【JS逆向】XX云歌曲评论【AES+RSA】加密

JS逆向入门教程p1 浏览器设置 常用工具 http编程语言(网络apidavaScript)逆向(js逆向 安卓逆向 ios逆向 PC逆向)调试图像识别下载城南Post助手、fiddler、wireshark(鲨鱼)抓包工具;通过进程抓包f12配置推荐,实验功能全部关闭时间线上的分配检测:js的运行时间线关闭时间线:V8(JavaScript引擎) node.js=v8+js+扩展先看值,再看对象(当看不到值,可以尝试查看对象)程序 栈 (代码的运行流程)a方法调用b方法ba。 阅读详情

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学者应洁身自好,切勿做出违法的事情,旨在提供逆向思路。

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aHR0cHM6Ly9tdXNpYy4xNjMuY29tLyMvc29uZz9pZD0xOTAxMDQ5Njcx

问题分析:

请求URL, 打开F12查看数据请求接口:

  

歌曲评论接口找到,但是请求参数是加密的,并且每次刷新页面和翻页,参数都在不断变化,猜测可能是加入了时间戳,具体还需分析后得知。

逆向分析:

全局搜索 ”encSecKey“ 关键字,在出现的结果中有一处很可疑:

 点进去看下:

猜测一下,这里很有可能就是加密所在,下个断,调试下:

断点停在这里,说明此处即为加密逻辑所在,并且根据调试的数据可以看出, 最终结果为拼接而成,这就很好办了,只要找到加密入口以及参数,就可以得出加密后的数据,”params“ 和 ”encSecKey“ 的结果都是从”bVj0x“ 这个结果中获取的,而”bVj0x“ 是调用了一些加密函数来处理原始数据的,继续看一下:

”bVj0x“ 调用的是window.asrsea 函数,真实调用的是函数d:

 函数d传入了一些参数,挨个分析下参数都是什么:

i9b  -- 是一个json对象,最后序列化为json字符串:

cursor---当前时间戳

pageSize、offset ---- 页面显示评论的数量

orderType ---- 评论排序方式

pageNo--- 页码

rid、threadId ---- R_SO_4_(歌曲id),可以在url中找到

第二、三、四个参数调用的都是bsP6J 函数,其中第三个参数传进去的是Xk6e.md参数,Xk6e.md是事先定义好的变量,往上翻就能找到:

 接着,跟进去bsP6J函数看下:

麒麟银河操作系统V10下ffmpeg的完整部署指南(兼容Linux系统) 本文提供在麒麟银河操作系统V10上从源码编译部署FFmpeg的完整指南,涵盖环境准备、核心依赖库(如x264)编译、FFmpeg本体配置安装、系统集成及常见问题排查。步骤详细,同样适用于Ubuntu、CentOS等主流Linux发行版,帮助用户打造功能强大的多媒体处理工具链。 阅读详情

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深度学习工具包 DeepLearningToolbox

深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. 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nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

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