scholarly journals A Local Feature Engineering Strategy to Improve Network Anomaly Detection

2020 ◽  
Vol 12 (10) ◽  
pp. 177
Author(s):  
Salvatore Carta ◽  
Alessandro Sebastian Podda ◽  
Diego Reforgiato Recupero ◽  
Roberto Saia
Keyword(s):  
Feature Space ◽  
Efficient Solutions ◽  
Local Feature ◽  
Network Activity ◽  
Feature Engineering ◽  
Real World Data ◽  
Canonical State ◽  
Long Time ◽  
Engineering Strategy ◽  
Network Anomaly Detection

The dramatic increase in devices and services that has characterized modern societies in recent decades, boosted by the exponential growth of ever faster network connections and the predominant use of wireless connection technologies, has materialized a very crucial challenge in terms of security. The anomaly-based intrusion detection systems, which for a long time have represented some of the most efficient solutions to detect intrusion attempts on a network, have to face this new and more complicated scenario. Well-known problems, such as the difficulty of distinguishing legitimate activities from illegitimate ones due to their similar characteristics and their high degree of heterogeneity, today have become even more complex, considering the increase in the network activity. After providing an extensive overview of the scenario under consideration, this work proposes a Local Feature Engineering (LFE) strategy aimed to face such problems through the adoption of a data preprocessing strategy that reduces the number of possible network event patterns, increasing at the same time their characterization. Unlike the canonical feature engineering approaches, which take into account the entire dataset, it operates locally in the feature space of each single event. The experiments conducted on real-world data showed that this strategy, which is based on the introduction of new features and the discretization of their values, improves the performance of the canonical state-of-the-art solutions.

scholarly journals An Incremental Classification Algorithm for Mining Data with Feature Space Heterogeneity

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yu Wang
Keyword(s):  
Feature Space ◽  
Classification Problem ◽  
Classification Algorithm ◽  
Data Sets ◽  
Real World Data ◽  
Supervised Clustering ◽  
Online Classification ◽  
Efficiency And Effectiveness ◽  
Feature Relevance ◽  
Incremental Classification

Feature space heterogeneity often exists in many real world data sets so that some features are of different importance for classification over different subsets. Moreover, the pattern of feature space heterogeneity might dynamically change over time as more and more data are accumulated. In this paper, we develop an incremental classification algorithm, Supervised Clustering for Classification with Feature Space Heterogeneity (SCCFSH), to address this problem. In our approach, supervised clustering is implemented to obtain a number of clusters such that samples in each cluster are from the same class. After the removal of outliers, relevance of features in each cluster is calculated based on their variations in this cluster. The feature relevance is incorporated into distance calculation for classification. The main advantage of SCCFSH lies in the fact that it is capable of solving a classification problem with feature space heterogeneity in an incremental way, which is favorable for online classification tasks with continuously changing data. Experimental results on a series of data sets and application to a database marketing problem show the efficiency and effectiveness of the proposed approach.

scholarly journals Plasticity of Neuron-Glial Transmission: Equipping Glia for Long-Term Integration of Network Activity

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Wayne Croft ◽  
Katharine L. Dobson ◽  
Tomas C. Bellamy
Keyword(s):  
Long Term Potentiation ◽  
Brain Regions ◽  
Neuronal Firing ◽  
Network Activity ◽  
Neuronal Function ◽  
Term Potentiation ◽  
Bidirectional Communication ◽  
Long Time ◽  
Activity Dependent

The capacity of synaptic networks to express activity-dependent changes in strength and connectivity is essential for learning and memory processes. In recent years, glial cells (most notably astrocytes) have been recognized as active participants in the modulation of synaptic transmission and synaptic plasticity, implicating these electrically nonexcitable cells in information processing in the brain. While the concept of bidirectional communication between neurons and glia and the mechanisms by which gliotransmission can modulate neuronal function are well established, less attention has been focussed on the computational potential of neuron-glial transmission itself. In particular, whether neuron-glial transmission is itself subject to activity-dependent plasticity and what the computational properties of such plasticity might be has not been explored in detail. In this review, we summarize current examples of plasticity in neuron-glial transmission, in many brain regions and neurotransmitter pathways. We argue that induction of glial plasticity typically requires repetitive neuronal firing over long time periods (minutes-hours) rather than the short-lived, stereotyped trigger typical of canonical long-term potentiation. We speculate that this equips glia with a mechanism for monitoring average firing rates in the synaptic network, which is suited to the longer term roles proposed for astrocytes in neurophysiology.

scholarly journals Preventing Disparate Treatment in Sequential Decision Making

2018 ◽  
Author(s):  
Hoda Heidari ◽  
Andreas Krause
Keyword(s):  
Decision Making ◽  
Learning Algorithm ◽  
Feature Space ◽  
Sequential Decision Making ◽  
Data Sets ◽  
Sequential Decision ◽  
Real World Data ◽  
Time Step ◽  
Job Application ◽  
Disparate Treatment

