3,194 research outputs found
An Efficient Fuzzy Clustering-Based Approach for Intrusion Detection
The need to increase accuracy in detecting sophisticated cyber attacks poses
a great challenge not only to the research community but also to corporations.
So far, many approaches have been proposed to cope with this threat. Among
them, data mining has brought on remarkable contributions to the intrusion
detection problem. However, the generalization ability of data mining-based
methods remains limited, and hence detecting sophisticated attacks remains a
tough task. In this thread, we present a novel method based on both clustering
and classification for developing an efficient intrusion detection system
(IDS). The key idea is to take useful information exploited from fuzzy
clustering into account for the process of building an IDS. To this aim, we
first present cornerstones to construct additional cluster features for a
training set. Then, we come up with an algorithm to generate an IDS based on
such cluster features and the original input features. Finally, we
experimentally prove that our method outperforms several well-known methods.Comment: 15th East-European Conference on Advances and Databases and
Information Systems (ADBIS 11), Vienna : Austria (2011
Adaptive Online Sequential ELM for Concept Drift Tackling
A machine learning method needs to adapt to over time changes in the
environment. Such changes are known as concept drift. In this paper, we propose
concept drift tackling method as an enhancement of Online Sequential Extreme
Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by
adding adaptive capability for classification and regression problem. The
scheme is named as adaptive OS-ELM (AOS-ELM). It is a single classifier scheme
that works well to handle real drift, virtual drift, and hybrid drift. The
AOS-ELM also works well for sudden drift and recurrent context change type. The
scheme is a simple unified method implemented in simple lines of code. We
evaluated AOS-ELM on regression and classification problem by using concept
drift public data set (SEA and STAGGER) and other public data sets such as
MNIST, USPS, and IDS. Experiments show that our method gives higher kappa value
compared to the multiclassifier ELM ensemble. Even though AOS-ELM in practice
does not need hidden nodes increase, we address some issues related to the
increasing of the hidden nodes such as error condition and rank values. We
propose taking the rank of the pseudoinverse matrix as an indicator parameter
to detect underfitting condition.Comment: Hindawi Publishing. Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 8091267, 17 pages Received 29 January 2016,
Accepted 17 May 2016. Special Issue on "Advances in Neural Networks and
Hybrid-Metaheuristics: Theory, Algorithms, and Novel Engineering
Applications". Academic Editor: Stefan Hauf
Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers
Machine Learning (ML) algorithms are used to train computers to perform a
variety of complex tasks and improve with experience. Computers learn how to
recognize patterns, make unintended decisions, or react to a dynamic
environment. Certain trained machines may be more effective than others because
they are based on more suitable ML algorithms or because they were trained
through superior training sets. Although ML algorithms are known and publicly
released, training sets may not be reasonably ascertainable and, indeed, may be
guarded as trade secrets. While much research has been performed about the
privacy of the elements of training sets, in this paper we focus our attention
on ML classifiers and on the statistical information that can be unconsciously
or maliciously revealed from them. We show that it is possible to infer
unexpected but useful information from ML classifiers. In particular, we build
a novel meta-classifier and train it to hack other classifiers, obtaining
meaningful information about their training sets. This kind of information
leakage can be exploited, for example, by a vendor to build more effective
classifiers or to simply acquire trade secrets from a competitor's apparatus,
potentially violating its intellectual property rights
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