book

Information Security Handbook

Name: Information Security Handbook
ISBN: 9781000540567

by Noor Zaman Jhanjhi, Khalid Hussain, Mamoona Humayun, Azween Bin Abdullah, João R.S. Tavares

February 2022

Intermediate to advanced

270 pages

8h 35m

English

CRC Press

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Cover
Half Title
Series Page
Title Page
Copyright Page
Contents
Preface
Editor Biographies
Contributors
1 SC-MCHMP: Score-Based Cluster Level Hybrid Multi-Channel MAC Protocol for Wireless Sensor Network
1.1 Introduction1.2 Literature Survey1.3 Proposed Work1.3.1 Procedure of Routing Mechanism1.3.1.1 1-Hop Discovery1.3.1.2 Metric Calculation1.3.1.3 Link Score Calculation1.3.1.4 Source Selection1.3.2 Csma/ca1.3.3 TDMA1.3.4 FDMA1.4 Results and Discussion1.5 ConclusionReferences

2 Software-Defined Networking (SDN) Security Concerns
2.1 Introduction2.2 Potential Anomalies in SDN2.2.1 Unauthorized Access2.2.2 Malicious Application2.2.3 Network Manipulation2.2.4 Traffic Diversion2.2.5 Denial of Service (DoS)2.2.6 Configuration Issues2.2.7 Data Leakage2.3 Security Analyses and Research Challenges2.3.1 SDN Security Analyses2.3.2 Scenarios2.3.2.1 Scenario 12.3.2.2 Scenario 22.3.2.3 Scenario 32.3.3 Research Challenges2.4 ConclusionReferences
3 Clustering in Wireless Sensor Networks Using Adaptive Neuro-Fuzzy Inference Logic
3.1 Introduction3.2 Related Work3.3 Proposed Work3.4 Simulation Results3.5 Conclusion and Future ScopeReferences
4 Security in Big Data
4.1 Big Data4.1.1 Volume – The Size of Data4.1.2 Variety – Different Forms of Data4.1.3 Velocity – Speed of Data Generation4.1.4 Value – Data's Worth4.1.5 Veracity – Data Uncertainty4.1.6 Variability – Data Inconsistency4.1.7 Visualization – Data Representation4.1.8 Volatility – How Long to Store Data4.1.9 Validity – Data Use4.2 Data Sources of Big Data4.3 Architecture4.3.1 Data Source4.3.2 Ingestion4.3.3 Storage Layer4.3.4 Staging4.3.5 Data Pipeline4.3.6 Data and Workflow Management4.3.7 Data Access4.4 Big Data Challenges4.5 Big Data Analytics Challenges in Big Data4.6 Technical Challenges in Big Data4.7 Characteristics-Oriented Challenges of Big Data4.7.1 Data Volume4.7.2 Data Velocity4.7.3 Data Variety4.7.4 Data Value4.8 Privacy Challenges4.9 Security Challenges4.10 Current Security Challenges in Big Data4.10.1 Big Data Security – A Definition4.10.2 Case Studies of Security Breaches Depicting Their Impact on Organizations4.11 Major Security Issues of Big Data4.11.1 Distributed Frameworks Security4.11.2 Nonrelational Data Stores Protection4.11.3 Storage Security4.11.4 Monitoring Real-Time Security4.11.5 Privacy-Preserving Data Analytics and Mining4.11.6 Granular Audit4.11.7 End-Point Security4.11.8 Data-Centric Security Based on Cryptography4.12 Solutions to Security Challenges4.12.1 Complete Data Supervision of Social Networks4.12.2 Improvement in Legal Mechanism4.12.3 Improvement to People Awareness of Data Quality4.12.4 Put Security First4.13 ConclusionReferences
5 Prevention of DOS/DDOS Attacks Through Expert Honey-Mesh Security Infrastructure
