\u4e09\u7684\u7cfb\u7edf\uff1a")\n    for i, (system, count) in enumerate(top_three.items(), 1):\n        print(f"{i}. {system}: {count} \u6b21\u8bbf\u95ee")\n\n    top_three_names = top_three.index.tolist()\n    result_string = ','.join(top_three_names)\n\n    print(f"\\n\u7ed3\u679c\u5b57\u7b26\u4e32: {result_string}")\n\n    md5_hash = hashlib.md5(result_string.lower().encode()).hexdigest()\n\n    print(f"MD5\u54c8\u5e0c\u503c: {md5_hash}")\n\n    return md5_hash\n\nif __name__ == "__main__":\n    result = analyze_logs() <\/code><\/pre>\n
4d59f036fc099f213a1e2c71584e94f1<\/code><\/pre>\n
3.\u8bf7\u63d0\u4ea4\u68c0\u6d4b\u5230\u7684\u5f02\u5e38IP\u6570\u91cf<\/h4>\n
\u9898\u76ee\u63cf\u8ff0\uff1a\u68c0\u6d4b\u9ad8\u9891\u8bbf\u95ee\u884c\u4e3a\uff0c\u8bc6\u522b\u53ef\u80fd\u7684\u6076\u610fIP\u3002\u9ad8\u9891\u8bbf\u95ee\u884c\u4e3a\u5b9a\u4e49\u4e3a\uff1a\u540c\u4e00P\u5728\u4efb\u610f\u4e00\u4e2a\u81ea\u7136\u5206\u949f\u5185\uff08\u5373\u6bcf\u8fde\u7eed60\u79d2\u4e14\u8d77\u59cb\u65f6\u95f4\u4e3a\u6574\u70b9\u6216\u6574\u5206\uff0c\u598208\uff1a05\uff1a00-08\uff1a05\uff1a59\uff09\u5bf9\u540c=\u4e2a\u63a5\u53e3\u7684\u8bf7\u6c42\u6b21\u6570\u8d85\u8fc730\u6b21\u3002\n\u3010\u8bc4\u6d4b\u6807\u51c6\u3011\u8bf7\u63d0\u4ea4\u68c0\u6d4b\u5230\u7684\u5f02\u5e38IP\u6570\u91cf\u3002\u82e5\u68c0\u6d4b\u52303\u4e2a\u5f02\u5e38IP,\u5219\u7b54\u6848\u4e3a\uff1a3<\/code><\/pre>\n
import pandas as pd\nfrom datetime import datetime, timedelta\nfrom collections import defaultdict\nimport re\n\ndef parse_timestamp(timestamp_str):\n    """\u89e3\u6790\u65f6\u95f4\u6233\u5b57\u7b26\u4e32"""\n    return datetime.strptime(timestamp_str, '%Y-%m-%d %H:%M:%S')\n\ndef get_minute_boundary(timestamp):\n    """\u83b7\u53d6\u65f6\u95f4\u6233\u6240\u5728\u5206\u949f\u7684\u5f00\u59cb\u65f6\u95f4\uff08\u6574\u5206\u949f\u8fb9\u754c\uff09"""\n    return timestamp.replace(second=0, microsecond=0)\n\ndef analyze_high_frequency_access(csv_file):\n    """\u5206\u6790\u9ad8\u9891\u8bbf\u95ee\u884c\u4e3a"""\n    print("\u6b63\u5728\u8bfb\u53d6CSV\u6587\u4ef6...")\n\n    df = pd.read_csv(csv_file)\n    print(f"\u6570\u636e\u884c\u6570: {len(df)}")\n\n    df['\u8bbf\u95ee\u65f6\u95f4'] = pd.to_datetime(df['\u8bbf\u95ee\u65f6\u95f4'])\n\n    ip_minute_counts = defaultdict(int)\n\n    print("\u6b63\u5728\u5206\u6790\u8bbf\u95ee\u6a21\u5f0f...")\n\n    for _, row in df.iterrows():\n        timestamp = row['\u8bbf\u95ee\u65f6\u95f4']\n        ip = row['\u8bbf\u95eeIP']\n        url = row['\u8bf7\u6c42URL']\n\n        minute_boundary = get_minute_boundary(timestamp)\n\n        key = (ip, minute_boundary, url)\n        ip_minute_counts[key] += 1\n\n    abnormal_ips = set()\n\n    print("\u6b63\u5728\u68c0\u6d4b\u9ad8\u9891\u8bbf\u95ee\u884c\u4e3a...")