Buffer overflow attacks data acquisition

In this abstract, we investigate the network traffic that may cause the unauthorized control of a computer in the campus network using buffer overflow attacks, the objective of which is to gain the control of privileged programs and computers. We provide statistics of the network traffic in a campus and an enterprise network together with probabilities of a buffer overflow attack to provide attakers the most vulnerable services using low interaction honeypot HoneyD together with a highly interactive shadow honeypot Argos that were used to detect attacks and describe their detection profiles. In this manner, we can collect data to be used for training classifiers to predict and detect even zero day vulnerabilities and malware. Our intension is to acquaint dataset that can identify serious security threats in much higher details, compared to 1999 KDD Cup dataset.