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Total Size:
5.6 MB
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C7525A18F53930591F96B52FF77A32F5058829D0
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Sept. 25, 2025, 10:35 a.m.
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(Last updated: Sept. 25, 2025, 10:38 a.m.)
| File | Size |
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| Gururaj H Cyber Security in Business Analytics 2026.pdf | 5.6 MB |
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158.5 MB
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2023-11-14
| Uploaded by indexFroggy | Size 158.5 MB | Health [ 8 /3 ] | Added 2023-11-14 |
NOTE
SOURCE: Gururaj H Cyber Security in Business Analytics 2026
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COVER
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MEDIAINFO
Textbook in PDF format There is a growing need for insights and practical experiences in the evolving field of cyber security for business analytics a need addressed by Cyber Security in Business Analytics. Divided into sections covering cyber security basics, Artificial Intelligence (AI) methods for threat detection, and practical applications in e-commerce and e-banking, the book's team of experts provides valuable insights into securing business data and improving decision-making processes. It covers topics such as data privacy, threat detection, risk assessment, and ethical considerations, catering to both technical and managerial audiences. Machine Learning (ML) now emerges as a transformative technology that facilitates automation within security systems, empowering security teams to more effectively navigate the complex threats of the present day. In contrast to the traditional rule-based detection methods, ML algorithms truly excel at identifying subtle anomalies and nefarious activities. They achieve this through the meticulous real-time analysis of vast datasets, revealing intricate patterns of attack. The capability for intelligent and automated decision-making not only enables swifter response times but also enhances adaptability to the ever-evolving landscape of cyber security. It thus identifies threats better through addressing known as well as unknown dangers and, hence, provides a more proactive kind of defense when it is integrated into cyber security frameworks. In terms of changing security policies and responding to newly emerging threats, it is a significant leap in safeguarding highly dynamic networks, particularly in areas where traditional defense mechanisms cannot protect against modern cyberattacks. Nmap, or Network Mapper, is a free and powerful network scanning tool. It was developed by Gordon Lyon, who is also known as Fyodor. This tool is widely used in network discovery and security auditing. Nmap can determine the live hosts on the network, scan for open ports, identify services running on those ports along with their versions, and even the operating system and hardware characteristics of network devices. Its capabilities go further with the Nmap Scripting Engine (NSE), which enables advanced service detection, vulnerability scanning, and much more through custom scripts [8]. Nmap can be installed on Windows, Linux, and macOS platforms. It is a network administrator’s, security expert’s, and ethical hacker’s best friend, not to mention the malicious attacker’s favorite tool. Its versatility and depth make it an indispensable tool in network security. Wireshark is a highly recognized network protocol analyzer and widely used in network troubleshooting, analysis, software and protocol development, and education. With this tool, the users are able to observe real-time data traffic at detailed levels on their networks. Thus, this proves to be one of the critical diagnostic tools for network-related issues. The tool supports the deep inspection of hundreds of protocols, providing both live capture and offline analysis capabilities. Its standard three-pane packet browser and powerful display filters make it easy to dissect and understand complex network communications. The Metasploit Framework is an open-source penetration testing platform designed by Rapid7 to provide a comprehensive set of tools used to discover, exploit, and validate vulnerabilities on various systems for security professionals and ethical hackers. • Presents real-case scenarios for enhancing understanding of how cyber security principles are applied in diverse organizational settings • Offers advanced technologies such as Artificial Intelligence methods for cyber threat detection, offering readers • Provides a detailed exploration of how AI can make cybersecurity better by helping detect threats, unusual activities, and predict potential risks • Focuses on the convergence of cyber security and data-driven decision-making and explores how businesses can leverage analytics while safeguarding sensitive information • Includes insights into cutting-edge techniques in the field, such as detailed explorations of various cyber security tools within the context of business analytics Preface Introduction to Learning Methods for Business Analytics Emerging Cyber Security Challenges and Trends in the Business World Cyber Security Issues, Challenges in E-Shopping/E-Commerce Knowledge Representation of Various Business Models Reactive Versus Proactive Cyber Security and Real-Time Threat Protection Exploring the Importance of Incident Management in Modern Organizations Issues, Challenges in E-Banking: Case Study Cyber Security for Machine Learning Systems in Business Data Privacy-Preserving Deep Learning Techniques for Business Big Data Navigating Cyber Security Tools: A Comprehensive Guide from Entry to Expert Level Improving Cyber Security Measures in Business Analytics for E-Commerce Platforms in Africa: Cyber Laws, Challenges, and Solutions Optimizing User Engagement with Personalized Recommendations and Targeted Advertising
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