Autonomous Vehicles, Volume 1: Using Machine Intelligence

Cover
Title Page
Copyright Page
Contents
Preface
Chapter 1 Anomalous Activity Detection Using Deep Learning Techniques in Autonomous Vehicles
1.1 Introduction
1.1.1 Organization of Chapter
1.2 Literature Review
1.3 Artificial Intelligence in Autonomous Vehicles
1.4 Technologies Inside Autonomous Vehicle
1.5 Major Tasks in Autonomous Vehicle Using AI
1.6 Benefits of Autonomous Vehicle
1.7 Applications of Autonomous Vehicle
1.8 Anomalous Activities and Their Categorization
1.9 Deep Learning Methods in Autonomous Vehicle
1.10 Working of Yolo
1.11 Proposed Methodology
1.12 Proposed Algorithms
1.13 Comparative Study and Discussion
1.14 Conclusion
References
Chapter 2 Algorithms and Difficulties for Autonomous Cars Based on Artificial Intelligence
2.1 Introduction
2.1.1 Algorithms for Machine Learning in Autonomous Driving
2.1.2 Regression Algorithms
2.1.3 Design Identification Systems (Classification)
2.1.4 Grouping Concept
2.1.5 Decision Matrix Algorithms
2.2 In Autonomous Cars, AI Algorithms are Applied
2.2.1 Algorithms for Route Planning and Control
2.2.2 Method for Detecting Items
2.2.3 Algorithmic Decision-Making
2.3 AI’s Challenges with Self-Driving Vehicles
2.3.1 Feedback in Real Time
2.3.2 Complexity of Computation
2.3.3 Black Box Behavior
2.3.4 Precision and Dependability
2.3.5 The Safeguarding
2.3.6 AI and Security
2.3.7 AI and Ethics
2.4 Conclusion
References
Chapter 3 Trusted Multipath Routing for Internet of Vehicles against DDoS Assault Using Brink Controller in Road Awareness (TMRBC-IOV)
3.1 Introduction
3.2 Related Work
3.3 VANET Grouping Algorithm (VGA)
3.4 Extension of Trusted Multipath Distance Vector Routing (TMDR-Ext)
3.5 Conclusion
References
Chapter 4 Technological Transformation of Middleware and Heuristic Approaches for Intelligent Transport System
4.1 Introduction
4.2 Evolution of VANET
4.3 Middleware Approach
4.4 Heuristic Search
4.5 Reviews of Middleware Approaches
4.6 Reviews of Heuristic Approaches
4.7 Conclusion and Future Scope
References
Chapter 5 Recent Advancements and Research Challenges in Design and Implementation of Autonomous Vehicles
5.1 Introduction
5.1.1 History and Motivation
5.1.2 Present Scenario and Need for Autonomous Vehicles
5.1.3 Features of Autonomous Vehicles
5.1.4 Challenges Faced by Autonomous Vehicles
5.2 Modules/Major Components of Autonomous Vehicles
5.2.1 Levels of Autonomous Vehicles
5.2.2 Functional Components of An Autonomous Vehicle
5.2.3 Traffic Control System of Autonomous Vehicles
5.2.4 Safety Features Followed by Autonomous Vehicles
5.3 Testing and Analysis of An Autonomous Vehicle in a Virtual Prototyping Environment
5.4 Application Areas of Autonomous Vehicles
5.5 Artificial Intelligence (AI) Approaches for Autonomous Vehicles
5.5.1 Pedestrian Detection Algorithm (PDA)
5.5.2 Road Signs and Traffic Signal Detection
5.5.3 Lane Detection System
5.6 Challenges to Design Autonomous Vehicles
5.7 Conclusion
References
Chapter 6 Review on Security Vulnerabilities and Defense Mechanism in Drone Technology
6.1 Introduction
6.2 Background
6.3 Security Threats in Drones
6.3.1 Electronics Attacks
6.3.1.1 GPS and Communication Jamming Attacks
6.3.1.2 GPS and Communication Spoofing Attacks
6.3.1.3 Eavesdropping
6.3.1.4 Electromagnetic Interference
6.3.1.5 Laser Attacks
6.3.2 Cyber-Attacks
6.3.2.1 Man-in-Middle Attacks
6.3.2.2 Black Hole and Grey Hole
6.3.2.3 False Node Injection
6.3.2.4 False Communication Data Injection
6.3.2.5 Firmware’s Manipulations
6.3.2.6 Sleep Deprivation
6.3.2.7 Malware Infection
6.3.2.8 Packet Sniffing
6.3.2.9 False Database Injection
6.3.2.10 Replay Attack
6.3.2.11 Network Isolations
6.3.2.12 Code Injection
6.3.3 Physical Attacks
6.3.3.1 Key Logger Attacks
6.3.3.2 Camera Spoofing
6.4 Defense Mechanism and Countermeasure Against Attacks
6.4.1 Defense Techniques for GPS Spoofing
6.4.2 Defense Technique for Man-in-Middle Attacks
6.4.3 Defense against Keylogger Attacks
6.4.4 Defense against Camera Spoofing Attacks
6.4.5 Defense against Buffer Overflow Attacks
6.4.6 Defense against Jamming Attack
6.5 Conclusion
References
