Unsupervised Ensemble Anomaly Detection Using Time-Periodic Packet Sampling

We propose an anomaly detection method for finding patterns in network traffic that do not conform to legitimate (i.e., normal) behavior. The proposed method trains a baseline model describing the normal behavior of network traffic without using manually labeled traffic data. The trained baseline model is used as the basis for comparison with the audit network traffic. This anomaly detection works in an unsupervised manner through the use of time-periodic packet sampling, which is used in a manner that differs from its intended purpose — the lossy nature of packet sampling is used to extract normal packets from the unlabeled original traffic data. Evaluation using actual traffic traces showed that the proposed method has false positive and false negative rates in the detection of anomalies regarding TCP SYN packets comparable to those of a conventional method that uses manually labeled traffic data to train the baseline model. Performance variation due to the probabilistic nature of sampled traffic data is mitigated by using ensemble anomaly detection that collectively exploits multiple baseline models in parallel. Alarm sensitivity is adjusted for the intended use by using maximum- and minimum-based anomaly detection that effectively take advantage of the performance variations among the multiple baseline models. Testing using actual traffic traces showed that the proposed anomaly detection method performs as well as one using manually labeled traffic data and better than one using randomly sampled (unlabeled) traffic data

A Practical Evaluation Method of Network Traffic Load for Capacity Planning

Part 6: Short PapersInternational audienceCommunications network operators are supposed to provide high quality network service at low cost. Operators always monitor the amount of traffic and decide equipment investment when the amount exceeds a certain threshold considering trade-offs between link capacity and its utilization. To find the proper threshold efficiently, this paper proposes a practical threshold definition method which consists of fine grained data collection and computer simulation. We evaluate the proposed method using commercial traffic data-set. The results show the proper timing for the equipment investment

Encouraging outpatients in an acute hospital for the relief of cancer-related pain: a qualitative study

Abstract Purpose To identify the processes of cancer-related pain relief and exacerbation faced by outpatients in an acute care hospital and to examine the support needed for outpatient pain control. Methods We conducted semi-structured, in-depth interviews with patients from the outpatient department of Showa University Northern Yokohama Hospital in Kanagawa Prefecture, Japan. Participants were recruited by purposive sampling. From the recorded data, verbatim transcripts were made and used as textual data for analysis by consistent comparative method. Results Between April 2018 and April 2022, interviews were conducted with 30 participants. Analysis of the verbatim transcripts generated 13 categories from 27 concepts. Category relationships were examined, and a conceptual framework was developed. Outpatients went from being in a state of hesitation towards consultation with medical professionals to receiving individual consistent follow-ups by medical professionals in the hospital and community pharmacies, which led to patient teleconsultations when their physical condition changed, leading to an improvement of pain. Conclusion The process of relief and exacerbation of cancer-related pain experienced by outpatients in the acute care hospital reveals that the provision of consistent follow-up through remote or in-person interviews has an important role to play in pain management, as it helps to build relationships between patients and medical professionals. Alternatively, when outpatients exhibited endurance, their pain worsened, and they fell into a negative cycle of poor pain control