Demystifying Intelligent Multimode Security Systems: An Edge-To-Cloud Cybersecurity Solutions Guide

Use this practical guide to understand the concepts behind Intelligent Multi-modal Security Systems (IMSS) and how to implement security within an IMSS system to improve the robustness of the devices and of the end-to-end solution.

There are nearly half a million active IMSS cameras globally, with over 100 million added annually. These cameras are used across enterprises (companies, traffic monitoring, driver enforcement, etc.), in peoples' homes, on mobile devices (drones, on-vehicle, etc.), and are worn on the body.

IMSS systems with a camera and network video recorder for storage are becoming the normal infrastructure for capturing, storing, and transmitting video content (sometimes up to 100 streams) in a secure manner and while protecting privacy.

Military, aerospace, and government entities are also embracing digital security and surveillance. IMSS content serves as evidence in courts of law.

Security within all of these types of IMSS systems needs to be bolstered by leveraging Intel hardware and software as the last line of defense, and this book provides you with best practices and solutions for maximizing security in your system implementation.

What You Will Learn

• Review the relevant technologies in a surveillance system
• Define and dissect the data pipeline with a focus on key criteria and understand the mapping of this pipeline to Intel hardware blocks
• Optimize the partition and future-proof it with security and manageability
• Understand threat modeling terminology, the assets pertinent to DSS, and emerging threats, and learn how to mitigate these threats using Intel hardware and software
• Understand the unique risks and threats to the intelligence in IMSS (machine learning training and inferencing, regulations, and standards) and explore the solution space for mitigations to these threats
• Sample applications illustrate how to design in security for several types of IMSS.--
• Explore ways to keep both yourself and your systems up to date in a rapidly changing technology and threat environment

Who This Book Is For

Surveillance system designers, integrators, and consultants; professional systems, hardware, and software designers who design, recommend, or integrate surveillance systems; security system integrators; video analytics engineers; agencies that write RFPs and/or RFIs; government, police, and security agencies; and corporate security divisions

使用這本實用指南，了解智能多模式安全系統（IMSS）背後的概念，以及如何在IMSS系統中實施安全措施，以提高設備和端到端解決方案的穩健性。

全球有近50萬個活躍的IMSS攝像頭，每年新增超過1億個。這些攝像頭被廣泛應用於企業（公司、交通監控、駕駛執法等）、人們的家中、移動設備（無人機、車載等）以及身上佩戴。

具有攝像頭和網絡錄像機的IMSS系統正在成為捕捉、存儲和傳輸視頻內容（有時高達100個流）的常規基礎設施，並且能夠以安全的方式保護隱私。

軍事、航空航天和政府機構也在推行數字安全和監控。IMSS內容在法庭上作為證據。

所有這些類型的IMSS系統中的安全性都需要通過利用英特爾硬件和軟件作為最後一道防線來加強，本書為您提供了在系統實施中最大化安全性的最佳實踐和解決方案。

您將學到什麼：

- 檢視監控系統中相關技術
- 定義並分析數據流水線，重點關注關鍵標準，並了解如何將此流水線映射到英特爾硬件模塊
- 通過安全性和可管理性優化分區並未其未來提供保護
- 了解威脅建模術語，與DSS相關的資產以及新興威脅，並學習如何使用英特爾硬件和軟件來減輕這些威脅
- 了解IMSS中智能性的獨特風險和威脅（機器學習訓練和推理、法規和標準），並探索解決這些威脅的方法
- 通過示例應用程序演示如何為多種類型的IMSS設計安全性
- 探索在快速變化的技術和威脅環境中保持自己和系統更新的方法

本書適合對監控系統進行設計、集成和諮詢的人員；專業系統、硬件和軟件設計師，他們設計、推薦或集成監控系統；安全系統集成商；視頻分析工程師；撰寫RFP和/或RFI的機構；政府、警察和安全機構；以及企業安全部門。

Lawrence Booth has been a systems architect and a systems-on-silicon architect focused on imaging and media-related processing for more than 30 years. After three years in secure video gateway cybersecurity architecture, he joined Intel's Internet of Things group, returning to his greatest interest--vision systems.

Dr. Werner Metz is a system architect with over 30 years of experience in architecting, developing, and implementing digital imaging systems. He has contributed at the level of image sensor architecture and design, conventional and deep learning imaging algorithms, digital image processor architecture, and analog image signal processor design. He has architected a wide range of consumer, commercial, and industrial imaging systems spanning visible, IR, thermal, and UV wavelengths for both human viewing and computer vision. He is currently responsible for the E2E video architecture at Intel, spanning camera to gateway to data center, with an emphasis on edge devices.

Sunil Cheruvu is Chief IoT Security Architect in the Internet of Things group at Intel Corporation. He has over 27 years of experience in architecting complex systems involving HW/FW/SW on multiple architectures, including Intel, ARM, and MIPS/PowerPC. At Intel, he leads security across all of the IoT vertical domains and he was the Content Protection and Trusted Data Path System Architect (end-to-end premium content protection within an SoC). He is the subject matter expert for IoT security across Intel and outside of Intel. At Microsoft, as a SW design engineer, he was the tech lead for vehicle bus networking stacks, threat modeling, and mitigations in the Windows Mobile for Automotive (WMfA) platform. At 3com and Conexant, he implemented the code for baseline privacy security in DOCSIS-compliant cable modems.

Lawrence Booth是一位系統架構師和專注於影像和媒體相關處理的系統晶片架構師，擁有超過30年的經驗。在從事安全視訊閘道的資訊安全架構工作三年後，他加入了英特爾的物聯網部門，回歸他最大的興趣-視覺系統。

Werner Metz博士是一位系統架構師，擁有超過30年的數位影像系統架構、開發和實施經驗。他在影像感測器架構和設計、傳統和深度學習影像演算法、數位影像處理器架構和類比影像信號處理器設計等方面做出了貢獻。他架構了各種消費者、商業和工業影像系統，涵蓋可見光、紅外線、熱像和紫外線波長，既適用於人類觀看，也適用於計算機視覺。他目前負責英特爾的端到端視訊架構，涵蓋攝像頭、閘道和數據中心，並強調邊緣設備。

Sunil Cheruvu是英特爾公司物聯網部門的首席物聯網安全架構師。他在涉及多種架構的硬體/軟體系統架構方面擁有超過27年的經驗，包括英特爾、ARM和MIPS/PowerPC。在英特爾，他負責物聯網垂直領域的安全工作，並擔任內部和外部的物聯網安全專家。在微軟，作為軟體設計工程師，他是Windows Mobile for Automotive（WMfA）平台上車輛匯流排網絡堆疊、威脅建模和緩解措施的技術負責人。在3com和Conexant，他實現了DOCSIS兼容的有線數據機中基本隱私安全的程式碼。