Hey! I’m Hrithika,
an AI Developer and Researcher based in India.
Hey! I’m Hrithika,
an AI Developer and Researcher based in India.



About Me
I am currently in the final year of my M.Tech in Computer Science at NMAMIT, Karkala. My interest in cybersecurity began with the increasing frequency of global security breaches, which inspired me to develop a patented intrusion detection and mitigation system. This work is designed to support both cybersecurity professionals and everyday users across domains such as IoT and smart vehicles.
While I am early in my journey with AI and cybersecurity, I am deeply committed to learning, growing, and contributing through applied research. I have mentored over 50 students through workshops and hackathons, helping them build skills in AI and machine learning. I am passionate about translating research into scalable solutions that address real-world problems.
Outside of development, I enjoy exploring emerging technology across disciplines, learning how innovation shapes different fields, and helping others navigate the world of tech. I’m especially drawn to ideas that bridge science and society, and I find purpose in mentoring, creating, and staying curious.
Projects
SmartIDS: Real-Time Intrusion Detection and Mitigation System
SmartIDS is a patented, AI-powered intrusion detection system developed as part of my research under VigilAInt. It combines convolutional neural networks, transformers, and reinforcement learning to detect and respond to network threats in real time through visual representations, enabling the model to capture temporal and spatial patterns effectively. The integration of LLM is to dynamically generate dashboard content, contextual insights, and automated mitigation procedures based on detected threats. It is aimed to support cybersecurity professionals and everyday users across various domains.

SmartIDS: Real-Time Intrusion Detection and Mitigation System
SmartIDS is a patented, AI-powered intrusion detection system developed as part of my research under VigilAInt. It combines convolutional neural networks, transformers, and reinforcement learning to detect and respond to network threats in real time through visual representations, enabling the model to capture temporal and spatial patterns effectively. The integration of LLM is to dynamically generate dashboard content, contextual insights, and automated mitigation procedures based on detected threats. It is aimed to support cybersecurity professionals and everyday users across various domains.

SmartIDS: Real-Time Intrusion Detection and Mitigation System
SmartIDS is a patented, AI-powered intrusion detection system developed as part of my research under VigilAInt. It combines convolutional neural networks, transformers, and reinforcement learning to detect and respond to network threats in real time through visual representations, enabling the model to capture temporal and spatial patterns effectively. The integration of LLM is to dynamically generate dashboard content, contextual insights, and automated mitigation procedures based on detected threats. It is aimed to support cybersecurity professionals and everyday users across various domains.

Lung X-ray Classification: COVID-19, Pneumonia, and Normal
This project explored deep learning techniques for medical image classification, focusing on detecting COVID-19, pneumonia, and healthy lungs using chest X-rays. Multiple CNN architectures including VGG16, ResNet50, MobileNet, and EfficientNetB0 were implemented and compared. EfficientNetB0 achieved the highest accuracy of 93.93%. The work involved preprocessing medical imaging datasets, model evaluation, and leading a 3-member team. This project deepened my understanding of convolutional architectures in biomedical applications and showcased how AI can assist in clinical diagnostics.

Lung X-ray Classification: COVID-19, Pneumonia, and Normal
This project explored deep learning techniques for medical image classification, focusing on detecting COVID-19, pneumonia, and healthy lungs using chest X-rays. Multiple CNN architectures including VGG16, ResNet50, MobileNet, and EfficientNetB0 were implemented and compared. EfficientNetB0 achieved the highest accuracy of 93.93%. The work involved preprocessing medical imaging datasets, model evaluation, and leading a 3-member team. This project deepened my understanding of convolutional architectures in biomedical applications and showcased how AI can assist in clinical diagnostics.

Lung X-ray Classification: COVID-19, Pneumonia, and Normal
Web design is the art and science of creating visually appealing and functional websites that provide seamless user experiences. By blending aesthetics, usability, and cutting-edge technology, web design shapes how individuals interact with digital spaces. It encompasses layout design, typography, color schemes, and responsive frameworks, ensuring websites look and perform flawlessly across devices. Whether for businesses, personal portfolios, or e-commerce platforms

Radiosensitivity Risk Analysis for Cancer Treatment
As part of academic research team, I helped design and develop a full-stack clinical system aimed at streamlining diagnostic workflows in oncology. The platform analyzes radiosensitivity-related risk parameters using the biomarker Telomere, enabling more personalized treatment strategies by assessing overall health and tailoring medication plans accordingly. The system automates diagnostic report generation and ensures secure delivery via email, reducing manual intervention and improving efficiency. The platform provides real-time access to medical data for both doctors and patients through a secure interface.