We study fairness in sequential decision making environments, where at each time step a learning algorithm receives data corresponding to a new individual (e.g. a new job application) and must make an irrevocable decision about him/her (e.g. whether to hire the applicant) based on observations made so far. In order to prevent cases of disparate treatment, our time-dependent notion of fairness requires algorithmic decisions to be consistent: if two individuals are similar in the feature space and arrive during the same time epoch, the algorithm must assign them to similar outcomes. We propose a general framework for post-processing predictions made by a black-box learning model, that guarantees the resulting sequence of outcomes is consistent. We show theoretically that imposing consistency will not significantly slow down learning. Our experiments on two real-world data sets illustrate and confirm this finding in practice.

Pain revised – learning from anomalies

2019 ◽  
Vol 20 (1) ◽  
pp. 29-32
Author(s):  
Lars-Petter Granan
Keyword(s):  
Chronic Pain ◽  
Clinical Medicine ◽  
Pain Treatment ◽  
Network Activity ◽  
Real World Data ◽  
Chronic Pain Condition ◽  
Pain Condition ◽  
Introductory Article ◽  
Starting Point ◽  
Chronic Pain Treatment

AbstractAs professional health care personnel we are well educated in anatomy, physiology, clinical medicine and so forth. Our patients present with various symptoms and signs that we use this knowledge to diagnose and treat. But sometimes the patient case contradicts our knowledge. Since the patient is the terrain and our knowledge is the map, these patient cases are anomalies that give us the opportunity to update our maps. One such anomaly is how time restricted amnesia can improve or even eradicate an underlying chronic pain condition and eliminate the patient’s dependence on daily opioid consumption. In this short communication I will use amnesia as a starting point to briefly review chronic pain from a learning and memory perspective. I will introduce, for many readers, new concepts like degeneracy and criticality, and together with more familiar concepts like habits and brain network activity, we will end with overarching principles for how chronic pain treatment in general can be crafted and individualized almost independently of the chronic pain condition at hand. This introductory article is followed by a review series that elaborates on the fundamental biological principles for chronic pain, treatment options, and testing the theory with real world data.

scholarly journals Feature Selection for Ridge Regression with Provable Guarantees

2016 ◽  
Vol 28 (4) ◽  
pp. 716-742 ◽  
Author(s):  
Saurabh Paul ◽  
Petros Drineas
Keyword(s):  
Feature Selection ◽  
Ridge Regression ◽  
Feature Selection Method ◽  
Feature Space ◽  
Data Sets ◽  
Real World Data ◽  
Feature Selection Technique ◽  
Worst Case ◽  
Single Set ◽  
Classification Function

We introduce single-set spectral sparsification as a deterministic sampling–based feature selection technique for regularized least-squares classification, which is the classification analog to ridge regression. The method is unsupervised and gives worst-case guarantees of the generalization power of the classification function after feature selection with respect to the classification function obtained using all features. We also introduce leverage-score sampling as an unsupervised randomized feature selection method for ridge regression. We provide risk bounds for both single-set spectral sparsification and leverage-score sampling on ridge regression in the fixed design setting and show that the risk in the sampled space is comparable to the risk in the full-feature space. We perform experiments on synthetic and real-world data sets; a subset of TechTC-300 data sets, to support our theory. Experimental results indicate that the proposed methods perform better than the existing feature selection methods.

A Novel Low Rank Representation Algorithm for Subspace Clustering

2016 ◽  
Vol 30 (04) ◽  
pp. 1650007 ◽  
Author(s):  
Yuanyuan Chen ◽  
Lei Zhang ◽  
Zhang Yi
Keyword(s):  
Subspace Clustering ◽  
Low Rank ◽  
Affinity Matrix ◽  
Real World Data ◽  
Data Set ◽  
Cluster Data ◽  
Augmented Lagrange Multiplier ◽  
Efficiency And Effectiveness ◽  
Long Time ◽  
Low Rank Representation

Low rank representation (LRR) is widely used to construct a good affinity matrix to cluster data drawn from the union of multiple linear subspaces. However, it is not easy to solve the LRR problem in a closed form, and augmented Lagrange multiplier method (ALM) is usually applied. ALM takes a relative long time dealing with the real-world data. To solve the LRR problem efficiently, we propose an efficient low rank representation (eLRR) algorithm. Given a contaminated data set, we propose a novel way to solve the LRR of the data. We establish a useful theorem which directly gives an approximate solution to our LRR optimization problem. Thus, we can construct a good affinity matrix for subspace clustering. Experimental results with several public databases verify the efficiency and effectiveness of our method.

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