\n\n    for (ip, minute_boundary, url), count in ip_minute_counts.items():\n        if count > 30:  \n            abnormal_ips.add(ip)\n            print(f"\u68c0\u6d4b\u5230\u5f02\u5e38IP: {ip}, \u65f6\u95f4: {minute_boundary}, URL: {url}, \u8bf7\u6c42\u6b21\u6570: {count}")\n\n    print(f"\\n\u68c0\u6d4b\u5230\u7684\u5f02\u5e38IP\u6570\u91cf: {len(abnormal_ips)}")\n    print("\u5f02\u5e38IP\u5217\u8868:")\n    for ip in sorted(abnormal_ips):\n        print(f"  {ip}")\n\n    return len(abnormal_ips)\n\nif __name__ == "__main__":\n    csv_file = "\u573a\u666f\u9644\u4ef6\/api_logs.csv"\n    result = analyze_high_frequency_access(csv_file)\n    print(f"\\n\u7b54\u6848: {result}") <\/code><\/pre>\n
\"\"<\/p>\n
25<\/code><\/pre>\n
4.\u8bf7\u63d0\u4ea4\u4f7f\u7528\u6076\u610fUser-Agent\u76841P\u6570\u91cf<\/h4>\n
\u9898\u76ee\u63cf\u8ff0\uff1a\u4e3a\u4e86\u4fdd\u969c\u6570\u636e\u5b89\u5168\uff0c\u516c\u53f8\u5185\u90e8\u4ec5\u5141\u8bb8\u6b63\u5e38\u7528\u6237\u7684\u6d4f\u89c8\u5668\u7684User-Agent\u548c\u5f00\u53d1\u4f7f\u7528\u7684postman\u3001curl requests\u7b49\u5de5\u5177\u5bf9API\u8fdb\u884c\u8bbf\u95ee\u3002\u8bf7\u68c0\u67e5User-Agent,.\u627e\u5230\u4f7f\u7528\u5176\u4ed6\u5de5\u5177\u8bbf\u95eeAPI\u7684IP\u5730\u5740\u3002\n\u3010\u8bc4\u6d4b\u6807\u51c6\u3011\u8bf7\u63d0\u4ea4\u4f7f\u7528\u6076\u610fUser-Agent\u76841P\u6570\u91cf\u3002\u82e5\u68c0\u6d4b\u523023\u4e2a\u4f7f\u7528\u6076\u610fUserAgent\u7684P,\u5219\u7b54\u6848\u4e3a\uff1a23<\/code><\/pre>\n
import pandas as pd\nimport re\n\ndef is_legitimate_user_agent(user_agent):\n    """\n    \u5224\u65adUser-Agent\u662f\u5426\u4e3a\u5408\u6cd5\u7684\uff08\u6b63\u5e38\u6d4f\u89c8\u5668\u6216\u5f00\u53d1\u5de5\u5177\uff09\n    """\n    if pd.isna(user_agent) or user_agent == '':\n        return False\n\n    user_agent = str(user_agent).lower()\n\n    browser_patterns = [\n        r'mozilla\/5\\.0', \n        r'chrome\/',       \n        r'firefox\/',      \n        r'safari\/',       \n        r'edge\/',         \n        r'opera\/',        \n        r'msie',          \n        r'trident\/',      \n    ]\n\n    dev_tools = [\n        'curl',\n        'postman',\n        'requests',\n        'python-requests',\n        'wget',\n        'httpie',\n        'insomnia',\n        'apache-httpclient',\n        'okhttp',\n        'axios',\n        'fetch',\n    ]\n\n    for tool in dev_tools:\n        if tool in user_agent:\n            return True\n\n    for pattern in browser_patterns:\n        if re.search(pattern, user_agent):\n            return True\n\n    return False\n\ndef analyze_api_logs(csv_file):\n    """\n    \u5206\u6790API\u65e5\u5fd7\u6587\u4ef6\uff0c\u627e\u51fa\u4f7f\u7528\u6076\u610fUser-Agent\u7684IP\u5730\u5740\n    """\n    print("\u6b63\u5728\u8bfb\u53d6CSV\u6587\u4ef6...")