5.1 Introduction5.1.1 Types of DDOS Attacks5.1.1.1 Direct Flooding Attacks5.1.1.2 Remote-Controlled Network Attacks5.1.1.3 Reflective Flooding Attacks5.1.1.4 Protocol Violation Attacks5.1.1.5 Fragmentation Attacks5.1.1.6 Network Infrastructure Attacks5.2 Working of DDOS Attacks5.2.1 Using a Botnet “Botnet” to Launch a DDOS Attack5.2.2 Prevention Measures of DDOS Attacks5.2.3 Never Overestimate the Defenses of the Network5.2.4 Create a Reference Model to Better Identify Active Attacks5.2.5 Apply the Latest Patches of Suppliers5.2.6 Secure the IoT Devices5.2.7 Deploy a Solution for the Mitigation of DDOS Attacks Dedicated5.2.8 Reduce the Attack Surface5.2.9 Plan the Scaling5.2.10 Implement Firewalls for Sophisticated Application Attacks5.3 Methodology5.3.1 Introduction to Expert Honey Mesh System5.3.2 Creation of Honey Pots5.3.3 Implementation of Honey Pot5.4 Proposed System5.4.1 Working of Proposed System5.4.1.1 Detection and Prevention of DDOS Attack Via Our Proposed System5.5 Experimental Result5.5.1 Simulation5.5.2 DDOS Attack Launched on Website5.5.3 Increase in the Volume of Attacks and Distribution Over Time5.5.4 Prevention of DDOS Attack Via Fuzzy Logic Mechanism5.5.4.1 Fuzzy-Logic Rules5.5.5 Implementation of Fuzzy-Logic Rules for Prevention of DDOS Attacks5.5.6 Advantages and Future Enhancements5.6 ConclusionReferences
6 Efficient Feature Grouping for IDS Using Clustering Algorithms in Detecting Known/Unknown Attacks
6.1 Introduction6.2 Related Work6.3 Design Methodology6.4 Applications and Future Work6.5 ConclusionReferences
7 PDF Malware Classifiers – A Survey, Future Directions, and Recommended Methodology
7.1 Introduction7.2 X-Ray of PDF File7.3 Reading of PDF Files7.4 Steps Involved in Classifying PDF Files7.4.1 Prior Procedure Before the Actual Parsing7.4.2 Feature Extraction7.4.3 Classifier7.5 Modes of Classification7.6 Static Classification of PDF Files7.7 Static Classification Systems7.7.1 PJScan7.7.2 N-Gram7.7.3 Slayer7.7.4 N-Gram II7.7.5 PDFrate v17.7.6 Hidost7.7.7 Slayer NEO7.7.8 PDFrate v27.8 Comparison Between the Different Static Classifiers7.9 Dynamic Classification of PDF Files7.10 Dynamic Classification Systems7.10.1 PlatPal7.10.2 MDScan7.10.3 WepaWet7.10.4 PDF Scrutinizer7.10.5 Lux0R7.11 Comparison Between Different Dynamic Classifiers7.12 Novel Methodologies7.13 Conclusions and Future DirectionsReferences
8 Key Authentication Schemes for Medical Cyber Physical System
8.1 Introduction8.2 Security Objectives for Medical CPS8.2.1 Mutual Authentication8.2.2 Data Freshness8.2.3 Forward Security8.2.4 Data Authenticity8.2.5 Data Integrity8.2.6 Data Confidentiality8.2.7 Unforgeability8.2.8 Scalability8.3 Security Challenges for Medical CPS8.3.1 Resource Constraint (Gupta et al., 2019)8.3.2 Denial of Service Attack (Alguliyev et al., 2018)8.3.3 Impersonation Attack (Xu et al., 2019a,b)8.3.4 Replay Attack (Kompara et al., 2019)8.3.5 Eavesdropping Attack (Shen et al., 2018)8.3.6 Compromised Nodes and Clone Attack (Xu et al., 2019a,b)8.3.7 Anonymous and Unlinkable Sessions (Kompara et al., 2019)8.3.8 Desynchronization/Jamming Attack (Liu & Chung, 2017)8.4 Types of Key Authentication Schemes for Medical CPS8.4.1 Physiological-Based Key Authentication Schemes8.4.2 Cryptographic-Based Authentication Schemes8.4.2.1 Pre-deployed Authentication Schemes8.4.2.2 Asymmetric Authentication Schemes8.4.3 Hybrid Authentication Schemes8.4.4 Channel-Based Authentication Schemes8.5 Conclusion and Future Research DirectionReferences
9 Ransomware Attack: Threats & Different Detection Technique
9.1 Introduction9.2 Related Study9.3 Materials & Methods9.3.1 Algorithms Used for Ransomware/Malware Detection9.4 Analysis9.5 Practical Implication9.6 ConclusionReferences
10 Security Management System (SMS)
