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Jaydip Sen

Praxis Business School

Jaydip Sen is a professor at the Department of Data Science at Praxis Business School in Kolkata, India. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, artificial intelligence in the financial domain, and adversarial machine learning. He has published over 250 papers in reputed indexed journals, refereed international conference proceedings, and 18 book chapters. He has authored five books and edited sixteen volumes. He is the editor of Knowledge Decision Support Systems in Finance and served on the technical program committees of several high-ranked international conferences of the Institute of Electronics and Electric Engineers (IEEE) and the Association for Computing Machinery (ACM). He has contributed to several IEEE USA standardization efforts, including the 802.16m and 3GPP LTE standards. Stanford University has listed Prof. Sen among the top 2% of scientists worldwide for the last seven consecutive years (2019–2025). Prof. Sen is a Senior Member of IEEE, USA, a Professional Member of ACM, USA, and a life member of Indian Society of Technical Education (ISTE). Prof. Sen also figured in the SciRank Global of top 5% scientists in the world (https://scirankglobal.org).

Links

Personal website

10books edited

15chapters authored

Latest work with IntechOpen by Jaydip Sen

Data Privacy and Security - Principles and Applications offers a comprehensive look at the critical aspects of protecting data in today’s interconnected world. As digital innovations rapidly expand across sectors such as healthcare, finance, artificial intelligence, and the Internet of Things (IoT), robust privacy and security measures are more essential than ever. This volume equips readers with foundational principles and cutting-edge strategies for safeguarding data, from practical security methods to ethical and regulatory considerations. Emphasizing both theoretical and applied knowledge, it explores the complexities of privacy-preserving technologies, data protection standards, and the vital role of regulatory compliance. This volume is designed for professionals, researchers, and students eager to understand how privacy can be maintained without compromising data utility. Whether delving into encryption, cybersecurity frameworks, or evolving privacy laws, this book provides a unique balance of insights and actionable strategies for securing data in various industries. Readers will gain a thorough understanding of the challenges and solutions associated with data privacy, offering an essential resource for navigating today’s digital landscape with a responsible, future-focused approach.

Data Privacy

Data Privacy

Edited by Jaydip Sen

Books edited

Wireless Sensor Networks

Wireless Sensor Networks

Edited by Jaydip Sen

Information Security and Privacy in the Digital World

Information Security and Privacy in the Digital World

Edited by Jaydip Sen

Machine Learning

Machine Learning

Edited by Jaydip Sen

Computer and Network Security

Computer and Network Security

Edited by Jaydip Sen

Internet of Things

Internet of Things

Edited by Jaydip Sen

Cloud Computing

Cloud Computing

Edited by Jaydip Sen

Theory and Practice of Cryptography and Network Security Protocols and Technologies

Theory and Practice of Cryptography and Network Security Protocols and Technologies

Edited by Jaydip Sen

Applied Cryptography and Network Security

Applied Cryptography and Network Security

Edited by Jaydip Sen

Cryptography and Security in Computing

Cryptography and Security in Computing

Edited by Jaydip Sen

Chapters authored

Routing Security Issues in Wireless Sensor Networks: Attacks and Defenses

By Jaydip Sen

Part of the book: Sustainable Wireless Sensor Networks

Secure Routing in Wireless Mesh Networks

By Jaydip Sen

Part of the book: Wireless Mesh Networks

Secure and Privacy-Preserving Data Aggregation Protocols for Wireless Sensor Networks

By Jaydip Sen

Part of the book: Cryptography and Security in Computing

Secure and Privacy-Preserving Authentication Protocols for Wireless Mesh Networks

By Jaydip Sen

Part of the book: Applied Cryptography and Network Security

Homomorphic Encryption — Theory and Application

By Jaydip Sen

Part of the book: Theory and Practice of Cryptography and Network Security Protocols and Technologies

Introductory Chapter: Machine Learning in Misuse and Anomaly Detection

By Jaydip Sen and Sidra Mehtab

Part of the book: Computer and Network Security

Machine Learning Applications in Misuse and Anomaly Detection

By Jaydip Sen and Sidra Mehtab

Machine learning and data mining algorithms play important roles in designing intrusion detection systems. Based on their approaches toward the detection of attacks in a network, intrusion detection systems can be broadly categorized into two types. In the misuse detection systems, an attack in a system is detected whenever the sequence of activities in the network matches with a known attack signature. In the anomaly detection approach, on the other hand, anomalous states in a system are identified based on a significant difference in the state transitions of the system from its normal states. This chapter presents a comprehensive discussion on some of the existing schemes of intrusion detection based on misuse detection, anomaly detection and hybrid detection approaches. Some future directions of research in the design of algorithms for intrusion detection are also identified.

Part of the book: Security and Privacy From a Legal, Ethical, and Technical Perspective

Design and Analysis of Robust Deep Learning Models for Stock Price Prediction

By Jaydip Sen and Sidra Mehtab

Building predictive models for robust and accurate prediction of stock prices and stock price movement is a challenging research problem to solve. The well-known efficient market hypothesis believes in the impossibility of accurate prediction of future stock prices in an efficient stock market as the stock prices are assumed to be purely stochastic. However, numerous works proposed by researchers have demonstrated that it is possible to predict future stock prices with a high level of precision using sophisticated algorithms, model architectures, and the selection of appropriate variables in the models. This chapter proposes a collection of predictive regression models built on deep learning architecture for robust and precise prediction of the future prices of a stock listed in the diversified sectors in the National Stock Exchange (NSE) of India. The Metastock tool is used to download the historical stock prices over a period of two years (2013–2014) at 5 minutes intervals. While the records for the first year are used to train the models, the testing is carried out using the remaining records. The design approaches of all the models and their performance results are presented in detail. The models are also compared based on their execution time and accuracy of prediction.

Part of the book: Machine Learning

Introductory Chapter: Machine Learning in Finance-Emerging Trends and Challenges

By Jaydip Sen, Rajdeep Sen and Abhishek Dutta

Part of the book: Machine Learning

Introductory Chapter: Data Privacy Preservation on the Internet of Things

By Jaydip Sen and Subhasis Dasgupta

Part of the book: Information Security and Privacy in the Digital World

View all chapters

Related collaborators

Zhe Yang

Blekinge Institute of Technology , Sweden

Abbas Mohammed

Blekinge Institute of Technology , Sweden

Mathias Grudén

Haffaf Hafid

Khelifa Benahmed

Sanjib Kumar Panda

National University of Singapore , Singapore

Magnus Jobs

Anders Rydberg

Madjid Merabti

Liverpool John Moores University , United Kingdom

Tan Yen Kheng

A*STAR - Agency for Science, Technology and Research