Dr. BALAJI KANNAN

@vistas.ac.in

ASSISTANT PROFESSOR- SCHOOL OF COMPUTING SCIENCES, COMPUTER SCIENCE AND INFROMATION TECHNOLOGY
VELS UNIVERSITY

Dr. BALAJI KANNAN


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https://researchid.co/balajikannan
Dr. Balaji Kannan is a distinguished faculty member in the Department of Computer Science at Vels University, Chennai. He holds a Ph.D. in Computer Science and is dedicated to academic excellence, research, and innovation in the field. His teaching and mentoring have contributed significantly to developing students’ technical and analytical skills. Dr. Balaji Kannan’s professional interests include emerging technologies and their applications in solving real-world challenges. He continues to inspire learners through his commitment to knowledge, research, and the advancement of computer science education.

EDUCATION

Academic Qualification
S.No Degree University / College Year of Passing
1 PhD Computer Science

Bharathidasan University, Tiruchirappalli/ Adaikalamatha College, Thanjavur, 2017-2023
2 ME (Computer Science and Engineering) Anna University, Chennai/ Arunai Engineering College, Tiruvannamalai, 2013-2015
3 M.Phil (Computer Science) Vinayaka Mission University, Salem. 2008-2009
4 MCA (Master of Computer Applications) University, Chennai. 2003-2006
5 B.Sc (Computer Science) Thanthai Hans Roever College, Perambalur, Bharathidasan University, Tiruchirappalli. 2000-2003
Additional Qualification
6 MBA( HR) Master of Business Administration University of Madras, Chennai. 2008-2010
7 DLL (Diploma in Labour Law) University of Madras, Chennai. 2008-2009

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Artificial Intelligence, Computer Science Applications, Computer Networks and Communications
6

Scopus Publications

15

Scholar Citations

2

Scholar h-index

Scopus Publications

  • MitigAInt A Review and Framework for AI-Based Threat Response in Cloud Infrastructure
    S. Saravana Kumar, Balaji Kannan
    2025 World Skills Conference on Universal Data Analytics and Sciences Worldsuas 2025, 2025
    The growing dependability on the cloud infrastructure in various sectors has also ushered in a greater security challenge due to the widened storm that has been consequently experienced to affect traditional security models. At that, the approach of including Artificial Intelligence (AI) into cybersecurity methods, hereinafter referred to as "MitigAInt" in the context of this review, can lead to potentially fruitful directions of threat prevention and agile protection. The given paper will review AI-based threat response systems in cloud in a comprehensive manner and will review the available learning on the topic and propose the modular framework (MitigAInt) that is expected to strengthen resilience, scalability, and responsiveness. MitigAInt focuses on hybrid AI and its mix of machine learning, deep learning and reinforcement learning by incorporating anomaly detection in real-time, predictive analytics, and autonomous decision making. The suggested approach provides the data ingestion pipelines, the layers of hostile identification, and response automation modules. Previous studies and analyses of current models support the effectiveness of AI in minimizing the effect of a breach, the time it takes to respond to the breach, and the occurrence of false positives. Lastly, in this paper, we have looked at the limitation, ethical considerations, and future prospects of developing more intelligent, explainable, and trustworthy AI-based cloud infrastructure security.
  • Enhancing Industrial IoT Security with AI and Cloud Computing: A Review of Threats and Solutions
    S. Saravana Kumar, Balaji Kannan
    Proceedings of International Conference on Modern Sustainable Systems Cmss 2025, 2025
    Installing and using the Industrial Internet of Things (IIoT) technology at a higher rate has led to enormous changes in the industrial activities and a paradigm shift in the way industries operate, as well as in the manner in which management of assets and supply chain management is done. The digitalisation process allows us to collect data in real-time, have a predictive maintenance, and implement smart automation, which significantly increases operational efficiency and productivity. But this fast transformation has opened the door to a major issue of cybersecurity that poses a risk to integrity, confidentiality, and availability of important industrial infrastructure. An enlargement of networked things and technologies raises the attack surface such that IIoT ecosystems experience a more significant risk of being attacked by OT, like data breaches, ransomware, industrial espionage. The present paper is a detailed account of the key security threats affecting IIoT settings, which is then followed by a detailed analysis of how the features of artificial intelligence (AI) solutions and cloud-based platforms could be used to improve threat detection, incident response, and system resiliency. The combination of an analysis of recent threat vectors, new vulnerabilities, and new defense capabilities allows proposing a framework of best practices in securing IIoT infrastructure, within the study. Although AI-based threat detection and cloud-based security orchestration have become prime technological solutions, the results of further research and optimization are required to establish a workable and scalable implementation of security.
  • Analysis on intelligent DevOps by using AI-Powered automation for cloud application management
    G Divya, Kasarla Priyanka, L Chandra Sekhar Reddy, Shaik Munawar, M.Mohammed Ibrahim, Balaji Kannan
    Icrteect 2025 2nd International Conference on Recent Trends in Electrical Electronics and Computing Technologies, 2025
    The combination of greater cloud computing pace and DevOps adoption allowed software deployment and monitoring as well as scaling to undergo automated enhancement. The current DevOps approaches using human intervention rules during automation process cause management inefficiencies in complex cloud-native systems. The research explores the integration of Machine Learning with Artificial Intelligence features into DevOps methodologies to develop autonomous healing capabilities through combining predictive diagnostic information with automated problem-fixing systems. When AI technologies are integrated under the AIOps framework companies acquire automatic anomaly detection tools and predictive resources management features and system failure assessment tools which improve their CI and CD deployment processes. The implementation of artificial intelligence-based monitoring systems enables organizations to receive genuine performance data and security protection functionality and cloud application scaling features. Automated correction systems initiated through programming helped identify sources of problems while reducing operational costs and system uptime losses.A DevOps platform created through AI automation enables deployment management and monitoring in addition to incident resolution as cloud-native functions. The analysis presents relevant research data and experimental evidence to prove how deployment operations benefit from AI and system reliability increases alongside the reduction of operational costs. Administrative roles experience substantial performance improvement through DevOps automation that employs AI because the system removes manual work and generates consistent reliable operations. The results indicate that cloud-native application control will utilize AI-driven DevOps that permits autonomous management along with self-adaptive operation of cloud platforms.
  • Enhancing Enterprise Network Security with Machine Learning: An In-Depth Analysis of Advanced Persistent Threat Detection
    G. Gowthami, C. Sadhana, S. Silvia Priscila, Sangeetha Radhakrishnan, M. SakthiVanitha, Balaji Kannan
    Lecture Notes in Networks and Systems, 2025
  • Artificial Intelligence based System to Prevent Animal Accidents in the Railway Tracks
    Balaji Kannan, R.Bagavathi Lakshmi, M. Sakthivanitha, R. Maruthi
    Proceedings 2024 8th International Conference on Inventive Systems and Control Icisc 2024, 2024
    The wildlife population has been in danger due to frequent animal accidents on railway tracks. In India, animal train collisions cause an average of 20 elephant deaths every year all over the nation. At the same time railway departments are taking necessary steps to identify the cause and implement prevention methods. Despite this, lots of accidents are happening in the railway tracks near the forest areas. This problem has been addressed by proposing a well-defined technological mechanism to alert the animals to be away from the railway tracks to reduce the wildlife deaths. In this study, Machine Learning(ML) frameworks are employed to detect the animals that are coming near the railway tracks and the developed module will provide appropriate warning or signals to the animals as well as to the loco pilot.
  • Prediction of Cyber Attacks Utilizing Deep Learning Model using Network/Web Traffic Data
    Balaji Kannan, M. Sakthivanitha, S. Jayashree, R. Maruthi
    Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing Icaaic 2024, 2024
    Nowadays, cyber-attacks are growing predominantly due to the development of technologies. It will lead to financial losses to a company and the other problems related to attacks. It is very important to predict such attacks from outsiders to safeguard our networking systems to provide effective security. The Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) techniques leverage enormous amounts of data to identify the cyber attacks. These learning approaches are used to identify a broad range of cyber-attacks by analyzing the web traffic or network traffic to identify potential threats such as malware, network intrusions and other types of attacks. The study demonstrates the various deep learning methods to predict the anomalies and other potential threats with more accuracy in real time.

