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本文介绍了SSL/TLS重协商，即在已建立的连接上重新协商加密参数。阐述了重协商的场景、过程，指出存在SSL/TLS重协商漏洞和资源消耗问题。还说明了检测服务器是否支持重协商的方法，以及服务器端和客户端防范重协商风险的措施，并给出相关案例。
未经许可，不得转载。
本文仅供学习交流使用，不得进行非法渗透测试，笔者不承担任何责任。
SSL/TLS 重协商（Renegotiation） 是指在已经建立的 SSL/TLS 连接上，客户端和服务器重新协商加密参数，以更新安全设置或重新进行身份验证。它可以由客户端或服务器发起。
1、更强身份验证
如最初未要求，但连接后，服务器要求客户端提供 SSL 证书。
2、更新加密参数
提高安全性，比如切换更强的加密算法或密钥。
3、会话恢复
部分服务器使用重协商机制来恢复会话，而不是重新建立连接。
重协商的过程与初始 SSL/TLS 握手类似，但发生在已有连接之上：
1、发起重协商：客户端或服务器发送 ClientHello 或 HelloRequest。
2、重新握手：双方重新交换 ClientHello、ServerHello 等信息，并重新协商密钥。
3、建立新的会话参数：完成密钥交换后，新的安全设置生效，但 TCP 连接仍然保持不变。
AI 编程助手新玩法：用 Claude Code 一键调用国产大模型（DeepSeek/Kimi/GLM 对比）
本文深入评测了Claude Code与三大国产大模型（DeepSeek、Kimi、智谱GLM）的集成效果，详细对比了它们在代码生成质量、响应速度和特殊场景支持度等方面的差异。通过实测数据和实战技巧，帮助开发者选择最适合的AI编程助手组合，提升开发效率。
STM32开发实战--解决STM32CubeMX生成代码时钟初始化卡死问题
本文详细分析了STM32CubeMX生成代码在时钟初始化阶段卡死的问题，提供了多种解决方案，包括简单修改和稳健的等待机制。通过深入探讨HAL库的时钟初始化陷阱及硬件层面的根本原因，帮助开发者快速定位并解决STM32开发中的常见问题。
海康威视web控件开发+vue结合做网页开发
需求:vue的项目,需要在页面展示出海康的硬盘录像机连接的摄像头的实时监控画面以及回放功能. 准备:web开发包(包含插件以及demo,在海康官网即可下载32位64位的都有.那些需要积分下载的博主良心不疼么.) 点击这里下载:链接: https://pan.baidu.com/s/12FrbycWyWWyiRJH5Yd1tPA 提取码: gam5 开始开发: 1.安装demo目录下的WebComponentsKit.exe插件(根据自己的浏览器以及系统选择32位还是64位的插件) 2.打.
【Python】-- 趣味代码 - 发牌游戏
Python -- 趣味代码 - 发牌游戏
YOLO26 改进 | （教师：YOLOv8L，学生：YOLO26） | 知识蒸馏 (Knowledge Distillation)
本文介绍了基于YOLO26框架的知识蒸馏实战方法，通过教师模型指导学生模型训练，在不增加推理成本的情况下提升模型精度。文章详细阐述了基于响应的知识蒸馏实现方案，通过最小化教师与学生模型预测Logits间的KL散度实现知识迁移。核心实现包括：1) 创建DistillationModel包装器类，同时运行师生模型；2) 自定义DistillationLoss，在原检测损失基础上增加KL散度损失。该方法适用于希望保持YOLO26轻量级特性同时获得接近大模型精度的场景，文中提供了完整的代码实现方案。
LDRA Testbed软件静态分析_常见问题及处理
通过前面的几篇文章，LDRA Testbed软件静态分析的内容基本讲解完了，本篇文章将针对使用Testbed进行软件静态分析的常见问题进行介绍。 到此为止，LDRA Testbed软件静态分析的内容已经全部讲完，如果有问题，欢迎私信提问（不经常登录，回复可能不及时，见谅）。下一篇文章将开始讲解如何使用Testbed进行单元测试。
基于OpenCV实现暗通道先验去雾算法及改进，效果惊人
深度技术文，前方高能
YOLOv8/YOLOv11目标检测系统与多目标跟踪检测系统，使用PySide6搭建目标检测系统，支持目标检测、分割、姿势估计、旋转框，用户端系统+后台管理系统可用于大论文凑工作量或毕设必备
YOLOv8/YOLOv11目标检测系统与多目标跟踪检测系统，目标检测、分割、姿势估计、obb（旋转框），用户端系统+后台管理系统开发实战，可用于大论文凑工作量或毕设必备，全网最详细教程，系统支持 目标检测、分割、姿势估计、obb（旋转框）等模型进来检测，是一个通用的目标跟踪与目标检测系统，适配多种场景如：鱼苗检测与跟踪、行人多目标跟踪、车辆多目标跟踪、牛只行为识别与跟踪、口罩佩戴检测、安全帽佩戴检测、火灾检测等。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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. 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');```[](https://bitdeli.com/free "Bitdeli Badge")
lwip-2.0.3
lwip是瑞典计算机科学院(SICS)的Adam Dunkels 开发的一个小型开源的TCP/IP协议栈。实现的重点是在保持TCP协议主要功能的基础上减少对RAM 的占用。
jre9下载 jdk9
(Java SE Runtime Environment 9.0) 9.0u175 官方最新版 【x86|x64】
IEM模型，地表粗糙度模拟
利用微波反演土壤水分时候，利用IEM模型可以实现对随机地表的模拟
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