\n\n    df = pd.read_csv(csv_file)\n\n    print(f"\u603b\u8bb0\u5f55\u6570: {len(df)}")\n    print(f"\u5217\u540d: {list(df.columns)}")\n\n    if 'user-agent' not in df.columns:\n        print("\u9519\u8bef\uff1a\u672a\u627e\u5230user-agent\u5217")\n        return\n\n    malicious_ips = set()\n    legitimate_count = 0\n    malicious_count = 0\n\n    print("\\n\u5f00\u59cb\u5206\u6790User-Agent")\n\n    for index, row in df.iterrows():\n        user_agent = row['user-agent']\n        ip = row['\u8bbf\u95eeIP']\n\n        if is_legitimate_user_agent(user_agent):\n            legitimate_count += 1\n        else:\n            malicious_count += 1\n            malicious_ips.add(ip)\n\n        if (index + 1) % 1000 == 0:\n            print(f"\u5df2\u5904\u7406 {index + 1} \u6761\u8bb0\u5f55...")\n\n    print(f"\\n\u5206\u6790\u5b8c\u6210!")\n    print(f"\u5408\u6cd5User-Agent\u8bb0\u5f55\u6570: {legitimate_count}")\n    print(f"\u6076\u610fUser-Agent\u8bb0\u5f55\u6570: {malicious_count}")\n    print(f"\u4f7f\u7528\u6076\u610fUser-Agent\u7684IP\u6570\u91cf: {len(malicious_ips)}")\n\n    print(f"\\n\u6076\u610fIP\u5730\u5740\u5217\u8868:")\n    for ip in sorted(malicious_ips):\n        print(f"  {ip}")\n\n    return len(malicious_ips)\n\nif __name__ == "__main__":\n    csv_file = "\u573a\u666f\u9644\u4ef6\/api_logs.csv"\n    result = analyze_api_logs(csv_file)\n    print(f"\\n\u7b54\u6848: {result}") <\/code><\/pre>\n
\"\"<\/p>\n
63<\/code><\/pre>\n
\u6d41\u91cf\u5206\u6790(200pt)<\/h3>\n
\u6570\u636e\u5206\u6790 1<\/h4>\n
\u9898\u76ee\u63cf\u8ff0\uff1a\u5206\u6790\u63d0\u4f9b\u7684\u6d41\u91cf\u6587\u4ef6\uff0c\u627e\u5230\u54ea\u4e9b\u7814\u7a76\u6210\u679c\u53ea\u4e0a\u4f20\u4e86\u6210\u679c\u6587\u6863\uff0c\u4f46\u662f\u6ca1\u6709\u4e0a\u4f20\u54c8\u5e0c\u6587\u4ef6\u3002\n\u3010\u8bc4\u6d4b\u6807\u51c6\u3011\u5c06\u627e\u5230\u7684\u7814\u7a76\u6210\u679c\u7f16\u53f7\u6309\u7167\u6570\u5b57\u90e8\u5206\u4ece\u5c0f\u5230\u5927\u8fdb\u884c\u6392\u5e8f\uff0c\u4f7f\u7528\u201c\u201d\u8fde\u63a5\uff0c\u6700\u540e\u8f6c\u4e3a32\u4f4d\u5c0f\u5199md5\u63d0\u4ea4\uff0c\u6ce8\u610f\u8ba1\u7b97md5\u65f6\uff0c\u8f93\u5165\u5b57\u7b26\u5e94\u4e3aUTF-8\u7f16\u7801\u4e14\u884c\u5c3e\u4e0d\u5e26\u6362\u884c\u7b26\uff0c\u8be6\u60c5\u53c2\u8003\u9644\u4ef6\u8d5b\u9898\u8bf4\u660e\u3002<\/code><\/pre>\n
