Real-Time Machine Learning Techniques in Robotics for Detecting Phishing Attacks

The wide application of robotics technology has been observed in this era, which made the lives of individuals using it convenient. However, the rapid evolution of cyber threats, especially in phishing attempts, has influenced cybercriminals to make it an attack target. To solve this problem, we proposed real-time machine learning (ML)-based techniques to effectively detect phishing activities in a robotic environment. Stochastic Gradient Descent (SGD) and Passive-Aggressive algorithms are used to enable an accurate real-time phishing detection. Even though the dataset is static, our proposed methodology has attempted to simulate prediction in real-time through processing the test samples in sequence, treating each instance received as if it is arriving in real-time. The performance of our ML-based real-time phishing detection in a robotics environment has shown promising results, achieving over 99.8% for both SGD and passive-aggressive classifiers.