Smart Home and Industrial IoT Devices.

Format:

Book

Author/Creator:

Bhardwaj, Akashdeep.

Contributor:

Author.

Language:

English

Subjects (All):

Internet of things.

Computer security.

Physical Description:

1 online resource (257 pages)

Edition:

1st ed.

Place of Publication:

Sharjah : Bentham Science Publishers, 2024.

Summary:

Smart Home and Industrial IoT Devices: Critical Perspectives on Cyber Threats, Frameworks and Protocols provides an in-depth examination of the Internet of Things (IoT) and its profound impact on smart homes and industrial systems. The book begins by exploring the significance of IoT in smart homes, followed by an analysis of emerging cyber threats targeting smart homes and cyber-physical systems. It presents AI and machine learning-based frameworks for monitoring water quality and managing irrigation in agriculture, highlighting their role in IoT ecosystems. The text also discusses a framework to mitigate cyber-attacks on robotic systems and introduces a multinomial naive Bayesian classifier for analyzing smart IoT devices. Dataflow analysis and modeling experiments are detailed, along with a comparison of IoT communication protocols using anomaly detection and security assessment. The book concludes with a discussion on efficient, lightweight intrusion detection systems and a unique taxonomy for IoT frameworks. This book is ideal for students, researchers, and professionals seeking to understand and secure IoT environments.

Contents:

Cover

Title

Copyright

End User License Agreement

Contents

Preface

Significance of IoT for Smart Homes and Cities

INTRODUCTION

Contributions of this chapter

Problem Statement

Scope

LITERATURE SURVEY

UNIQUE TAXONOMY AND INNOVATION

Fog Security

Fog Design

Fog Node Management

Energy Management

Capacity Management

EXPERIMENTAL SETUP

RESULTS OBTAINED

INNOVATION AND USE OF BLOCKCHAIN FOR IOT

NOVELTY OF THIS CHAPTER

CONCLUSION

Computing and IoT devices

REFERENCES

New Age Attacks on Smart Homes and Cyber-Physical Systems

LITERATURE REVIEW

SUPPLY CHAIN VULNERABILITIES

SolarWinds Supply Chain Attack

Kaseya VSA Supply Chain Attack

AI-DRIVEN THREATS

Deepfake Videos

Deep Fake Detection and Countermeasures

Deepfake Video Generation

Phishing Attacks

Automated Malware Creation

CROSS-DOMAIN EXPLOITS

ADAPTIVE THREAT LANDSCAPE

Smart IoT and Machine Learning-Based Framework for Water Quality Assessment and Device Component Monitoring

SMART SOLUTIONS FOR WATER MANAGEMENT

Water Processing, Storage, and Distribution

Monitoring Water Quality

Process Data at the Edge

Data Analysis and Computation

Management Benefits

RESEARCH METHODOLOGY

IOT-BASED PROPOSED FRAMEWORK

ASSESSMENT OF WATER QUALITY USING MACHINE LEARNING

● Data Preprocessing

● Data Exploration

● Data Visualization and Imputation

● Outliers Removal

● Methodology

● Feature Engineering

● Feature Normalization and Selection

● Modeling using ML Techniques

RESULTS AND DISCUSSION

i. Precision

ii. Recall

iii. F-Score

iv. Accuracy

DISCLOSURE

Smart Water Management Framework for Irrigation

LITERATURE REVIEW.

SMART DEVICES FOR WATER MANAGEMENT

DISCLOSURE OF PREVIOUSLY PUBLISHED ARTICLE

Secure Framework against Cyberattacks on Cyber-Physical Robotic Systems

TAXONOMY OF CYBERSECURITY ROBOTIC CHALLENGES

PROPOSED SECURE SMART CYBERSECURITY FRAMEWORK

EXPERIMENTAL RESULTS

Multinomial Naïve Bayesian Classifier Framework for Systematic Analysis of Smart IoT Devices

RELATED WORK

Step 1: Import the Required Libraries and Dataset to Perform Exploratory Data Analysis

Step 2: Perform the data visualization and plot the word cloud for Amazon Alexa reviews

Step 3: Perform data cleaning and tokenization

Step 4: Build and train a deep learning model to analyze a smart IoT device

RESULTS AND COMPARATIVE ANALYSIS

IIoT: Traffic Data Flow Analysis and Modeling Experiment for Smart IoT Devices

RESULTS

FUTURE WORK

Comparison of IoT Communication Protocols Using Anomaly Detection with Security Assessments of Smart Devices

TLS AND DTLS COMPARISON

ATTACK ON IOT COMMUNICATION PROTOCOLS

PROPOSED ATTACK FRAMEWORK

RESULTS OBTAINED AND DISCUSSIONS

ABBREVIATIONS

All-Inclusive Attack Taxonomy and IoT Security Framework

IOT ATTACK TAXONOMY

IOT ATTACK FRAMEWORK

RESEARCH PERFORMED

Improving Performance of Machine Learning-Based Intrusion Detection System Using Simple Statistical Techniques in Feature Selection

INTRODUCTION.

LITERATURE REVIEW

RESEARCH

Methodology

Machine Learning Algorithms

Gaussian Naïve-Bayes Algorithm (NB)

Support Vector Machine Algorithm (SVM)

Logistic Regression Algorithm (LR)

Decision Tree (DT)

Random Forest Algorithm (RF)

Ada-boost Algorithm (AD)

Statistical Techniques for Feature Selection

Pearson Correlation Coefficient

Chi-Square Method (Chi2)

ANOVA

Performance Measures

Dataset and Pre-processing

DISCUSSIONS

Subject Index

Back Cover.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

9789815256710

9815256718

OCLC:

1467878531