Highly Motivated graduate with 4.5 years of industry experience and a strong academic background. Passionate about hands-on learning, with excellent communication skills and a proactive approach. Experienced Automation Engineer skilled in programming, automated testing, and Cloud VM Provisioning Automation. Adept at collaborating on solutions and implementing automated testing methodologies, with a focus on best practices and process efficiency.
->Intrusion Detection Using Machine Learning Algorithms, Machine Learning, Deep Neural Network, SVM, Random Forest (RF).
->The goal of this project is to evaluate and compare the effectiveness of different machine learning algorithms for intrusion detection, specifically focusing on Deep Neural Networks (DNN), Support Vector Machines (SVM), and Random Forest (RF).
->Implement clustering and anomaly detection algorithms to uncover new, previously unseen threats, providing the IDS with the ability to detect novel and evolving attacks.
->Incorporate deep neural networks to further improve the system's ability to autonomously learn and adapt, continuously enhancing its detection capabilities and making it more effective at identifying both known and unknown intrusions.
->Develop an IDS that is capable of learning from new data in real-time, allowing it to stay ahead of emerging cyber threats and reduce the time taken to detect and respond to intrusions.