Search for Articles:

MECS Press Menu

Chitra S.

Work place: Department of Computer Science, Bishop Heber College, Bharathidasan University, Trichy, Tamil Nadu, India

E-mail: chitrasathish1979@gmail.com

Website: https://orcid.org/0000-0002-1893-6469

Research Interests:

Biography

Chitra S., Ph.D. in Computer Science from Bharathidasan University in 2021. Prior to this, completed an M.Phil. in Computer Science from Kurinji College of Arts and Science in 2009, an MCA (Master of Computer Applications) from Bharathidasan University in 2002, and a B.Sc. in Mathematics from Srimathi Indira Gandhi College in 1999.
Work Experience: Over 17 years of teaching experience and additional industry experience in software development and administration. The professional career began as a Teacher at Sri Balaji Matriculation School, Tiruchirappalli, from 1 August 1999 to 31 May 2000. Subsequently, worked as a VB Programmer at RDS Solutions, Tiruchirappalli, from 3 January 2001 to 4 September 2002, followed by a role as Computer Staff at Jayaraman Chartered Accountant, Tiruchirappalli, from 5 September 2002 to 19 October 2004. Joined Srimad Andavan Arts and Science College, Tiruchirappalli, as an Assistant Professor on 17 January 2008 and served until 26 June 2023. Since 26 June 2023, working as an Assistant Professor in the Department of Computer Science at Bishop Heber College, Tiruchirappalli, contributing to teaching, research, curriculum development, and student mentoring.
Awards: Received several recognitions for academic and research excellence. In 2021, honored with the Rashtriya Nirman Ratna Award by The National Academy for Art Education, Pune, in recognition of outstanding contributions to education and research also received the Natramilasan Award at the State level from Tamil Sangam, Kulithalai, on 5 September 2021. In addition, granted an Indian Patent titled "IoT-Based Smart Public Food Distribution Management System to Control" (Patent No. 202141047733A), which was officially granted in October 2021, demonstrating innovative research contributions in the field of the Internet of Things (IoT) and smart public distribution systems.

Author Articles
Leveraging CNN-LSTM Networks for Real-Time Intrusion Detection and Classification in IoT

By Sabeena S. Chitra S.

DOI: https://doi.org/10.5815/ijwmt.2026.05.23, Pub. Date: 8 Oct. 2026

The IoT (Internet of Things) devices extend the attack surface for cybercriminals, requiring robust IDS (Intrusion Detection Systems). In order to tackle the issues, the AI (Artificial Intelligence), especially the ML (Machine Learning) and DL (Deep Learning) is incorporated into IoT IDS to analyze large datasets, identify complex patterns, and adapt to evolving threats. Hence, the study proposes a modified CNN-LSTM model for real-time intrusion detection and classification in IoT Device Network Logs. The proposed model utilizes the CNNs (Convolutional Neural Networks) for spatial feature extraction and LSTM (Long Short-Term Memory) networks for capturing temporal dependencies, augmenting the accuracy and detection efficiency. The proposed CNN-LSTM model enhances the real-time intrusion detection and contains the ability to detect developing attacks in dynamic IoT environments, which makes it highly flexible for large-scale deployments. The IoT Device Network Logs dataset is used for evaluating the proposed modified CNN-LSTM model. The modified CNN-LSTM model is assessed using the performance metrics such as accuracy, precision, recall, F1-score, time complexity and false alarm rate. As a result, the modified CNN-LSTM model achieves superior performance and earlier detection in contrast to the conventional models deliberating its potential for enhancing IoT security. 

[...] Read more.
Other Articles

Subscribe to receive issue release notifications and newsletters from MECS Press journals