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

KDM1A microenvironment, its oncogenic potential, and therapeutic significance

The lysine-specific histone demethylase 1A (KDM1A) was the first demethylase to challenge the concept of the irreversible nature of methylation marks. KDM1A, containing a flavin adenine dinucleotide (FAD)-dependent amine oxidase domain, demethylates histone 3 lysine 4 and histone 3 lysine 9 (H3K4me1/2 and H3K9me1/2). It has emerged as an epigenetic developmental regulator and was shown to be involved in carcinogenesis. The functional diversity of KDM1A originates from its complex structure and interactions with transcription factors, promoters, enhancers, oncoproteins, and tumor-associated genes (tumor suppressors and activators). In this review, we discuss the microenvironment of KDM1A in cancer progression that enables this protein to activate or repress target gene expression, thus making it an important epigenetic modifier that regulates the growth and differentiation potential of cells. A detailed analysis of the mechanisms underlying the interactions between KDM1A and the associated complexes will help to improve our understanding of epigenetic regulation, which may enable the discovery of more effective anticancer drugs

The d-hop k-data coverage query problem in wireless sensor networks

The task of querying wireless sensor network (WSN) to retrieve data of interest is very significant and a wealth of query types have been proposed in the context of WSNs. This article describes the d-hop k-data coverage query problem, which is a novel query type, aiming at extracting feature(s) distribution maps from WSNs. This problem generalizes earlier research problems, like top-k, skyband, and d-hop dominating sets. For this problem, we provide a fully distributed solution, the DaCoN protocol, that avoids constructing a network spanner, since such a structure requires an expensive initialization procedure, misses the notion of neighborhoods, and most importantly, it creates hot-spots of communication, that shorten the network lifetime (i.e., some nodes deplete their energy very fast). We have developed a simulator to study the performance and behavior of the DaCoN protocol for various sensornet topologies and data distributions, and the obtained results attested the energy-efficiency and effectiveness of the protocol. Copyright 2008 ACM

Targeting Smyd3 by next-generation antisense oligonucleotides suppresses liver tumor growth

The oncogenic function of suppressor of variegation, enhancer of zeste and MYeloid-Nervy-DEAF1-domain family methyltransferase Smyd3 has been implicated in various malignancies, including hepatocellular carcinoma (HCC). Here, we show that targeting Smyd3 by next-generation antisense oligonucleotides (Smyd3-ASO) is an efficient approach to modulate its mRNA levels in vivo and to halt the growth of already initiated liver tumors. Smyd3-ASO treatment dramatically decreased tumor burden in a mouse model of chemically induced HCC and negatively affected the growth rates, migration, oncosphere formation, and xenograft growth capacity of a panel of human hepatic cancer cell lines. Smyd3-ASOs prevented the activation of oncofetal genes and the development of cancer-specific gene expression program. The results point to a mechanism by which Smyd3-ASO treatment blocks cellular de-differentiation, a hallmark feature of HCC development, and, as a result, it inhibits the expansion of hepatic cancer stem cells, a population that has been presumed to resist chemotherapy. © 2021 The Author