10.1 Introduction10.2 Application of Information Security in Corporations10.2.1 Information Security Components: Confidentiality, Integrity, Availability (CIA)10.2.1.1 Confidentiality10.2.1.2 Integrity10.2.1.3 Availability10.3 Information Security Incidents [11]10.4 Common Attacks on CIA Triad [12]10.5 Information Security Management10.6 Security Management System10.7 Basis of Security Management System10.7.1 Risk Management10.7.2 Risk Assessment10.7.3 Challenges for Risk Assessment10.8 Some Other Essentials for SMS10.9 Steps Involved in the Infrastructures of the SMS10.10 System Development and Maintenance10.11 Business Continuity Management10.12 Security Management System Components10.13 Security Management System Domains10.13.1 ISO/IEC 2700110.13.1.1 History of ISO/IEC 2700110.13.1.2 PDCA Model10.13.1.3 Changes in ISO/IEC 27001 in 2013 Version10.14 Security Controls [35]10.15 Certification10.16 SMS (ISO/IEC 27001) Audits [23]10.16.1 Benefits of ISO/IEC 2700110.17 Security Management System Challenges10.18 ConclusionReferences
11 Automatic Street Light Control Based on Pedestrian and Automobile Detection
11.1 Introduction11.2 Background of the Invention11.3 Literature Review11.4 Circuit Diagram11.4.1 Major Components Used11.5 Architecture Diagram11.6 Proposed Work11.6.1 Instructions11.7 Results and Discussion11.7.1 Usage of Automatic Street Lights11.7.2 Feasibility and Cost Effectiveness11.7.3 Used in Versatile Areas11.7.4 Large-Scale Manufacturing11.7.5 Merging with AI and IoT11.7.6 Stability and Emergency11.7.7 Security Protocols11.8 Conclusion & Future WorksReferences
12 Cost-Oriented Electronic Voting System Using Hashing Function with Digital Persona
12.1 Introduction12.2 Literature Review12.3 Proposed Method12.3.1 System Requirements12.3.2 System Architecture12.3.3 System Modules12.3.4 System Modeling12.4 Working Procedure12.4.1 Election Participants12.4.2 Election Process12.4.3 Generate Results12.5 Discussion and Findings12.5.1 Outcome of Proposed Voting System12.5.2 Existing Voting System of Pakistan with Proposed System12.5.3 Optimization in Multiple Enhancement Factors in Proposed System12.6 Conclusion12.7 Future WorkReference
13 Blockchain-Based Supply Chain System Using Intelligent Chatbot with IoT-RFID
13.1 Introduction13.2 Background13.2.1 Ethereum13.2.2 Hyperledger Fabric13.3 Chatbot with Blockchain13.3.1 IOT (Internet of Things)13.3.2 RFID Chips13.3.3 RFID with Blockchain13.3.4 Modern Supply Chain13.4 Problem Statement13.5 Literature Review13.6 Methodology13.7 ConclusionReferences
Index

Content preview from Information Security Handbook

3 Clustering in Wireless Sensor Networks Using Adaptive Neuro-Fuzzy Inference Logic

Seema Gaba, Radhika Gupta, Sahil Verma, Kavita, and Imran Taj

CONTENTS

3.1 Introduction
3.2 Related Work
3.3 Proposed Work
3.4 Simulation Results
3.5 Conclusion and Future Scope
References

DOI: 10.1201/9780367808228-3

3.1 Introduction

Numerous applications of self-organized networks that operate without any centralized support have tempted many researchers to study this area. Wireless sensor networks are the most well-known types of self-organized networks being used in many applications. These networks are called ad-hoc networks. The main provision of creating these networks is to readily provide services [1] to end users. The absence of infrastructure ...

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ISBN: 9781000540567

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Information Security Handbook

by Noor Zaman Jhanjhi, Khalid Hussain, Mamoona Humayun, Azween Bin Abdullah, João R.S. Tavares

3 Clustering in Wireless Sensor Networks Using Adaptive Neuro-Fuzzy Inference Logic

3.1 Introduction

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