Dr. Ritika Vivek Ladha

Designation: Assistant Professor

Biography:

Dr. Ritika Vivek Ladha is an Assistant Professor in the CSE (AI–ML) department at Adani University, where she has been serving since 2022. She completed her Ph.D. in Computer Science and Engineering from Nirma University in 2022. Her research focuses on network security, intrusion detection systems, deep learning, machine learning, and IoT security, with several highly cited publications in leading SCI-indexed journals. She is actively guiding one Ph.D. scholar and continues to contribute to advanced cybersecurity research. She has also completed CEH training from EC-Council and holds the Red Hat Certified System Administrator certification, strengthening her expertise in security-focused domains.

  • Ph.D in Computer Science and Engineering, Nirma University
  • M.Tech. in Computer Science and Engineering and specialization in Information and Network Security, Nirma University
  • B.E. in Computer Science, GTU

  • 2022-Present in Adani University
  • 2018-2022 as Research Scholar in Nirma University
  • 2015- 2018 as Assistant Professor in Silver Oak University

  • Network Security
  • Cyber Security
  • Machine Learning
  • Deep Learning

  1. A review on machine learning and deep learning perspectives of IDS for IoT: Recent updates, security issues, and challenges. Archives of Computational Methods in Engineering (2021)
  2. A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions Artificial Intelligence Review (2022)
  3. Fusion of statistical importance for feature selection in deep neural network-based intrusion detection system Information Fusion (2023)
  4. Attack classification using feature selection techniques: a comparative study Journal of Ambient Intelligence and Humanized Computing (2021)
  5. Attack classification of imbalanced intrusion data for IoT network using ensemble-learning-based deep neural network IEEE Internet of Things Journal (2023)
  6. Application domains, evaluation data sets, and research challenges of IoT: A systematic review IEEE Internet of Things Journal (2020)
  7. Role of swarm and evolutionary algorithms for intrusion detection system: A survey Swarm and Evolutionary Computation (2020)
  8. Hate speech detection: A comprehensive review of recent works
  9. Expert Systems (2024)
  10. A Review on Challenges and Future Research Directions for Machine Learning-Based Intrusion Detection System. Archives of Computational Methods in Engineering (2023)
  11. Analyzing fusion of regularization techniques in the deep learning‐based intrusion detection system International Journal of Intelligent Systems (2021)
  12. Survey on mobile forensics International Journal of Computer Applications (2015)
  13. Face recognition techniques: A survey for forensic applications International Journal of Advanced Research in Computer Engineering (2015)
  14. Privacy Preserving Data Mining: A Comprehensive Survey International Journal of Computer Applications (2017)
  15. A comprehensive survey on: quantum cryptography International Journal of Scientific Research (2015)

  1. A review of the advancement in intrusion detection datasets Procedia Computer Science (2020)
  2. Intrusion detection using deep neural network with antirectifier layer ACN 2020 Conference Proceedings (2021)
  3. A compendium on risk assessment of phishing attack using attack modeling techniques Procedia Computer Science (2024)
  4. Spectral unmixing with hyperspectral datasets of AVIRIS-NG 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (2017)
  5. ScrapLLM: Benchmarking Data Acquisition for Research in Hate Speech Detection 2025 IEEE 7th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA)
  6. A Comprehensive Assessment of Deep Learning Techniques for Attack Detection in Internet of Vehicles (IoV) Networks 2025 IEEE 13th International Conference on Information and Communication Networks)