Dr. M.Chithik Raja
Faculty , Information Technology · University of Technology and Applied Sciences Salalah
Research Interests
Cyber Security, Data Analytics, Machine Learning, Deep Learning, Network Security, Ethical Hacking, Malware Analysis, Pattern recognition
Biography
Dr. Chithik Raja Mohamed Sinnaiya is a Lecturer at IT-Dept. CCIS,UTAS-SLL in Oman. He has a diverse academic background, holding a Ph.D. from AMET University, an M.E. from Anna University, an M.Sc. from M.K. University, and a B.Sc. from M.K. University. His professional experience spans several institutions, including Mekelle University in Ethiopia, Surya College of Engineering and Technology, Vickram College of Engineering, GTN Arts College, and Mary Matha Arts & Science College. Dr. Chithik’s research interests lie in computer science and cybernetics. He has published 28 journal articles, 5 books, and 2 conference proceedings. His work has garnered 26 citations and an h-index of 3. He has also been recognized with awards such as the Most Outstanding Educator and Researcher by the International Organization of Educators and Researchers in 2024 and the Best Student Mentor by the University of Technology and Applied Sciences Salalah in 2023. In addition to his academic and res
Education
Ph.D. in the Faculty of Engineering and Technology from AMET University in January 2022, with a dissertation titled “Studies On Hybrid Machine Learning Algorithms For Intruder Detection.” M.E. from Anna University, an M.Sc. from Wakf Board College, under Madurai Kamaraj University and a B.Sc. from H.K.R.H. College, also affiliated with Madurai Kamaraj University, Higher Secondary Certificate (HSC) in Bio-Maths from V.M.G.H.S. School, Secondary School Leaving Certificate (SSLC) from the V.M.G.H.S. school,
Recent Scopus Publications
- Analysing Cyber Attacks on Personal Data and Mitigation Using AI-Driven Techniques
- Behavioral Anomaly Detection in Big Data Streams: An Attention-Enhanced LSTM Approach for Privacy-Aware Zero-Day Attack Detection
- AI-Driven Helmet Detection in Construction Zones: A YOLOv8X Approach for Sustainable Safety Monitoring
- Measuring Marketing Effectiveness and Return on Investment
- Breast Cancer Diagnosis from Ultrasonic Image and Histopathology Image Using Deep Learning Approach
Links
- ORCID https://orcid.org/0000-0003-3035-0709
- Google Scholar https://scholar.google.com/citations?user=hQqmVMMAAAAJ
- Scopus https://www.scopus.com/authid/detail.uri?authorId=56737226400