RECENT SCHOLAR PUBLICATIONS

  • MitigAInt A Review and Framework for AI-Based Threat Response in Cloud Infrastructure
    SS Kumar, B Kannan
    2025 World Skills Conference on Universal Data Analytics and Sciences … , 2025
    2025
    Citations: 1
  • Enhancing Industrial IoT Security with AI and Cloud Computing: A Review of Threats and Solutions
    SS Kumar, B Kannan
    2025 International Conference on Modern Sustainable Systems (CMSS), 1082-1089 , 2025
    2025
  • Enhancing Enterprise Network Security with Machine Learning: An In-Depth Analysis of Advanced Persistent Threat Detection
    G Gowthami, C Sadhana, S Silvia Priscila, S Radhakrishnan, ...
    International Conference on Computing and Communication Networks, 525-537 , 2024
    2024
  • Artificial intelligence based system to prevent animal accidents in the railway tracks
    B Kannan, RB Lakshmi, M Sakthivanitha, R Maruthi
    2024 8th International Conference on Inventive Systems and Control (ICISC … , 2024
    2024
    Citations: 8
  • Prediction of cyber attacks utilizing deep learning model using network/web traffic data
    B Kannan, M Sakthivanitha, S Jayashree, R Maruthi
    2024 3rd International Conference on Applied Artificial Intelligence and … , 2024
    2024
    Citations: 6

MOST CITED SCHOLAR PUBLICATIONS

  • Artificial intelligence based system to prevent animal accidents in the railway tracks
    B Kannan, RB Lakshmi, M Sakthivanitha, R Maruthi
    2024 8th International Conference on Inventive Systems and Control (ICISC … , 2024
    2024
    Citations: 8
  • Prediction of cyber attacks utilizing deep learning model using network/web traffic data
    B Kannan, M Sakthivanitha, S Jayashree, R Maruthi
    2024 3rd International Conference on Applied Artificial Intelligence and … , 2024
    2024
    Citations: 6
  • MitigAInt A Review and Framework for AI-Based Threat Response in Cloud Infrastructure
    SS Kumar, B Kannan
    2025 World Skills Conference on Universal Data Analytics and Sciences … , 2025
    2025
    Citations: 1
  • Enhancing Industrial IoT Security with AI and Cloud Computing: A Review of Threats and Solutions
    SS Kumar, B Kannan
    2025 International Conference on Modern Sustainable Systems (CMSS), 1082-1089 , 2025
    2025
  • Enhancing Enterprise Network Security with Machine Learning: An In-Depth Analysis of Advanced Persistent Threat Detection
    G Gowthami, C Sadhana, S Silvia Priscila, S Radhakrishnan, ...
    International Conference on Computing and Communication Networks, 525-537 , 2024
    2024