book

Cyber Security and Digital Forensics

by Mangesh M. Ghonge, Sabyasachi Pramanik, Ramchandra Mangrulkar, Dac-Nhuong Le

March 2022

Intermediate to advanced

432 pages

10h 44m

English

Wiley-Scrivener

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1.1 Introduction and Related Work1.2 IoT: Evolution, Applications and Security Requirements1.3 Service-Oriented IoT Architecture and IoT Protocol Stack1.4 Anatomy of Attacks on Service-Oriented IoT Architecture1.5 Major Security Issues in Service-Oriented IoT Architecture1.6 ConclusionReferences
2.1 Introduction2.2 Security of Modern Cryptographic Systems2.3 Quantum Key Distribution2.4 Post-Quantum Digital Signature2.5 Conclusion and Future DirectionsReferences
3.1 Introduction3.2 Digital Forensic Analysis Knowledge3.3 Answer Set Programming in Digital Investigations3.4 Data Science Processing with Artificial Intelligence Models3.5 Pattern Recognition Techniques3.6 ANN Applications3.7 Knowledge on Stages of Digital Forensic Analysis3.8 Deep Learning and Modelling3.9 ConclusionReferences
4.1 Introduction4.2 Homomorphic Encryption Techniques4.3 Homomorphic Encryption Libraries4.4 Computations on Encrypted Data4.5 Applications of Homomorphic Encryption4.6 ConclusionReferences
5.1 Introduction5.2 Physical Capabilities of Robots5.3 Traditional Psychology, Neuroscience and Future Robotics5.4 Synthetic Psychology and Robotics: A Vision of the Future5.5 Synthetic Psychology: The Foresight5.6 Synthetic Psychology and Mathematical Optimization5.7 Synthetic Psychology and Medical Diagnosis5.8 ConclusionReferences

6.1 Overview6.2 Digital Forensics6.3 Digital Forensics History6.4 Evolutionary Cycle of Digital Forensics6.5 Stages of Digital Forensics Process6.6 Types of Digital Forensics6.7 Evidence Collection and Analysis6.8 Digital Forensics Tools6.9 SummaryReferences
7.1 Introduction7.2 Objective7.3 Types of Digital Forensics7.4 ConclusionReferences
8.1 Introduction8.2 Literature Review8.3 Medical Cyber Physical System (MCPS)8.4 CPSSEC vs. Cyber Security8.5 Proposed Framework8.6 Assessment of Hypothetical MCPS Using 4S Framework8.7 Conclusion8.8 Future ScopeReferences
9.1 IoT and Blockchain9.2 IoT Application Domains and Challenges in Data Sharing9.3 Why Blockchain?9.4 IoT Data Sharing Security Mechanism On Blockchain9.5 ConclusionReferences
10.1 Introduction10.2 Face Recognition10.3 Forensic Face Recognition10.4 ConclusionReferences
11.1 Introduction11.2 Phases of Digital Forensics Process11.3 Analysis of Challenges and Need of Digital Forensics11.4 Appropriateness of Forensics Tool11.5 Phase-Wise Digital Forensics Techniques11.6 Pros and Cons of Digital Forensics Investigation Process11.7 ConclusionReferences
12.1 Introduction12.2 Proposed Method12.3 Experimental Studies and Outcomes Analysis12.4 Conclusions and Future ScopeReferences
13.1 Introduction13.2 A Brief Literature Review13.3 Machine Learning and Cybersecurity: Various Issues13.4 ML Datasets and Algorithms Used in Cybersecurity13.5 Applications of Machine Learning in the Realm of Cybersecurity13.6 ConclusionsReferences
14.1 Introduction14.2 Significance of Security Improvement in DCS and SCADA14.3 Related Work14.4 Proposed Methodology14.5 Result Analysis14.6 ConclusionReferences
15.1 Introduction15.2 SQL Injection15.3 Cross Site Scripting15.4 Cross Site Request Forgery15.5 Command Injection15.6 File Inclusion15.7 ConclusionReferences
16.1 Introduction16.2 Types of Ransomwares16.3 Ransomware Life Cycle16.4 Detection Strategies16.5 Analysis of Ransomware16.6 Prevention Strategies16.7 Ransomware Traits Analysis16.8 Research Directions16.9 ConclusionReferences

Content preview from Cyber Security and Digital Forensics

12Utilizing Machine Learning and Deep Learning in Cybesecurity: An Innovative Approach

Dushyant Kaushik¹, Muskan Garg¹, Annu¹, Ankur Gupta¹ and Sabyasachi Pramanik²*

¹Vaish College of Engineering, Rohtak, Haryana, India

²Haldia Institute of Technology, Haldia, West Bengal, India

*Corresponding author: sabyalnt@gmail.com

Abstract

Machine learning (ML) and deep learning (DL) have both produced overwhelming interest and drawn unparalleled community interest recently. With a growing convergence of online activities and digital life, the way people have learned and function is evolving, but this also leads them towards significant security concerns. Protecting sensitive information, documents, networks and machine-connected devices from unwanted cyber threats is a difficult task. Robust cybersecurity protection is necessary for this reason. For a problem solution, current innovations like machine learning and deep learning is incorporated to cyber threats. This paper also highlights the problems and benefits with using ML / DL and presents recommendations for research directions for machine learning and deep learning in cybersecurity.

Keywords: Cyber protection, deep learning, text mining, intrusion detection, machine learning

12.1 Introduction

Currently, Internet-connected systems, such as hardware, software and data, can still be secured by cybersecurity from cyber threats. Cybersecurity [1] is a combination of products and systems intended to fight against attacks and illegal ...