Input significance analysis: feature selection through synaptic weights manipulation for EFuNNs classifier

This work is interested in ISA methods that can manipulate synaptic weights namelyConnection Weights (CW) and Garson’s Algorithm (GA) and the classifier selected isEvolving Fuzzy Neural Networks (EFuNNs). Firstly, it test FS method on a dataset selectedfrom the UCI Machine Learning Repository and executed in an online environment, recordthe results and compared with the results that used original and ranked data from the previouswork. This is to identify whether FS can contribute to improved results and which of the ISAmethods mentioned above that work well with FS, i.e. give the best results. Secondly, to attestthe FS results by using a differently selected dataset taken from the same source and in thesame environment. The results are promising when FS is applied, some efficiency andaccuracy are noticeable compared to the original and ranked data.Keywords: feature selection; feature ranking; input significance analysis; evolvingconnectionist systems; evolving fuzzy neural network; connection weights; Garson’salgorithm

Intrusion Detection at Packet Level by Unsupervised Architectures

Intrusion Detection Systems (IDS’s) monitor the traffic in computer networks for detecting suspect activities. Connectionist techniques can support the development of IDS’s by modeling ‘normal’ traffic. This paper presents the application of some unsupervised neural methods to a packet dataset for the first time. This work considers three unsupervised neural methods, namely, Vector Quantization (VQ), Self-Organizing Maps (SOM) and Auto-Associative Back-Propagation (AABP) networks. The former paradigm proves quite powerful in supporting the basic space-spanning mechanism to sift normal traffic from anomalous traffic. The SOM attains quite acceptable results in dealing with some anomalies while it fails in dealing with some others. The AABP model effectively drives a nonlinear compression paradigm and eventually yields a compact visualization of the network traffic progression

Neural visualization of network traffic data for intrusion detection

This study introduces and describes a novel intrusion detection system (IDS) called MOVCIDS (mobile visualization connectionist IDS). This system applies neural projection architectures to detect anomalous situations taking place in a computer network. By its advanced visualization facilities, the proposed IDS allows providing an overview of the network traffic as well as identifying anomalous situations tackled by computer networks, responding to the challenges presented by volume, dynamics and diversity of the traffic, including novel (0-day) attacks. MOVCIDS provides a novel point of view in the field of IDSs by enabling the most interesting projections (based on the fourth order statistics; the kurtosis index) of a massive traffic dataset to be extracted. These projections are then depicted through a functional and mobile visualization interface, providing visual information of the internal structure of the traffic data. The interface makes MOVCIDS accessible from any mobile device to give more accessibility to network administrators, enabling continuous visualization, monitoring and supervision of computer networks. Additionally, a novel testing technique has been developed to evaluate MOVCIDS and other IDSs employing numerical datasets. To show the performance and validate the proposed IDS, it has been tested in different real domains containing several attacks and anomalous situations. In addition, the importance of the temporal dimension on intrusion detection, and the ability of this IDS to process it, are emphasized in this workJunta de Castilla and Leon project BU006A08, Business intelligence for production within the framework of the Instituto Tecnologico de Cas-tilla y Leon (ITCL) and the Agencia de Desarrollo Empresarial (ADE), and the Spanish Ministry of Education and Innovation project CIT-020000-2008-2. The authors would also like to thank the vehicle interior manufacturer, Grupo Antolin Ingenieria S. A., within the framework of the project MAGNO2008-1028-CENIT Project funded by the Spanish Government

RT-MOVICAB-IDS: Addressing real-time intrusion detection

This study presents a novel Hybrid Intelligent Intrusion Detection System (IDS) known as RT-MOVICAB-IDS that incorporates temporal control. One of its main goals is to facilitate real-time Intrusion Detection, as accurate and swift responses are crucial in this field, especially if automatic abortion mechanisms are running. The formulation of this hybrid IDS combines Artificial Neural Networks (ANN) and Case-Based Reasoning (CBR) within a Multi-Agent System (MAS) to detect intrusions in dynamic computer networks. Temporal restrictions are imposed on this IDS, in order to perform real/execution time processing and assure system response predictability. Therefore, a dynamic real-time multi-agent architecture for IDS is proposed in this study, allowing the addition of predictable agents (both reactive and deliberative). In particular, two of the deliberative agents deployed in this system incorporate temporal-bounded CBR. This upgraded CBR is based on an anytime approximation, which allows the adaptation of this Artificial Intelligence paradigm to real-time requirements. Experimental results using real data sets are presented which validate the performance of this novel hybrid IDSMinisterio de Economía y Competitividad (TIN2010-21272-C02-01, TIN2009-13839-C03-01), Ministerio de Ciencia e Innovación (CIT-020000-2008-2, CIT-020000-2009-12

Evolutionary Design of Neural Architectures -- A Preliminary Taxonomy and Guide to Literature

This report briefly motivates current research on evolutionary design of neural architectures (EDNA) and presents a short overview of major research issues in this area. It also includes a preliminary taxonomy of research on EDNA and an extensive bibliography of publications on this topic. The taxonomy is an attempt to categorize current research on EDNA in terms of major research issues addressed and approaches pursued. It is our hope that this will help identify open research questions as well as promising directions for further research on EDNA. The report also includes an appendix that provides some suggestions for effective use of the electronic version of the bibliography

A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage

A key aspect of a sustainable urban transportation system is the effectiveness of transportation policies. To be effective, a policy has to consider a broad range of elements, such as pollution emission, traffic flow, and human mobility. Due to the complexity and variability of these elements in the urban area, to produce effective policies remains a very challenging task. With the introduction of the smart city paradigm, a widely available amount of data can be generated in the urban spaces. Such data can be a fundamental source of knowledge to improve policies because they can reflect the sustainability issues underlying the city. In this context, we propose an approach to exploit urban positioning data based on stigmergy, a bio-inspired mechanism providing scalar and temporal aggregation of samples. By employing stigmergy, samples in proximity with each other are aggregated into a functional structure called trail. The trail summarizes relevant dynamics in data and allows matching them, providing a measure of their similarity. Moreover, this mechanism can be specialized to unfold specific dynamics. Specifically, we identify high-density urban areas (i.e hotspots), analyze their activity over time, and unfold anomalies. Moreover, by matching activity patterns, a continuous measure of the dissimilarity with respect to the typical activity pattern is provided. This measure can be used by policy makers to evaluate the effect of policies and change them dynamically. As a case study, we analyze taxi trip data gathered in Manhattan from 2013 to 2015.Comment: Preprin