X.509 certificate revocation defends against man-in-the-middle attacks involving a compromised certificate. Certificate revocation strategies face scalability, effectiveness, and deployment challenges as HTTPS adoption rates have soared. We propose Certificate Revocation Table (CRT), a new revocation strategy that is competitive with or exceeds alternative state-of-the-art solutions in effectiveness, efficiency, certificate growth scalability, mass revocation event scalability, revocation timeliness, privacy, and deployment requirements. The CRT periodically checks the revocation status of X.509 certificates recently used by an organization, such as clients on a university's private network. By prechecking the revocation status of each certificate the client is likely to use, the client can avoid the security problems of on-demand certificate revocation checking. To validate both the effectiveness and efficiency of using a CRT, we used 60 days of TLS traffic logs from Brigham Young University to measure the effects of actively refreshing certificates for various certificate working set window lengths. Using a certificate working set window size of 45 days, an average of 99.86% of the TLS handshakes from BYU would have revocation information cached in advance using our approach. Revocation status information can be initially downloaded by clients with a 6.7 MB file and then subsequently updated using only 205.1 KB of bandwidth daily. Updates to this CRT that only include revoked certificates require just 215 bytes of bandwidth per day.

Keeping computer networks safe from attack requires ever-increasing vigilance. Our work on applying locality to network intrusion detection is presented in this dissertation. Network servers that allow connections from both the internal network and the Internet are vulnerable to attack from all sides. Analysis of the behavior of incoming connections for properties of locality can be used to create a normal profile for such network servers. Intrusions can then be detected due to their abnormal behavior. Data was collected from a typical network server both under normal conditions and under specific attacks. Experiments show that connections to the server do in fact exhibit locality, and attacks on the server can be detected through their violation of locality. Key to the detection of locality is a data structure called a working-set, which is a kind of cache of certain data related to network connections. Under real network conditions, we have demonstrated that the working-set behaves in a manner consistent with locality. Determining the reasons for this behavior is our next goal. A model that generates synthetic traffic based on actual network traffic allows us to study basic traffic characteristics. Simulation of working-set processing of the synthetic traffic shows that it behaves much like actual traffic. Attacks inserted into a replay of the synthetic traffic produce working-set responses similar to those produced in actual traffic. In the future, our model can be used to further the development of intrusion detection strategies.