《物聯網安全與深度學習技術》是2022年電子工業出版社出版的圖書，作者是吳巍。
基本介紹
- 書名： 物聯網安全與深度學習技術
- 作者：吳巍
- 出版社： 電子工業出版社
- 出版時間：2022年
- 頁數：204 頁
- 定價：88 元
- 開本：16 開
- ISBN： 9787121428913
內容簡介,圖書目錄,
內容簡介
本書從物聯網技術發展現狀、體系架構及演進趨勢入手，設計物聯網安全架構；在對物聯網典型安全事件進行回顧的基礎上，梳理物聯網安全問題分類，提煉安全威脅和安全需求，提出物聯網安全體系框架，引出物聯網身份安全的重要性；深入介紹物聯網安全認證技術，從傳統身份認證機制、物聯網身份認證方法入手，對基於生物特徵的物聯網身份認證方法和基於深度學習的聲紋識別技術進行詳細描述；介紹了生物特徵識別技術中能夠實用化、商業化的深度學習算法，並對典型的深度學習框架和平台進行了分析。
圖書目錄
第1 章 物聯網技術基礎···········································································.001
1.1 物聯網發展現狀··········································································.001
1.1.1 美國·················································································.001
1.1.2 歐盟·················································································.002
1.1.3 日本·················································································.002
1.1.4 韓國·················································································.003
1.1.5 中國·················································································.003
1.2 物聯網體系架構··········································································.004
1.2.1 感知層·············································································.005
1.2.2 網路層·············································································.005
1.2.3 套用層·············································································.006
1.3 物聯網信息的三大特性·······························································.007
1.3.1 高敏感性··········································································.007
1.3.2 高可靠性··········································································.007
1.3.3 高關聯性··········································································.007
1.4 物聯網體系架構的發展·······························································.008
1.5 小結·······························································································011
第2 章 物聯網安全架構··········································································.012
2.1 引言·····························································································.012
2.2 物聯網典型安全事件···································································.012
2.2.1 事件回顧··········································································.012
2.2.2 事件分析··········································································.016
2.3 物聯網安全問題分類···································································.017
2.3.1 網際網路引入的安全問題···················································.017
2.3.2 物聯網場景下的網際網路“安全”問題····························.017
2.3.3 物聯網引入的安全問題···················································.017
2.3.4 物聯網自身的安全問題···················································.018
2.4 物聯網安全威脅分析···································································.018
2.4.1 感知層安全威脅······························································.019
2.4.2 網路層安全威脅······························································.020
2.4.3 套用層安全威脅······························································.022
2.5 物聯網安全需求分析···································································.023
2.5.1 縱橫聯動的一體化安全保障支撐····································.024
2.5.2 感知層感測器設備的身份鑑別與數據防護····················.026
2.5.3 網路層異構網路規模化安全互聯與全網統一監管·········.027
2.5.4 套用層數據多域安全共享···············································.028
2.6 物聯網安全體系框架···································································.029
2.6.1 技術體系··········································································.029
2.6.2 物聯網系統的安全信息流···············································.036
2.7 小結·····························································································.037
第3 章 物聯網安全認證技術···································································.039
3.1 引言·····························································································.039
3.2 身份認證方式··············································································.040
3.2.1 基於秘密信息的認證方式···············································.040
3.2.2 基於信物的認證方式·······················································.041
3.2.3 基於密鑰的認證方式·······················································.041
3.2.4 基於生物特徵的認證方式···············································.042
3.3 物聯網身份認證的特點·······························································.043
3.3.1 輕量級·············································································.043
3.3.2 非對稱·············································································.043
3.3.3 複雜性·············································································.043
3.4 幾種物聯網身份認證方式···························································.044
3.4.1 基於RFID 的物聯網身份認證方式·································.044
3.4.2 基於感測網路的物聯網認證方式····································.044
3.4.3 基於藍牙的感知網路認證方式·······································.045
3.4.4 基於生物特徵識別的認證方式·······································.045
3.5 基於生物特徵的物聯網身份認證方法········································.047
3.5.1 生物特徵身份認證流程···················································.047
3.5.2 指紋識別··········································································.049
3.5.3 人臉識別··········································································.049
3.5.4 虹膜識別··········································································.051
3.5.5 指靜脈識別······································································.051
3.5.6 聲紋識別··········································································.052
3.6 基於深度學習的聲紋識別技術····················································.053
3.6.1 概述·················································································.053
3.6.2 聲紋識別的工作原理·······················································.054
3.6.3 聲紋識別的流程······························································.055
3.6.4 聲紋識別技術的三次突破···············································.059
3.6.5 基於深度學習的典型聲紋識別算法································.060
3.6.6 聲紋識別套用趨勢··························································.063
3.7 小結·····························································································.066
本章參考文獻······················································································.066
第4 章 卷積神經網路技術······································································.068
4.1 卷積運算······················································································.069
4.2 動機·····························································································.072
4.3 池化·····························································································.077
4.4 將卷積與池化作為一個無限強的先驗········································.082
4.5 基本卷積函式的變體···································································.083
4.6 結構化輸出··················································································.093
4.7 數據類型······················································································.094
4.8 高效的卷積算法··········································································.095
4.9 隨機或無監督的特徵···································································.096
4.10 小結···························································································.097
本章參考文獻······················································································.098
第5 章 序列建模：循環和遞歸網路·······················································.101
5.1 展開計算圖··················································································.102
5.2 RNN·····························································································.105
5.2.1 Teacher Forcing 和輸出循環網路····································.109
5.2.2 計算RNN 的梯度······························································111
5.2.3 作為有向圖模型的循環網路·············································113
5.2.4 基於上下文的RNN 序列建模···········································117
5.3 雙向RNN ······················································································119
5.4 基於編碼-解碼的序列到序列架構···············································.121
5.5 深度RNN ····················································································.123
5.6 遞歸神經網路··············································································.124
5.7 長期依賴的挑戰··········································································.126
5.8 回聲狀態網路··············································································.128
5.9 滲漏單元和其他多時間尺度的策略············································.130
5.9.1 時間維度的跳躍連線·······················································.130
5.9.2 滲漏單元和一系列時間尺度···········································.131
5.9.3 刪除連線··········································································.131
5.10 長短期記憶和其他門控RNN ····················································.132
5.10.1 長短期記憶····································································.133
5.10.2 其他門控RNN·······························································.135
5.11 最佳化長期依賴············································································.136
5.11.1 截斷梯度········································································.136
5.11.2 引導信息流的正則化·····················································.138
5.12 外顯記憶····················································································.139
5.13 小結···························································································.142
本章參考文獻······················································································.142
第6 章 深度學習框架和平台的分析與對比············································.148
6.1 概述·····························································································.148
6.2 深度學習框架··············································································.151
6.2.1 TensorFlow·······································································.152
6.2.2 Caffe ················································································.156
6.2.3 PyTorch ············································································.162
6.2.4 CNTK···············································································.164
6.2.5 MXNet ·············································································.166
6.3 深度學習框架的分析與對比·······················································.169
6.3.1 總體分析··········································································.169
6.3.2 深度學習框架的對比·······················································.170
6.3.3 深度學習框架對硬體的利用情況····································.178
6.4 深度學習平台··············································································.180
6.4.1 華為深度學習服務DLS ··················································.180
6.4.2 阿里深度學習開發平台X-DeepLearning ························.184
6.4.3 百度深度學習開發平台PAddle·······································.189
6.4.4 幾種平台的對比······························································.191
6.5 小結·····························································································.192
