Jaydip Sen

By Jaydip Sen

Part of the book: Sustainable Wireless Sensor Networks

By Jaydip Sen

Part of the book: Wireless Mesh Networks

By Jaydip Sen

Part of the book: Cryptography and Security in Computing

By Jaydip Sen

Part of the book: Applied Cryptography and Network Security

By Jaydip Sen

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

By Jaydip Sen and Sidra Mehtab

Part of the book: Computer and Network Security

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

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

By Jaydip Sen, Rajdeep Sen and Abhishek Dutta

Part of the book: Machine Learning

By Jaydip Sen

Wireless sensor networks (WSNs) are made up of a large number of tiny sensors, which can sense, analyze, and communicate information about the outside world. These networks play a significant role in a broad range of fields, from crucial military surveillance applications to monitoring building security. Key management in WSNs is a critical task. While the security and integrity of messages communicated through these networks and the authenticity of the nodes are dependent on the robustness of the key management schemes, designing an efficient key generation, distribution, and revocation scheme is quite challenging. While resource-constrained sensor nodes should not be exposed to computationally demanding asymmetric key algorithms, the use of symmetric key-based systems leaves the entire network vulnerable to several attacks. This chapter provides a comprehensive survey of several well-known cryptographic mechanisms and key management schemes for WSNs.

Part of the book: Wireless Sensor Networks - Design, Applications and Challenges [Working title]