System provenance forensic analysis has been studied by a large body of research work. This area needs fine granularity data such as system calls along with event fields to track the dependencies of events. While prior work on security datasets has been proposed, we found a useful dataset of realistic attacks and details that can be used for provenance tracking is lacking. We created a new dataset of eleven vulnerable cases for system forensic analysis. It includes the full details of system calls including syscall parameters. Realistic attack scenarios with real software vulnerabilities and exploits are used. Also, we created two sets of benign and adversary scenarios which are manually labeled for supervised machine-learning analysis. We demonstrate the details of the dataset events and dependency analysis.

Programming courses in colleges often involve a myriad of coding assignments, which brings heavy grading workloads for instructors. To alleviate this problem, automatic programming evaluation tools are becoming more of a requirement than an option. However, after considering the actual requirements in our teaching practice, we have noticed that the current solutions still suffer from shortcomings and limitations. In the process of addressing the challenges, we propose and implement a brand new code assessment application based on PowerShell, which shows both extendibility and configurability. In particular, we integrate both black-box testing and the lexical analysis into the system, thus achieving a customized solution to meet specific requirements. This paper presents the architecture and design of our automatic code assessment application. Furthermore, we conduct empirical evaluations on the proposed system following the Technology Acceptance Model, and also investigate the drawbacks of manual assessment of coding assignments in terms of reliability and fairness. Finally, the evaluations demonstrate the effectiveness of our proposed auto-grader in facilitating the code assessment targeting college-level programming courses.

When establishing lightpaths in an all-optical DWDM network, it is possible that concurrent lightpaths requests will block one another if the lightpaths attempt to reserve the same wavelength of the same link. In this paper, we propose a novel signaling mechanism, referred to as label prioritization, which attempts to reduce the backward-link blocking in GMPLS-centric all-optical networks by assigning different priorities to the suggested wavelengths of each connection request. The prioritization of the wavelength encourages concurrent lightpath requests to choose different wavelengths, thereby reducing the possibility that the requests will be blocked. The label prioritization mechanism consists of a signaling extension to GMPLS to support the label prioritization and a modification in the optical switch controller to support the signaling extension. Simulation results show that the label prioritization method can effectively reduce wavelength conflicts.