1,621 research outputs found
Sparse neural networks with large learning diversity
Coded recurrent neural networks with three levels of sparsity are introduced.
The first level is related to the size of messages, much smaller than the
number of available neurons. The second one is provided by a particular coding
rule, acting as a local constraint in the neural activity. The third one is a
characteristic of the low final connection density of the network after the
learning phase. Though the proposed network is very simple since it is based on
binary neurons and binary connections, it is able to learn a large number of
messages and recall them, even in presence of strong erasures. The performance
of the network is assessed as a classifier and as an associative memory
FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification
This paper introduces a novel real-time Fuzzy Supervised Learning with Binary
Meta-Feature (FSL-BM) for big data classification task. The study of real-time
algorithms addresses several major concerns, which are namely: accuracy, memory
consumption, and ability to stretch assumptions and time complexity. Attaining
a fast computational model providing fuzzy logic and supervised learning is one
of the main challenges in the machine learning. In this research paper, we
present FSL-BM algorithm as an efficient solution of supervised learning with
fuzzy logic processing using binary meta-feature representation using Hamming
Distance and Hash function to relax assumptions. While many studies focused on
reducing time complexity and increasing accuracy during the last decade, the
novel contribution of this proposed solution comes through integration of
Hamming Distance, Hash function, binary meta-features, binary classification to
provide real time supervised method. Hash Tables (HT) component gives a fast
access to existing indices; and therefore, the generation of new indices in a
constant time complexity, which supersedes existing fuzzy supervised algorithms
with better or comparable results. To summarize, the main contribution of this
technique for real-time Fuzzy Supervised Learning is to represent hypothesis
through binary input as meta-feature space and creating the Fuzzy Supervised
Hash table to train and validate model.Comment: FICC201
Dynamic Data Mining: Methodology and Algorithms
Supervised data stream mining has become an important and challenging data mining task in modern
organizations. The key challenges are threefold: (1) a possibly infinite number of streaming examples
and time-critical analysis constraints; (2) concept drift; and (3) skewed data distributions.
To address these three challenges, this thesis proposes the novel dynamic data mining (DDM)
methodology by effectively applying supervised ensemble models to data stream mining. DDM can be
loosely defined as categorization-organization-selection of supervised ensemble models. It is inspired
by the idea that although the underlying concepts in a data stream are time-varying, their distinctions
can be identified. Therefore, the models trained on the distinct concepts can be dynamically selected in
order to classify incoming examples of similar concepts.
First, following the general paradigm of DDM, we examine the different concept-drifting stream
mining scenarios and propose corresponding effective and efficient data mining algorithms.
• To address concept drift caused merely by changes of variable distributions, which we term
pseudo concept drift, base models built on categorized streaming data are organized and
selected in line with their corresponding variable distribution characteristics.
• To address concept drift caused by changes of variable and class joint distributions, which we
term true concept drift, an effective data categorization scheme is introduced. A group of
working models is dynamically organized and selected for reacting to the drifting concept.
Secondly, we introduce an integration stream mining framework, enabling the paradigm advocated by
DDM to be widely applicable for other stream mining problems. Therefore, we are able to introduce
easily six effective algorithms for mining data streams with skewed class distributions.
In addition, we also introduce a new ensemble model approach for batch learning, following the same
methodology. Both theoretical and empirical studies demonstrate its effectiveness.
Future work would be targeted at improving the effectiveness and efficiency of the proposed
algorithms. Meantime, we would explore the possibilities of using the integration framework to solve
other open stream mining research problems
Dos and Don'ts of Machine Learning in Computer Security
With the growing processing power of computing systems and the increasing
availability of massive datasets, machine learning algorithms have led to major
breakthroughs in many different areas. This development has influenced computer
security, spawning a series of work on learning-based security systems, such as
for malware detection, vulnerability discovery, and binary code analysis.
Despite great potential, machine learning in security is prone to subtle
pitfalls that undermine its performance and render learning-based systems
potentially unsuitable for security tasks and practical deployment. In this
paper, we look at this problem with critical eyes. First, we identify common
pitfalls in the design, implementation, and evaluation of learning-based
security systems. We conduct a study of 30 papers from top-tier security
conferences within the past 10 years, confirming that these pitfalls are
widespread in the current security literature. In an empirical analysis, we
further demonstrate how individual pitfalls can lead to unrealistic performance
and interpretations, obstructing the understanding of the security problem at
hand. As a remedy, we propose actionable recommendations to support researchers
in avoiding or mitigating the pitfalls where possible. Furthermore, we identify
open problems when applying machine learning in security and provide directions
for further research.Comment: to appear at USENIX Security Symposium 202
A Machine Learning Approach for Intrusion Detection
Master's thesis in Information- and communication technology (IKT590)Securing networks and their confidentiality from intrusions is crucial, and for this rea-son, Intrusion Detection Systems have to be employed. The main goal of this thesis is to achieve a proper detection performance of a Network Intrusion Detection System (NIDS). In this thesis, we have examined the detection efficiency of machine learning algorithms such as Neural Network, Convolutional Neural Network, Random Forestand Long Short-Term Memory. We have constructed our models so that they can detect different types of attacks utilizing the CICIDS2017 dataset. We have worked on identifying 15 various attacks present in CICIDS2017, instead of merely identifying normal-abnormal traffic. We have also discussed the reason why to use precisely this dataset, and why should one classify by attack to enhance the detection. Previous works based on benchmark datasets such as NSL-KDD and KDD99 are discussed. Also, how to address and solve these issues. The thesis also shows how the results are effected using different machine learning algorithms. As the research will demon-strate, the Neural Network, Convulotional Neural Network, Random Forest and Long Short-Term Memory are evaluated by conducting cross validation; the average score across five folds of each model is at 92.30%, 87.73%, 94.42% and 87.94%, respectively. Nevertheless, the confusion metrics was also a crucial measurement to evaluate the models, as we shall see. Keywords: Information security, NIDS, Machine Learning, Neural Network, Convolutional Neural Network, Random Forest, Long Short-Term Memory, CICIDS2017
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