Radiosensitivity Risk Analysis for Cancer Treatment
As part of academic research team, I helped design and develop a full-stack clinical system aimed at streamlining diagnostic workflows in oncology. The platform analyzes radiosensitivity-related risk parameters using the biomarker Telomere, enabling more personalized treatment strategies by assessing overall health and tailoring medication plans accordingly. The system automates diagnostic report generation and ensures secure delivery via email, reducing manual intervention and improving efficiency. The platform provides real-time access to medical data for both doctors and patients through a secure interface.

Radiosensitivity Risk Analysis for Cancer Treatment
As part of academic research team, I helped design and develop a full-stack clinical system aimed at streamlining diagnostic workflows in oncology. The platform analyzes radiosensitivity-related risk parameters using the biomarker Telomere, enabling more personalized treatment strategies by assessing overall health and tailoring medication plans accordingly. The system automates diagnostic report generation and ensures secure delivery via email, reducing manual intervention and improving efficiency. The platform provides real-time access to medical data for both doctors and patients through a secure interface.

YOLO-Based Object Recognition with Distance Estimation to Assist the Visually Impaired
This prototype system combines YOLOv3 object detection with geometric estimation techniques to detect objects and approximate their real-time distance from the camera. The model was trained on everyday objects like phones, bottles, and bags, achieving around 80% accuracy in distance approximation. The solution was designed with accessibility in mind, particularly for the visually impaired, enabling spatial awareness and object recognition in daily environments. This project helped me understand camera calibration, bounding box annotation, and computer vision pipelines.

YOLO-Based Object Recognition with Distance Estimation to Assist the Visually Impaired
This prototype system combines YOLOv3 object detection with geometric estimation techniques to detect objects and approximate their real-time distance from the camera. The model was trained on everyday objects like phones, bottles, and bags, achieving around 80% accuracy in distance approximation. The solution was designed with accessibility in mind, particularly for the visually impaired, enabling spatial awareness and object recognition in daily environments. This project helped me understand camera calibration, bounding box annotation, and computer vision pipelines.

YOLO-Based Object Recognition with Distance Estimation to Assist the Visually Impaired
This prototype system combines YOLOv3 object detection with geometric estimation techniques to detect objects and approximate their real-time distance from the camera. The model was trained on everyday objects like phones, bottles, and bags, achieving around 80% accuracy in distance approximation. The solution was designed with accessibility in mind, particularly for the visually impaired, enabling spatial awareness and object recognition in daily environments. This project helped me understand camera calibration, bounding box annotation, and computer vision pipelines.

Framer developer
A Framer developer specializes in building interactive and visually stunning digital experiences using Framer, a powerful design and prototyping tool. Combining creative design principles with advanced coding skills, they craft responsive websites, animations, and prototypes that bring ideas to life. With a focus on seamless interactions and performance, Framer developers bridge the gap between design and development, enabling teams to iterate quickly and deliver polished, user-centric products.

