Sajal Saha

Work place: Adamas University/CSE, Barasat, 700126, India



Research Interests: Computer Networks, Network Architecture, Network Security


Dr. Sajal Saha has 18 years of academic experience working both in the affiliated college and university system. Currently he is the Professor and Head, Department of Computer Science and Engineering and Director-Product & Innovation of Adamas University. Prior to this, he was the Principal of Meghnad Saha Institute of Technology under Techno India Group affiliated to Maulana Abul Kalam Azad University of Technology, West Bengal. From 2019 to 2021, he served as Dean of the School of Computing Sciences and Dean of Research at The Assam Kaziranga University. There he served as Associate Dean (Compliance) from 2016 to 2019. His total number of research publications are 40. He authored 2 books titled “GIS and Remote Sensing: Applications in Flood Damage assessment” and “Mobility Management in IP based Network: Framework, Issues and Challenges”. For more details:

Author Articles
Detection of Unknown Insider Attack on Components of Big Data System: A Smart System Application for Big Data Cluster

By Swagata Paul Sajal Saha Radha Tamal Goswami

DOI:, Pub. Date: 8 Oct. 2022

Big data applications running on a big data cluster, creates a set of process on different nodes and exchange data via regular network protocols. The nodes of the cluster may receive some new type of attack or unpredictable internal attack from those applications submitted by client. As the applications are allowed to run on the cluster, it may acquire multiple node resources so that the whole cluster becomes slow or unavailable to other clients. Detection of these new types of attacks is not possible using traditional methods. The cumulative network traffic of the nodes must be analyzed to detect such attacks. This work presents an efficient testbed for internal attack generation, data set creation, and attack detection in the cluster. This work also finds the nodes under attack. A new insider attack named BUSY YARN Attack has been identified and analyzed in this work. The framework can be used to recognize similar insider attacks of type DOS where target node(s) in the cluster is unpredictable.

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