Nowadays, in almost every computer system, log files are used to keep records of occurring events. Those log files are then used for analyzing and debugging system failures. Due to this important utility, researchers have worked on finding fast and efficient ways to detect anomalies in a computer system by analyzing its log records. Research in log-based anomaly detection can be divided into two main categories: batch log-based anomaly detection and streaming logbased anomaly detection. Batch log-based anomaly detection is computationally heavy and does not allow us to instantaneously detect anomalies. On the other hand, streaming anomaly detection allows for immediate alert. However, current streaming approaches are mainly supervised. In this work, we propose a fully unsupervised framework which can detect anomalies in real time. We test our framework on hdfs log files and successfully detect anomalies with an F- 1 score of 83%.
","abstract_html":"<p>Nowadays, in almost every computer system, log files are used to keep records of occurring events. Those log files are then used for analyzing and debugging system failures. Due to this important utility, researchers have worked on finding fast and efficient ways to detect anomalies in a computer system by analyzing its log records. Research in log-based anomaly detection can be divided into two main categories: batch log-based anomaly detection and streaming logbased anomaly detection. Batch log-based anomaly detection is computationally heavy and does not allow us to instantaneously detect anomalies. On the other hand, streaming anomaly detection allows for immediate alert. However, current streaming approaches are mainly supervised. In this work, we propose a fully unsupervised framework which can detect anomalies in real time. We test our framework on hdfs log files and successfully detect anomalies with an F- 1 score of 83%.</p>","abstract_has_math":false,"creators":["Zeufack, Vannel"],"institution":null,"degree_name":"Master of Science in Computer Science (MSCS)","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Dr. Ahyoung Lee"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-12-18T08:00:00Z","date_published":"2020-12-18T08:00:00Z","updated_at":"2026-07-24T02:43:42Z","subjects":["Anomaly detection","Unsupervised","Log files","Streaming"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.kennesaw.edu/cs_etd/38","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Ahyoung Lee"]},{"key":"dc:creator","label":"Author","values":["Zeufack, Vannel"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2020-12-18T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Computer Science (MSCS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Anomaly detection","Unsupervised","Log files","Streaming"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.kennesaw.edu/cs_etd/38"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Nowadays, in almost every computer system, log files are used to keep records of occurring events. Those log files are then used for analyzing and debugging system failures. Due to this important utility, researchers have worked on finding fast and efficient ways to detect anomalies in a computer system by analyzing its log records. Research in log-based anomaly detection can be divided into two main categories: batch log-based anomaly detection and streaming logbased anomaly detection. Batch log-based anomaly detection is computationally heavy and does not allow us to instantaneously detect anomalies. On the other hand, streaming anomaly detection allows for immediate alert. However, current streaming approaches are mainly supervised. In this work, we propose a fully unsupervised framework which can detect anomalies in real time. We test our framework on hdfs log files and successfully detect anomalies with an F- 1 score of 83%.
"]},{"key":"dc:title","label":"Title","values":["An Unsupervised Anomaly Detection Framework for Detecting Anomalies in Real Time through Network System’s Log Files Analysis"]}]}],"canonical_facts":{"dc:contributor":["Dr. Ahyoung Lee"],"dc:creator":["Zeufack, Vannel"],"dc:date.available":["2020-12-18T08:00:00Z"],"dc:description.abstract":["Nowadays, in almost every computer system, log files are used to keep records of occurring events. Those log files are then used for analyzing and debugging system failures. Due to this important utility, researchers have worked on finding fast and efficient ways to detect anomalies in a computer system by analyzing its log records. Research in log-based anomaly detection can be divided into two main categories: batch log-based anomaly detection and streaming logbased anomaly detection. Batch log-based anomaly detection is computationally heavy and does not allow us to instantaneously detect anomalies. On the other hand, streaming anomaly detection allows for immediate alert. However, current streaming approaches are mainly supervised. In this work, we propose a fully unsupervised framework which can detect anomalies in real time. We test our framework on hdfs log files and successfully detect anomalies with an F- 1 score of 83%.
"],"dc:identifier":["https://digitalcommons.kennesaw.edu/cs_etd/38"],"dc:subject":["Anomaly detection","Unsupervised","Log files","Streaming"],"dc:title":["An Unsupervised Anomaly Detection Framework for Detecting Anomalies in Real Time through Network System’s Log Files Analysis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science in Computer Science (MSCS)"]},"updated_at":"2026-07-24T02:43:42Z"}