Patents
A Hybrid CNN-Transformer Powered Intrusion Detection System and Method Thereof
Status: Published – Grant Pending
Publication Date : 16/05/2025
Introduces a real-time intrusion detection system that combines CNNs and transformers with visual traffic representation for scalable threat detection.
Real-Time Deep Learning and Reinforcement-Based Intrusion Detection System and Method Thereof
Status: Published – Grant Pending
Publication Date : 16/05/2025
Proposes an adaptive IDS using deep learning and reinforcement learning to improve detection accuracy in evolving attack environments.
Network Intrusion Detection System
Status: Published – Grant Pending
Publication Date : 23/05/2025
Presents a modular intrusion detection framework designed for flexible deployment across various network infrastructures.
A Hybrid CNN-Transformer Powered Intrusion Detection System and Method Thereof
Status: Published – Grant Pending
Publication Date : 16/05/2025
Introduces a real-time intrusion detection system that combines CNNs and transformers with visual traffic representation for scalable threat detection.
Real-Time Deep Learning and Reinforcement-Based Intrusion Detection System and Method Thereof
Status: Published – Grant Pending
Publication Date : 16/05/2025
Proposes an adaptive IDS using deep learning and reinforcement learning to improve detection accuracy in evolving attack environments.
Network Intrusion Detection System
Status: Published – Grant Pending
Publication Date : 23/05/2025
Presents a modular intrusion detection framework designed for flexible deployment across various network infrastructures.
A Hybrid CNN-Transformer Powered Intrusion Detection System and Method Thereof
Status: Published – Grant Pending
Publication Date : 16/05/2025
Introduces a real-time intrusion detection system that combines CNNs and transformers with visual traffic representation for scalable threat detection.
Real-Time Deep Learning and Reinforcement-Based Intrusion Detection System and Method Thereof
Status: Published – Grant Pending
Publication Date : 16/05/2025
Proposes an adaptive IDS using deep learning and reinforcement learning to improve detection accuracy in evolving attack environments.
Network Intrusion Detection System
Status: Published – Grant Pending
Publication Date : 23/05/2025
Presents a modular intrusion detection framework designed for flexible deployment across various network infrastructures.
A Hybrid CNN-Transformer Powered Intrusion Detection System and Method Thereof
Status: Published – Grant Pending
Publication Date : 16/05/2025
Introduces a real-time intrusion detection system that combines CNNs and transformers with visual traffic representation for scalable threat detection.
Real-Time Deep Learning and Reinforcement-Based Intrusion Detection System and Method Thereof
Status: Published – Grant Pending
Publication Date : 16/05/2025
Proposes an adaptive IDS using deep learning and reinforcement learning to improve detection accuracy in evolving attack environments.
Network Intrusion Detection System
Status: Published – Grant Pending
Publication Date : 23/05/2025
Presents a modular intrusion detection framework designed for flexible deployment across various network infrastructures.
A Hybrid CNN-Transformer Powered Intrusion Detection System and Method Thereof
Status: Published – Grant Pending
Publication Date : 16/05/2025
Introduces a real-time intrusion detection system that combines CNNs and transformers with visual traffic representation for scalable threat detection.
Real-Time Deep Learning and Reinforcement-Based Intrusion Detection System and Method Thereof
Status: Published – Grant Pending
Publication Date : 16/05/2025
Proposes an adaptive IDS using deep learning and reinforcement learning to improve detection accuracy in evolving attack environments.
Network Intrusion Detection System
Status: Published – Grant Pending
Publication Date : 23/05/2025
Presents a modular intrusion detection framework designed for flexible deployment across various network infrastructures.
A Hybrid CNN-Transformer Powered Intrusion Detection System and Method Thereof
Status: Published – Grant Pending
Publication Date : 16/05/2025
Introduces a real-time intrusion detection system that combines CNNs and transformers with visual traffic representation for scalable threat detection.
Real-Time Deep Learning and Reinforcement-Based Intrusion Detection System and Method Thereof
Status: Published – Grant Pending
Publication Date : 16/05/2025
Proposes an adaptive IDS using deep learning and reinforcement learning to improve detection accuracy in evolving attack environments.
Network Intrusion Detection System
Status: Published – Grant Pending
Publication Date : 23/05/2025
Presents a modular intrusion detection framework designed for flexible deployment across various network infrastructures.
A Hybrid CNN-Transformer Powered Intrusion Detection System and Method Thereof
Status: Published – Grant Pending
Publication Date : 16/05/2025
