Nahom M Birhan
Research Assistant- (ECE)- AI Security
Currently working as a Research Assistant at Old Dominion University, actively engaged in research on jailbreak and backdoor attacks and defenses in large language models (LLMs) and multimodal language model settings. I am a Machine Learning and AI Software Engineer with over five years of professional experience, specializing in applied machine learning for cybersecurity.
I am committed to ethical AI development and the innovative application of machine learning in the field of security.
Research Interests
My research focuses on the intersection of machine learning, artificial intelligence, and cybersecurity, with a particular emphasis on leveraging ML for cybersecurity applications and exploring the robustness and vulnerabilities of ML models. I'm interested in developing lightweight ML solutions for edge devices and IoT environments, addressing challenges in network security, anomaly detection, and impostor identification. My work extends to investigating email phishing detection, blockchain applications in healthcare data security, and novel ML applications in environmental monitoring. Through this research, I aim to enhance the security and efficiency of AI systems while deepening our understanding of their potential vulnerabilities and limitations in real-world, resource-constrained scenarios.
Research Areas
Research Projects
Jailbreak and Backdoor Attacks in LLMs and Multimodal LMs
Actively working on this research, not published yet
Impostor Detection in IoT Edge Sensor Networks
Developed lightweight LSTM model for edge devices, optimized using TensorFlow Lite quantization
Technologies: Raspberry PI, Arduino nano 33 BLE, TensorFlow/Lite, Python, C/C++
Email Phishing Detection in Africa
Analyzed African-specific phishing patterns, implemented and compared five ML models, mitigated overfitting
Technologies: Scikit-learn, TensorFlow, Numpy
Blockchain-Based Healthcare Data Security
Proposed system integrating blockchain and IPFS for secure medical records, compared IPFS and Amazon S3 access times.
Technologies: IPFS, AWS (IAM, S3), Blockchain concepts
Prime and Composite Classification
Created dataset of 2 million numbers, developed models using sequence models and 1D-CNN, analyzed performance
Technologies: Python, TensorFlow, NumPy, Google Colab
Smart Irrigation System (BSc. research)
Designed automated system with XBee sensor communication, integrated motors, pumps, and sensors, developed control algorithms
Technologies: Proteus, XBee, Arduino, C/C++, Various sensors