Chapter 7 Review of IoT-Based Smart City and Smart Homes Security Standards in Smart Cities and Home Automation
7.1 Introduction
7.2 Overview and Motivation
7.3 Existing Research Work
7.4 Different Security Threats Identified in IoT-Used Smart Cities and Smart Homes
7.4.1 Security Threats at Sensor Layer
7.4.1.1 Eavesdropping Attacks
7.4.1.2 Node Capturing Attacks
7.4.1.3 Sleep Deprivation Attacks
7.4.1.4 Malicious Code Injection Attacks
7.4.2 Security Threats at Network Layer
7.4.2.1 Distributed Denial of Service (DDOS) Attack
7.4.2.2 Sniffing Attack
7.4.2.3 Routing Attack
7.4.2.4 Traffic Examination Attacks
7.4.3 Security Threats at Platform Layer
7.4.3.1 SQL Injection
7.4.3.2 Cloud Malware Injection
7.4.3.3 Storage Attacks
7.4.3.4 Side Channel Attacks
7.4.4 Security Threats at Application Layer
7.4.4.1 Sniffing Attack
7.4.4.2 Reprogram Attack
7.4.4.3 Data Theft
7.4.4.4 Malicious Script Attack
7.5 Security Solutions For IoT-Based Environment in Smart Cities and Smart Homes
7.5.1 Blockchain
7.5.2 Lightweight Cryptography
7.5.3 Biometrics
7.5.4 Machine Learning
7.6 Conclusion
References
Chapter 8 Traffic Management for Smart City Using Deep Learning
8.1 Introduction
8.2 Literature Review
8.3 Proposed Method
8.4 Experimental Evaluation
8.4.1 Hardware and Software Configuration
8.4.2 About Dataset
8.4.3 Implementation
8.4.4 Result
8.5 Conclusion
References
Chapter 9 Cyber Security and Threat Analysis in Autonomous Vehicles
9.1 Introduction
9.2 Autonomous Vehicles
9.2.1 Autonomous vs. Automated
9.2.2 Significance of Autonomous Vehicles
9.2.3 Challenges in Autonomous Vehicles
9.2.4 Future Aspects
9.3 Related Works
9.4 Security Problems in Autonomous Vehicles
9.4.1 Different Attack Surfaces and Resulting Attacks
9.5 Possible Attacks in Autonomous Vehicles
9.5.1 Internal Network Attacks
9.5.2 External Attacks
9.6 Defence Strategies against Autonomous Vehicle Attacks
9.6.1 Against Internal Network Attacks
9.6.2 Against External Attack
9.7 Cyber Threat Analysis
9.8 Security and Safety Standards in AVs
9.9 Conclusion
References
Chapter 10 Big Data Technologies in UAV’s Traffic Management System: Importance, Benefits, Challenges and Applications
10.1 Introduction
10.2 Literature Review
10.3 Overview of UAV’s Traffic Management System
10.4 Importance of Big Data Technologies and Algorithm
10.5 Benefits of Big Data Techniques in UTM
10.6 Challenges of Big Data Techniques in UTM
10.7 Applications of Big Data Techniques in UTM
10.8 Case Study and Future Aspects
10.9 Conclusion
References
Chapter 11 Reliable Machine Learning-Based Detection for Cyber Security Attacks on Connected and Autonomous Vehicles
11.1 Introduction
11.2 Literature Survey
11.3 Proposed Architecture
11.4 Experimental Results
11.5 Analysis of the Proposal
11.6 Conclusion
References
Chapter 12 Multitask Learning for Security and Privacy in IoV (Internet of Vehicles)
12.1 Introduction
12.2 IoT Architecture
12.3 Taxonomy of Various Security Attacks in Internet of Things
12.3.1 Perception Layer Attacks
12.3.2 Network Layer Attacks
12.3.3 Application Layer Attacks
12.4 Machine Learning Algorithms for Security and Privacy in IoV
12.5 A Machine Learning-Based Learning Analytics Methodology for Security and Privacy in Internet of Vehicles
12.5.1 Methodology
12.5.2 Result Analysis
12.6 Conclusion
References
Chapter 13 ML Techniques for Attack and Anomaly Detection in Internet of Things Networks
13.1 Introduction
13.2 Internet of Things
13.3 Cyber-Attack in IoT
13.4 IoT Attack Detection in ML Technics
13.5 Conclusion
References
Chapter 14 Applying Nature-Inspired Algorithms for Threat Modeling in Autonomous Vehicles
14.1 Introduction
14.2 Related Work
14.3 Proposed Mechanism
14.4 Performance Results
14.5 Future Directions
14.6 Conclusion
References
Chapter 15 The Smart City Based on AI and Infrastructure: A New Mobility Concepts and Realities
15.1 Introduction
15.2 Research Method
15.3 Vehicles that are Both Networked and Autonomous
15.4 Personal Aerial Automobile Vehicles and Unmanned Aerial Automobile Vehicles
15.5 Mobile Connectivity as a Service
15.6 Major Role for Smart City Development with IoT and Industry 4.0
15.7 Conclusion
References
Index
EULA