Introduces a real-time intrusion detection system that combines CNNs and transformers with visual traffic representation for scalable threat detection.
Real-Time Deep Learning and Reinforcement-Based Intrusion Detection System and Method Thereof
Status: Published – Grant Pending
Publication Date : 16/05/2025
Proposes an adaptive IDS using deep learning and reinforcement learning to improve detection accuracy in evolving attack environments.
Network Intrusion Detection System
Status: Published – Grant Pending
Publication Date : 23/05/2025
Presents a modular intrusion detection framework designed for flexible deployment across various network infrastructures.
A Hybrid CNN-Transformer Powered Intrusion Detection System and Method Thereof
Status: Published – Grant Pending
Publication Date : 16/05/2025
Introduces a real-time intrusion detection system that combines CNNs and transformers with visual traffic representation for scalable threat detection.
Real-Time Deep Learning and Reinforcement-Based Intrusion Detection System and Method Thereof
Status: Published – Grant Pending
Publication Date : 16/05/2025
Proposes an adaptive IDS using deep learning and reinforcement learning to improve detection accuracy in evolving attack environments.
Network Intrusion Detection System
Status: Published – Grant Pending
Publication Date : 23/05/2025
Presents a modular intrusion detection framework designed for flexible deployment across various network infrastructures.
Publications
SmartIDS
Journal Paper – In Preparation for IEEE Open Access (Q1)
This upcoming paper will present the complete implementation of SmartIDS, a patented AI-based intrusion detection system that integrates CNNs, Transformers, reinforcement learning, and visual signal transformation techniques. The paper benchmarks SmartIDS across five large-scale datasets and includes deployment feasibility and LLM-based threat mitigation.
IoT Intrusion: Detection Methods and Mitigation Strategies
Conference Paper – IEEE AIDE 2025
DOI: 10.1109/AIDE64228.2025.10987325
This paper presents a comparative study of deep learning techniques for IoT intrusion detection, synthesized from over 50 research sources. It proposes a lightweight, adaptable framework for securing smart environments.
Radiosensitivity and Risk Analysis for Cancer Treatment
Journal Paper – IJNRD, Vol. 8, Issue 5, 2023
https://www.ijnrd.org/papers/IJNRD2305147.pdf
This publication introduces a full-stack system to analyze telomere-based radiosensitivity biomarkers and automate secure report distribution for oncology clinics. The system enhances personalized care through risk-driven treatment planning.
SmartIDS
Journal Paper – In Preparation for IEEE Open Access (Q1)
This upcoming paper will present the complete implementation of SmartIDS, a patented AI-based intrusion detection system that integrates CNNs, Transformers, reinforcement learning, and visual signal transformation techniques. The paper benchmarks SmartIDS across five large-scale datasets and includes deployment feasibility and LLM-based threat mitigation.
IoT Intrusion: Detection Methods and Mitigation Strategies
Conference Paper – IEEE AIDE 2025
DOI: 10.1109/AIDE64228.2025.10987325
This paper presents a comparative study of deep learning techniques for IoT intrusion detection, synthesized from over 50 research sources. It proposes a lightweight, adaptable framework for securing smart environments.
Radiosensitivity and Risk Analysis for Cancer Treatment
Journal Paper – IJNRD, Vol. 8, Issue 5, 2023
https://www.ijnrd.org/papers/IJNRD2305147.pdf
This publication introduces a full-stack system to analyze telomere-based radiosensitivity biomarkers and automate secure report distribution for oncology clinics. The system enhances personalized care through risk-driven treatment planning.
SmartIDS
Journal Paper – In Preparation for IEEE Open Access (Q1)
This upcoming paper will present the complete implementation of SmartIDS, a patented AI-based intrusion detection system that integrates CNNs, Transformers, reinforcement learning, and visual signal transformation techniques. The paper benchmarks SmartIDS across five large-scale datasets and includes deployment feasibility and LLM-based threat mitigation.
IoT Intrusion: Detection Methods and Mitigation Strategies
Conference Paper – IEEE AIDE 2025
DOI: 10.1109/AIDE64228.2025.10987325
This paper presents a comparative study of deep learning techniques for IoT intrusion detection, synthesized from over 50 research sources. It proposes a lightweight, adaptable framework for securing smart environments.
Radiosensitivity and Risk Analysis for Cancer Treatment
Journal Paper – IJNRD, Vol. 8, Issue 5, 2023
https://www.ijnrd.org/papers/IJNRD2305147.pdf
This publication introduces a full-stack system to analyze telomere-based radiosensitivity biomarkers and automate secure report distribution for oncology clinics. The system enhances personalized care through risk-driven treatment planning.