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

Use of a Metallic Complex Derived from Curcuma Longa as Green Corrosion Inhibitor for Carbon Steel in Sulfuric Acid

A tin-containing metallic complex derived from Curcuma longa, bis[1,7-bis(4-hydroxy-3-methoxyphenyl)-1,6-heptadiene-3,5-dionato-κO,κO′]bis(butyl), has been obtained and used as a green corrosion inhibitor for carbon steel in 0.5 M sulfuric acid by using weight loss, electrochemical techniques, and the Density Functional Theory. It was found that the obtained metallic complex greatly decreases the steel corrosion rate by adsorption according to a Frumkin model in a weak, physical type of adsorption. Inhibitor efficiency increased with its concentration, and it acted as a mixed type of inhibitor. Results were supported by quantum-chemical research in order to examine the relationship between structural and electronic properties and the inhibitor efficiency