Improved SSL/TLS Man-in-the-middle attack detection technique using timing analysis and other behavioral anomalies

Even with the necessary protection that the TLS protocol is assumed to provide, data communications and financial transactions carried out online have been noted to be at great risk due to the impending danger of Man-in-the-middle attacks. This research was conducted to confirm the possibility of mitigating the continuous threat that attacks such as the man-in-the-middle constitute to the SSL and TLS key exchange by analyzing differences in time and other possible behavioral anomalies between a simulated attack and a standard SSL session through the use of machine learning. The results gotten in this research have been used to demonstrate the successful implementation of an improved SSL/TLS MiTM detection system