Cyber Security and Business Intelligence
Innovations and Machine Learning for Cyber Risk Management
Sorozatcím: Routledge Studies in Innovation, Organizations and Technology;
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Beszerezhetőség
Még nem jelent meg, de rendelhető. A megjelenéstől számított néhány héten belül megérkezik.
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A termék adatai:
- Kiadás sorszáma 1
- Kiadó Routledge
- Megjelenés dátuma 2025. május 27.
- ISBN 9781032259581
- Kötéstípus Puhakötés
- Terjedelem234 oldal
- Méret 234x156 mm
- Nyelv angol
- Illusztrációk 86 Illustrations, black & white; 86 Line drawings, black & white; 31 Tables, black & white 700
Kategóriák
Rövid leírás:
The book provides both practical and managerial implications of cyber security that also supports business intelligence and discusses the latest innovations in cyber security.
TöbbHosszú leírás:
To cope with the competitive worldwide marketplace, organizations rely on business intelligence to an increasing extent. Cyber security is an inevitable practice to protect the entire business sector and its customer. This book presents the significance and application of cyber security for safeguarding organizations, individuals? personal information, and government.
The book provides both practical and managerial implications of cyber security that also supports business intelligence and discusses the latest innovations in cyber security. It offers a roadmap to master degree students and PhD researchers for cyber security analysis in order to minimize the cyber security risk and protect customers from cyber-attack. The book also introduces the most advanced and novel machine learning techniques including, but not limited to, Support Vector Machine, Neural Networks, Extreme Learning Machine, Ensemble Learning, and Deep Learning Approaches, with a goal to apply those to cyber risk management datasets. It will also leverage real-world financial instances to practise business product modelling and data analysis.
The contents of this book will be useful for a wide audience who are involved in managing network systems, data security, data forecasting, cyber risk modelling, fraudulent credit risk detection, portfolio management, and data regulatory bodies. It will be particularly beneficial to academics as well as practitioners who are looking to protect their IT system, and reduce data breaches and cyber-attack vulnerabilities.
TöbbTartalomjegyzék:
1. Leveraging Business Intelligence to Enhance Cyber Security Innovation
Sarika Faisal, Syed Far Abid Hossain, Saba Fahmida and Rayisa Rayhana
2. Cyber Risk and the Cost of Unpreparedness of Financial Institutions
Naveenan R.V and Suresh G
3.?Cyber Security in Banking Sector
Mohammad Zoynul Abedin, Petr Hajek and Nusrat Afrin Shilpa
4. Is the Application of Blockchain Technology in Accounting Feasible? A Developing Nation Perspective
Emon Kalyan Chowdhury
5. Empirical Analysis of Regression Techniques to Predict the Cybersecurity Salary
Mahmudul Hasan, Md. Mahedi Hassan, Md. Faisal-E-Alam, and Nazrin Akter
6.?Test Plan for Immersive Technology-Based Medical Support System
Mohammad Nasfikur R Khan, Bhushan Lohar, Robert Cloutier and Kari J. Lippert
7.?Current Challenges of Hand-Based Biometric Systems
Katerina Prihodova, and Miloslav Hub
8.?Investigating Machine Learning Algorithms with Model Explainability for Network Intrusion Detection
Sad Wadi Sajid, K.M. Rashid Anjum, Md. Al-Shahariar and Mahmudul Hasan
9.?How Much Do the Features Affect the Classifiers on UNSW-NB15? An XAI Equipped Model Interpretability
Mahmudul Hasan, Abdullah Haque, Md Mahmudul Islam and Md Al Amin
10. On the Selection of Suitable Dimensionality Reduction and Data Balancing Techniques to Classify DarkNet Access on CICDarknet2020
Mahmudul Hasan, Ashraful Islam and Ashrafuzzaman Shohag
11. An Effective Three-Layer Network Security to Prevent Distributed Denial of Service (DDoS) Attacks in Early Stages
Mahmudul Hasan, Sad Wadi Sajid and Md. Al Amin
12. Information Hiding Through a Novel DNA Steganography Technique to Secure Text Communication
Nahid Binte Sadia, Mahmudul Hasan and Md. Rashedul Islam
13. An Explainable AI-Driven Machine Learning Framework for Cybersecurity Anomaly Detection
Md. Mahedi Hassan, Md. Fahim Abrar and Mahmudul Hasan
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Több
