Detection And Mitigation Of Insider Attacks In A Cloud Infrastructure

Businesses constantly face online hacking threats or security breaches in their online mainframe that expose sensitive information to the wrong audience. Companies look to store their data in a separate location, distancing the availability of the information and reducing the risk of data breaches. Modern organizations need to remain vigilant against insider attacks, cloud computing risks, and security flaws within their mainframe. Detection and Mitigation of Insider Attacks in a Cloud Infrastructure: Emerging Research and Opportunities is an essential reference source that discusses maintaining a secure management of sensitive data, and intellectual property and provides a robust security algorithm on consumer data. Featuring research on topics such as public cryptography, security principles, and trustworthy computing, this book is ideally designed for IT professionals, business managers, researchers, students, and professionals seeking coverage on preventing and detecting the insider attacks using trusted cloud computing techniques.

Businesses constantly face online hacking threats or security breaches in their online mainframe that expose sensitive information to the wrong audience. Companies look to store their data in a separate location, distancing the availability of the information and reducing the risk of data breaches. Modern organizations need to remain vigilant against insider attacks, cloud computing risks, and security flaws within their mainframe. Detection and Mitigation of Insider Attacks in a Cloud Infrastructure: Emerging Research and Opportunities is an essential reference source that discusses maintaining a secure management of sensitive data, and intellectual property and provides a robust security algorithm on consumer data. Featuring research on topics such as public cryptography, security principles, and trustworthy computing, this book is ideally designed for IT professionals, business managers, researchers, students, and professionals seeking coverage on preventing and detecting the insider attacks using trusted cloud computing techniques.

Today's malware mutates randomly to avoid detection, but reactively adaptive malware is more intelligent, learning and adapting to new computer defenses on the fly. Using the same algorithms that antivirus software uses to detect viruses, reactively adaptive malware deploys those algorithms to outwit antivirus defenses and to go undetected. This book provides details of the tools, the types of malware the tools will detect, implementation of the tools in a cloud computing framework and the applications for insider threat detection.

Infrastructure development presents significant challenges for both developing and developed countries, hindering their progress in achieving the Sustainable Development Goals (SDGs). Governments often struggle to effectively leverage the necessary resources and expertise for financing and managing infrastructure projects, resulting in untapped potential for sustainable and inclusive development. Achieving the Sustainable Development Goals Through Infrastructure Development, edited by Cristina Raluca Gh. Popescu, Poshan Yu, and Yue Wei, offers a comprehensive guide to address these challenges. Focusing on public-private partnerships (PPPs) as a transformative solution, the book equips policymakers, investors, practitioners, and researchers with the essential knowledge and tools needed to navigate the complexities of infrastructure development and leverage the expertise and resources of the private sector. By showcasing successful case studies, analyzing critical success factors, and providing valuable insights into the implementation of PPPs in both developing and developed countries, this book becomes an indispensable resource for driving progress towards the SDGs. Covering crucial topics such as financing, risk management, legal frameworks, and sustainability considerations, it empowers readers to make informed decisions and foster collaborative partnerships between the public and private sectors. Through its comprehensive roadmap, this book enables stakeholders to unlock the full potential of sustainable and inclusive infrastructure development, paving the way for a prosperous future for all.

In today’s modern age of information, new technologies are quickly emerging and being deployed into the field of information technology. Cloud computing is a tool that has proven to be a versatile piece of software within IT. Unfortunately, the high usage of Cloud has raised many concerns related to privacy, security, and data protection that have prevented cloud computing solutions from becoming the prevalent alternative for mission critical systems. Up-to-date research and current techniques are needed to help solve these vulnerabilities in cloud computing. Modern Principles, Practices, and Algorithms for Cloud Security is a pivotal reference source that provides vital research on the application of privacy and security in cloud computing. While highlighting topics such as chaos theory, soft computing, and cloud forensics, this publication explores present techniques and methodologies, as well as current trends in cloud protection. This book is ideally designed for IT specialists, scientists, software developers, security analysts, computer engineers, academicians, researchers, and students seeking current research on the defense of cloud services.

Every type of organization is vulnerable to insider abuse, errors, and malicious attacks: Grant anyone access to a system and you automatically introduce a vulnerability. Insiders can be current or former employees, contractors, or other business partners who have been granted authorized access to networks, systems, or data, and all of them can bypass security measures through legitimate means. Insider Threat – A Guide to Understanding, Detecting, and Defending Against the Enemy from Within shows how a security culture based on international best practice can help mitigate the insider threat, providing short-term quick fixes and long-term solutions that can be applied as part of an effective insider threat program. Read this book to learn the seven organizational characteristics common to insider threat victims; the ten stages of a malicious attack; the ten steps of a successful insider threat program; and the construction of a three-tier security culture, encompassing artefacts, values, and shared assumptions. Perhaps most importantly, it also sets out what not to do, listing a set of worst practices that should be avoided. About the author Dr Julie Mehan is the founder and president of JEMStone Strategies and a principal in a strategic consulting firm in Virginia. She has delivered cybersecurity and related privacy services to senior commercial, Department of Defense, and federal government clients. Dr Mehan is also an associate professor at the University of Maryland University College, specializing in courses in cybersecurity, cyberterror, IT in organizations, and ethics in an Internet society

This book defines the nature and scope of insider problems as viewed by the financial industry. This edited volume is based on the first workshop on Insider Attack and Cyber Security, IACS 2007. The workshop was a joint effort from the Information Security Departments of Columbia University and Dartmouth College. The book sets an agenda for an ongoing research initiative to solve one of the most vexing problems encountered in security, and a range of topics from critical IT infrastructure to insider threats. In some ways, the insider problem is the ultimate security problem.

As the advancement of technology continues, cyber security continues to play a significant role in today’s world. With society becoming more dependent on the internet, new opportunities for virtual attacks can lead to the exposure of critical information. Machine and deep learning techniques to prevent this exposure of information are being applied to address mounting concerns in computer security. The Handbook of Research on Machine and Deep Learning Applications for Cyber Security is a pivotal reference source that provides vital research on the application of machine learning techniques for network security research. While highlighting topics such as web security, malware detection, and secure information sharing, this publication explores recent research findings in the area of electronic security as well as challenges and countermeasures in cyber security research. It is ideally designed for software engineers, IT specialists, cybersecurity analysts, industrial experts, academicians, researchers, and post-graduate students.