Dr. Santosh Kumar Smmarwar

About

Santosh Kumar Smmarwar received the Ph.D degree (in Cyber Security) in 2024 from the National Institute of Technology Raipur, India. He is currently an Assistant Professor with the Department of Centre for Internet of Things, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneshwar, India. He received his Master’s degree (in Cyber Law & Information Security) in 2011 from the Indian Institute of Information Technology Allahabad, India and Bachelor's degree (in Computer Science & Engineering) in 2008 from Samrat Ashok Technological Institute (S.A.T.I.) Vidisha, India. His research interests include Malware Detection, Intrusion Detection, Internet of Things, and Cyber Security

Employment

• Siksha O Anusandhan University Assistant Professor
  2023 - Present

Education

• Indian Institute of Information Technology Allahabad MS in cyber law and Information Security
  2009 - 2011
• Samrat Ashok Technological Institute B.E
  2004 - 2008

Projects & Funding

Projects & funding information is unavailable.

Publications (12)

• AIMD: AI-powered android malware detection for securing AIoT devices and networks using graph embedding and ensemble learning
  Journal of Systems Architecture 2026 DOI: 10.1016/j.sysarc.2026.103707
  +Save
• Android malware detection and identification frameworks by leveraging the machine and deep learning techniques: A comprehensive review
  Telematics and Informatics Reports 2024 DOI: 10.1016/j.teler.2024.100130
  +Save
• Malware Detection Framework Based on Iterative Neighborhood Component Analysis for Internet of Medical Things
  2024 DOI: 10.1007/978-3-031-54547-4_8
  +Save
• XAI-AMD-DL: An Explainable AI Approach for Android Malware Detection System Using Deep Learning
  2023 IEEE World Conference on Applied Intelligence and Computing (AIC) 2023 DOI: 10.1109/aic57670.2023.10263974
  +Save
• AI-empowered malware detection system for industrial internet of things
  Computers and Electrical Engineering 2023 DOI: 10.1016/j.compeleceng.2023.108731
  +Save
• Analysis of Feature Selection Methods for Android Malware Detection Using Machine Learning Techniques
  Big Data Analytics in Fog-Enabled IoT Networks 2023 DOI: 10.1201/9781003264545-8
  +Save
• An optimized and efficient android malware detection framework for future sustainable computing
  Sustainable Energy Technologies and Assessments 2022 DOI: 10.1016/j.seta.2022.102852
  +Save
• Deep malware detection framework for IoT-based smart agriculture
  Computers and Electrical Engineering 2022 DOI: 10.1016/j.compeleceng.2022.108410
  +Save
• A Hybrid Feature Selection Approach-Based Android Malware Detection Framework Using Machine Learning Techniques
  2022 DOI: 10.1007/978-981-16-8664-1_30
  +Save
• A Study on Data Sharing Using Blockchain System and Its Challenges and Applications
  Advances in Malware and Data-Driven Network Security 2022 DOI: 10.4018/978-1-7998-7789-9.ch011
  +Save