Dr. Aditya Kumar Shukla
Also known as: A. K. Shukla, Shukla, A.K., Dr. Aditya Kumar Shukla, Dr. A. K. Shukla
CISCO System, GLA University
0000-0002-2992-1781
About
Dr. Aditya Kumar Shukla is an academic scholar and technology professional who earned his Ph.D. from GLA University, Mathura, in 2025. He completed his doctoral research in the Department of Computer Engineering and Applications while concurrently working as a Technical Leader at Cisco in Noida, Uttar Pradesh.
His doctoral research, conducted under the supervision of Dr. Ashish Sharma, focused on “Security Attack Detection in Heterogeneous Cloud Environments through Machine Learning and Cryptography.” His research interests include cloud computing security, DDoS attack detection, multivariate neural models, machine learning, and cryptographic security. He has published several Scopus- and SCI-indexed research papers, including research in the International Journal of Communication Networks and Information Security.
Professionally, Dr. Shukla serves as a Technical Owner and software architect for Cisco AppDynamics AI and observability platforms. His responsibilities span the Cognition Engine ML pipeline, AI Assistant, ATD services, anomaly detection, RCA, Kafka-based systems, RAG, and cloud-native architectures.
He has extensive experience designing solutions across SaaS, Virtual Appliance, Classic On-Premises, and BYOK environments, with expertise in Kubernetes, Helm, Kafka, MySQL, Redis, Java, Python, and Azure OpenAI. His work also focuses on secure delivery, vulnerability remediation, code quality, test automation, production reliability, and cross-functional technical leadership.
Employment
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CISCO System Technical Leader2013 - Present
Education
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GLA University PHD2020 - 2025
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GLA University MCA2004 - 2007
Projects & Funding
Projects & funding information is unavailable.
Publications (17)
- SACFFNet: Shuffle Attention Convolutional Forward Fractional Network Based Attack Detection in Cloud Computing Save
- A Hybrid Machine Learning and Large Language Model Framework for Real-Time DDos Detection and Mitigation With Explainability Save
- Early Cloud Computing Intrusion Detection Using Time Series Data: A Multivariate Neural Model and Improved Zebra Optimization Algorithm Approach Save
- ENHANCING SECURITY RESILIENCE: DYNAMIC DRIFT DETECTION IN CLOUD-BASED INTRUSION DETECTION USING THE HYBRID MODEL Save
- Cloud Data Security by Hybrid Machine Learning and Cryptosystem Approach Save
- Reduce Low-Frequency Distributed Denial of Service Threats by Combining Deep and Active Learning Save
- A Bio-Inspired Feature Selection and Ensemble Classification for DDoS Detection in Cloud Save
- Cloud Based DDOS Attack Detection in a Distributed and Collaborative Manner Using Deep Neural Networks Save
- Enhancing DDoS Attack Detection with Multi-Layer Perceptron Algorithms: A Machine Learning Approach Using the CICDDoS2019 Dataset Save
- Reduce Low-Frequency Distributed Denial of Service Threats by Combining Deep and Active Learning Save