A Change of Variables to the Dual and Factorization of Composite Anomalous Jacobians

Changes of variables giving the dual model are constructed explicitly for sigma-models without isotropy. In particular, the jacobian is calculated to give the known results. The global aspects of the abelian case as well as some of those of the cases where the isometry group is simply connected are considered. Considering the anomalous case, we infer by a consistency argument that the `multiplicative anomaly' should be replaceable by adequate rules for factorization of composite jacobians. These rules are then generalized in a simple way for composite jacobians defined in spaces of different types. Implimentation of these rules then gives specific formulas for the anomally for semisimple algebras and also for solvable ones.Comment: 15 pages, no figures, Latex file, A treatment of the global aspects of the abelian and of semisimple duality groups are added. General formulas for the mixed anomaly are derive

Data Stream Clustering for Real-Time Anomaly Detection: An Application to Insider Threats

Insider threat detection is an emergent concern for academia, industries, and governments due to the growing number of insider incidents in recent years. The continuous streaming of unbounded data coming from various sources in an organisation, typically in a high velocity, leads to a typical Big Data computational problem. The malicious insider threat refers to anomalous behaviour(s) (outliers) that deviate from the normal baseline of a data stream. The absence of previously logged activities executed by users shapes the insider threat detection mechanism into an unsupervised anomaly detection approach over a data stream. A common shortcoming in the existing data mining approaches to detect insider threats is the high number of false alarms/positives (FPs). To handle the Big Data issue and to address the shortcoming, we propose a streaming anomaly detection approach, namely Ensemble of Random subspace Anomaly detectors In Data Streams (E-RAIDS), for insider threat detection. E-RAIDS learns an ensemble of p established outlier detection techniques [Micro-cluster-based Continuous Outlier Detection (MCOD) or Anytime Outlier Detection (AnyOut)] which employ clustering over continuous data streams. Each model of the p models learns from a random feature subspace to detect local outliers, which might not be detected over the whole feature space. E-RAIDS introduces an aggregate component that combines the results from the p feature subspaces, in order to confirm whether to generate an alarm at each window iteration. The merit of E-RAIDS is that it defines a survival factor and a vote factor to address the shortcoming of high number of FPs. Experiments on E-RAIDS-MCOD and E-RAIDS-AnyOut are carried out, on synthetic data sets including malicious insider threat scenarios generated at Carnegie Mellon University, to test the effectiveness of voting feature subspaces, and the capability to detect (more than one)-behaviour-all-threat in real-time. The results show that E-RAIDS-MCOD reports the highest F1 measure and less number of false alarm = 0 compared to E-RAIDS-AnyOut, as well as it attains to detect approximately all the insider threats in real-time

Conceptual design trade study for an energy-efficient mid-range aircraft with novel technologies

Present work demonstrates an initial conceptual design of a medium-range passenger aircraft featuring novel technologies being investigated under the German Cluster of Excellence SE2 A (Sustainable and Energy-Efficient Aviation). Novel aircraft and engine technologies include the hybrid laminar flow control, active load alleviation, boundary layer ingestion, ultra-high bypass ratio turbofan engines, and new materials and structures. To design such an aircraft, major trade studies and multi-disciplinary design optimization (MDO) were performed using a multi-fidelity analysis approach. The open-source aircraft design environment SUAVE was modified and integrated with other aircraft design and analysis tools to assess the performance of the aircraft equipped with the mentioned technologies and compared against the reference Airbus A320 aircraft. Results demonstrate reduction of the fuel burn by 43.6% with a forward-swept wing and by 36.7% with a backward-swept wing due to the benefits introduced by the novel technologies. © 2021, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved