Email, as a primary communication service, plays a crucial role in communication, making it one of the most common targets of cyberattacks. Masquerade attacks, which involve impersonating legitimate users to obtain authentication credentials and gain unauthorized access to services, are particularly common. Since masquerade attacks use legitimate logins, their dwell-time behavior resembles normal users, making detection challenging. Further complicating the issue are the diverse email usage patterns, making the identification of detection results time-consuming and labor-intensive. To slove those problems, we propose a rule-based, self-training, semi-supervised model for masquerade attack detection. It analyzes four types of email logs and classifies usage scenarios. It uses an Auto-Encoder for semisupervised detection with limited labeled data, refining results through iterative self-training with coarse-grained rules. The framework is validated on email data from 6,736 users over three months, identifying 7 anomalous accounts and 12 anomalous IP addresses. Compared to the company's Security Operations Center security tools, the proposed framework detected 3 more anomalous accounts and 7 more anomalous IP while reducing suspicious accounts by 3.72 times and suspicious IP by 3.54 times.

Current moIecuIar bioIogy techniques were deveIoped primarily for characterization of single genes, not entire genomes, and, as such, are not ideally suited to high resolution analysis of complex traits and the moiecular genetics of very large populations.Despite rapid progress in the human genome project effort, there is little doubt that radicaIIy new conceptual approaches are needed before routine whole genome-based analyses can be undertaken by both basic research and clinical laboratories.Physical mapping of genomes, using restriction endonucleases, has played a major role in the identification and characterizing various loci, for example, by aiding clone contig formation and by characterizing genetic lesions.Restriction maps provide precise genomic distances, unlike ordered sequencebased landmarks such as Sequence Tagged Sites (ST%), that are essential for optimizing the efficiency of sequencing efforts, and for determining the spatial relationships of specific loci.When compared to tedious hybridization-based fingerprinting approaches, ordered restriction maps offer relatively unambiguous clone characterization that is useful in contig formation, establishment of minimal tiling paths for sequencing, and preliminary characterization of sequence lesions.In addition, such maps provide a useful scaffold for sequence assembly, often critical in the final sequence finishing stage.Despite the broad applications of restriction maps, the associated techniques for their generation have changed little over the last ten years, primarily because they still utilize electrophoretic analysis.To help overcome these shortcomings, our laboratory developed the first practical non-electrophoretic genomic mapping approach, Optical Mapping, to meet this need.Optical Mapping is a single molecule methodology for the rapid production of ordered restriction