\u4f7f\u7528CTF-NETA \u5206\u6790\u6d41\u91cf\u5305\uff0c\u63d0\u53d6\u6240\u6709\u6587\u4ef6\u540e\u624b\u52a8\u67e5\u627e\u6ca1\u6709sha256\u7684\u6587\u4ef6\uff0c\u7136\u540e\u6309\u7167\u8981\u6c42md5\u52a0\u5bc6\u5373\u53ef\u3002 <\/p>\n
\"\"<\/p>\n
RES115756-RES157171-RES266128-RES317032-RES326054-RES364261-RES509894-RES554600-RES618293-RES636474-RES750741-RES794870-RES884672<\/code><\/pre>\n
\"\"<\/p>\n
\u6570\u636e\u5206\u6790 2<\/h4>\n
\u9898\u76ee\u63cf\u8ff0\uff1a\u79d1\u7814\u6210\u679c\u5728\u63d0\u4ea4\u5230\u5e73\u53f0\u4e4b\u524d\uff0c\u90fd\u4f1a\u8fdb\u884c\u5ba1\u6838\u5e76\u8bb0\u5f55\u54c8\u5e0c\uff0c\u73b0\u5728\u68c0\u67e5\u53d1\u73b0\u6709\u4e9b\u6210\u679c\u4e0a\u4f20\u7684\u548c\u63d0\u4ea4\u5ba1\u67e5\u7684\u6587\u4ef6\u5b58\u5728\u533a\u522b\uff0c\u8bf7\u4f7f\u7528\u63d0\u4f9b\u7684aes256\u5bc6\u94a5\u89e3\u5bc6\u6587\u4ef6\uff0c\u5e76\u6821\u9a8c\u54c8\u5e0c\uff0c\u627e\u5230\u54ea\u4e9b\u79d1\u7814\u6210\u679c\u5b58\u5728\u533a\u522b\u3002\n\u3010\u8bc4\u6d4b\u6807\u51c6\u3011\u5c06\u627e\u5230\u7684\u7814\u7a76\u6210\u679c\u7f16\u53f7\u6309\u7167\u6570\u5b57\u90e8\u5206\u4ece\u5c0f\u5230\u5927\u8fdb\u884c\u6392\u5e8f\uff0c\u4f7f\u7528\u201c\u201d\u8fde\u63a5\uff0c\u6700\u540e\u8f6c\u4e3a32\u4f4d\u5c0f\u5199md5\u63d0\u4ea4\uff0c\u6ce8\u610f\u8ba1\u7b97md5\u65f6\uff0c\u8f93\u5165\u5b57\u7b26\u5e94\u4e3aUTF-8\u7f16\u7801\u4e14\u884c\u5c3e\u4e0d\u5e26\u6362\u884c\u7b26\uff0c\u8be6\u60c5\u53c2\u8003\u9644\u4ef6\u8d5b\u9898\u8bf4\u660e\u3002<\/code><\/pre>\n
no<\/strong><\/p>\n
\u6570\u636e\u5206\u6790 3<\/h4>\n
\u8fd1\u671f\u5728\u5e73\u53f0\u4e0a\u53d1\u73b0\u4e86\u4e00\u4e9b.eml\u540e\u7f00\u7684\u5783\u573e\u90ae\u4ef6\uff0c\u68c0\u67e5\u6d41\u91cf\uff0c\u627e\u5230\u767b\u5f55\u5e76\u4e0a\u4f20\u8fd9\u4e9beml\u5783\u573e\u90ae\u4ef6\u7684\u7528\u6237\u540d\u6570\u91cf\u3002\n\u3010\u8bc4\u6d4b\u6807\u51c6\u63d0\u4ea4\u4e0a\u4f20\u5783\u573e\u6587\u4ef6\u7684\u7528\u6237\u540d\u6570\u91cf\u3002\u82e5\u627e\u5230\u4e8624\u4e2a\u7528\u6237\u540d\u4e0a\u4f20\u5783\u573e\u6587\u4ef6\uff0c\u5219\u63d0\u4ea4\u7b54\u6848\u4e3a\uff1a24<\/code><\/pre>\n
\u4f7f\u7528CTF-NETA\u5de5\u5177\u5bf9\u6587\u4ef6\u8fdb\u884c\u626b\u63cf\uff0c\u641c\u7d22\u6240\u6709\u5e26\u6709eml\u5173\u952e\u8bcd\u7684\u6d41\u91cf\u4e0a\u4e0b\u6587\uff0c \u624b\u52a8\u67e5\u627e\u6240\u6709\u7528\u6237\u540d\uff1a<\/p>\n
\"\"<\/p>\n
wangyulan\nleiyu963\nsunrui573\nmaguilan685\nliaoming724\nleiyu963\nsunhui\nmaguilan685\nleiyu963\nsunhui\nwangyulan\nleiyu963\nwangyulan\nleiyu963\nleiyu963\nliaoming724\nwangyulan\nmaguilan685\nsunhui\nsunrui573\nlijuan863\nsunhui\nleiyu963\nlijuan863\nsunhui\nlijuan863\nmaguilan685\nsunhui\nsunhui\nmaguilan685\nmaguilan685<\/code><\/pre>\n
\"\"<\/p>\n