Support for U2020 X-Series USB Power Sensors Delta Match Calibration required on ALL N5231A, N5232A, and N5239A models. Unguided TRL Cal allowed on all PNA 4-port models Noise Parameters

Nmap Cookbook The Fat-free Guide to Network Scanning.pdf

Nmap Network Scanning官方资源完全版 高清PDF 免积分网盘下载(我级别不够上传大文件) 为开源中国贡献力量！

Autonomous Cyber Deception: Reasoning, Adaptive Planning, and Evaluation of HoneyThings By 作者: Ehab Al-Shaer – Jinpeng Wei – Kevin W. Hamlen – Cliff Wang ISBN-10 书号: 3030021092 ISBN-13 书号: 9783030021092 Edition 版本: 1st ed. 2019 出版日期: 2019-01-02 pages 页数: (235 ) $84.99 This textbook surveys the knowledge base in automated and resilient cyber deception. It features four major parts: cyber deception reasoning frameworks, dynamic decision-making for cyber deception, network-based deception, and malware deception. An important distinguishing characteristic of this book is its inclusion of student exercises at the end of each chapter. Exercises include technical problems, short-answer discussion questions, or hands-on lab exercises, organized at a range of difficulties from easy to advanced,. This is a useful textbook for a wide range of classes and degree levels within the security arena and other related topics. It’s also suitable for researchers and practitioners with a variety of cyber security backgrounds from novice to experienced. Cover Front Matter Part I. Cyber Deception Reasoning Frameworks 1. Using Deep Learning to Generate Relational HoneyData 2. Towards Intelligent Cyber Deception Systems 3. Honeypot Deception Tactics Part II. Dynamic Decision-Making for Cyber Deception 4. Modeling and Analysis of Deception Games Based on Hypergame Theory 5. Dynamic Bayesian Games for Adversarial and Defensive Cyber Deception Part III. Network-Based Deception 6. CONCEAL:A Strategy Composition for Resilient Cyber Deception: Framework, Metrics, and Deployment 7. WetShifter:A Comprehensive Multi-Dimensional Network Obfuscation and Deception Solution 8. Deception-Enhanced Threat Sensing for Resilient Intrusion Detection 9. HONEYSCOPE: IoT Device Protection with Deceptive Network Views Part IV. Malware Deception 10. gExtractor: Automated Extraction of Malware Deception Parameters for Autononous Cyber Deception 11. Malware Deception with Automatic Analysis and Generation of HoneyResource

纳奥 基于 for Unity的神经网络Ambien遮挡。 产品特点 双边模糊（具有正常和深度阈值） 时间平滑 下采样和双边上采样 此限制使用CommandBuffers写入GBuffer，从而实现仅Ambien模式。 该项目包括Unity的传统电影效果和进行比较。 要求 Unity 2017.1.0f3及更高版本 DirectX 11 屏幕截图 使用NNAO： 没有NNAO： 仅NNAO： 使用NNAO： 没有NNAO： 仅NNAO：

官网已经下载不到了。这个亲测可用。适用于hp usb network print adapter

TensorLayer中的DCGAN 这是的TensorLayer实现。 寻找文本到图像合成？ :NEW_button: :fire: 2019年5月：我们只是更新了此项目以支持TF2和TL2。 请享用！ :NEW_button: :fire: 2019年5月：该项目被选为TL项目的默认模板。 先决条件 的Python3.5 3.6 TensorFlow == 2.0.0a0 pip3 install tensorflow-gpu==2.0.0a0 TensorLayer = 2.1.0 pip3 install tensorlayer==2.1.0 用法 首先，将对齐的面部图像从或到data文件夹。 其次，训练GAN： $ python

Certified Pre-sales Associate IP Network Datacom认证题.docx

network ：friends，money and sbutes的教材。

本书详细介绍了Microsoft Windows系统下的网络编程机制，深入浅出的讲述了基本网络的网络协议以及利用Windows API进行网络编程的方法，通俗易懂。