MACHINE LEARNING FOR CYBERSECURITY: THREAT DETECTION AND MITIGATION

Dr. Araddhana Arvind Deshmukh · Dr. Jyoti Chetan Vanikar · Dr Aadam Quraishi MD · Samreen Rizvi

Jul 2024 · Xoffencer international book publication house

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224

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About this ebook

As a result of the increasingly complex structure of today's information systems, there is a growing agreement that Artificial Intelligence (AI) is required in order to keep up with the exponential expansion of big data. Techniques from the field of machine learning (ML), in particular deep learning, are already being used to address a broad range of issues that are encountered in the real world. There are a number of intriguing examples of machine learning's practical triumphs, including machine translation, recommendations for vacations and travel, item identification and monitoring, and even various applications in the healthcare industry. Furthermore, machine learning has shown a great deal of promise in the area of autonomous driving and communication systems, which is why it is rightly considered to be a technical enabler. On the other hand, the civilization of today is more reliant than ever before on information technology systems, even autonomous ones, which are itself abused by malicious actors. In actuality, cybercriminals are always inventing new threats, and, they will have the ability to do significant harm or even kill people due to their capabilities. In order for defensive mechanisms to be able to prevent such events and limit the multiplicity of hazards that might potentially harm both current and future information technology systems, they need to be able to quickly adapt to (i) settings that are continually changing and (ii) threat landscapes that are always developing. It is hard to ignore the use of machine learning in the field of cybersecurity since it is manifestly impossible to address such a dual demand using methodologies that are static and human-defined. It is not surprising that a number of surveys and technical studies have been conducted on the subject of machine learning integration in the field of cybersecurity. Even though there have been a lot of accomplishments in research settings, there has been only a little amount of progress made in creating and integrating machine learning in industrial systems. The vast majority of these solutions are still using 'unsupervised' techniques, mostly for 'anomaly detection,' according to a recent report. This is despite the fact that more than ninety percent of enterprises are presently incorporating AI and ML into their defensive systems.

About the author

Dr. Araddhana Arvind Deshmukh, Professor in School of CSIT, Symbiosis Skills, and Professional University, Pune┭┮┯┰She has completed PhD at Aarhus University, Denmark. Dr. Araddhana has 21 years of teaching experience. She has published 142 papers. She has authored 11 books and published 21 patents out of 5 waa granted. She was a reviewer and chaired many international conferences She has delivered 57 talks. She received funding for research from BCUD and AICTE, India. She has received 7 awards, 'Excellence in Research', 'Young Researchers Award ‘and Best Paper Award ‘in 19 different conferences. She is a Fellow member of IEI(India), UACEE(USA), IAENG(Hongkong), and CSI (Pune), Her area of research is Cloud Security, Computing, the Internet of Things, and Artificial Intelligence, Dr. Araddhana has triple graduate degrees and double post-graduate degrees in her hand.

Dr. Jyoti Chetan Vanikar holds an M.Sc in Mathematics from Karnatak University Dharwar. She obtained her Ph.D. from Shri JJT University, Rajasthan. Worked as an Assistant Professor in Mathematics at the National Defense Academy (NDA), Khadakwasla, Pune for two decades. Currently working as an Assistant Professor at Symbiosis Skills and Professional University Kiwale Pune. Dr Jyoti has teaching and research experience of more than 21 Years. Dr Jyoti's main research area is the analytical and numerical modeling of surfacegroundwater interactions. She has published research articles in reputed international journals, Book Chapters, and Indian Patents. For outstanding research work, one of her articles was awarded First Prize at the International Conference in Material and Environmental Science (ICMES) 2018.

Dr Aadam Quraishi MD,. MBA has research and development roles involving some combination of NLP, deep learning, reinforcement learning, computer vision, and predictive modeling. He is actively leading a team of data scientists, ML researchers, and engineers, conducting research across the full machine learning life cycle - data access, infrastructure, model R&D, systems design, and deployment

Samreen Rizvi Armed with a Bachelor of Science and a Master's in Computer Application, Samreen Rizvi has over a decade of rich IT professional experience. Throughout her career journey, she's not only mastered the intricacies of data management, system architecture, and thorough testing but has also excelled in building collaborative partnerships with stakeholders. Samreen's professional narrative stands out for her prowess in implementing cutting-edge data privacy and governance measures across a spectrum of organizations. Driven by an unwavering commitment to safeguarding data integrity and fortifying security, Samreen thrives in the dynamic world of IT. Her multifaceted background and dedication to data protection set her apart as a trusted professional in the field. Beyond her impressive career, Samreen is actively engaged in research and has authored multiple papers delving into the realms of artificial intelligence, machine learning, and cybersecurity

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