47,943 research outputs found
Intrusion Detection A Text Mining Based Approach
Intrusion Detection is one of major threats for organization. The approach of
intrusion detection using text processing has been one of research interests
which is gaining significant importance from researchers. In text mining based
approach for intrusion detection, system calls serve as source for mining and
predicting possibility of intrusion or attack. When an application runs, there
might be several system calls which are initiated in the background. These
system calls form the strong basis and the deciding factor for intrusion
detection. In this paper, we mainly discuss the approach for intrusion
detection by designing a distance measure which is designed by taking into
consideration the conventional Gaussian function and modified to suit the need
for similarity function. A Framework for intrusion detection is also discussed
as part of this research.Comment: 13 pages, 4 figures, Special issue on Computing Applications and Data
Mining, Paper 01021609, International Journal of Computer Science and
Information Security (IJCSIS), Vol. 14 S1, February 201
FuGeIDS: Fuzzy Genetic paradigms in Intrusion Detection Systems
With the increase in the number of security threats, Intrusion Detection
Systems have evolved as a significant countermeasure against these threats. And
as such, the topic of Intrusion Detection Systems has become one of the most
prominent research topics in recent years. This paper gives an overview of the
Intrusion Detection System and looks at two major machine learning paradigms
used in Intrusion Detection System, Genetic Algorithms and Fuzzy Logic and how
to apply them for intrusion detection.Comment: 7 pages, 2 figures, 1 tabl
An Optimized Weighted Association Rule Mining On Dynamic Content
Association rule mining aims to explore large transaction databases for
association rules. Classical Association Rule Mining (ARM) model assumes that
all items have the same significance without taking their weight into account.
It also ignores the difference between the transactions and importance of each
and every itemsets. But, the Weighted Association Rule Mining (WARM) does not
work on databases with only binary attributes. It makes use of the importance
of each itemset and transaction. WARM requires each item to be given weight to
reflect their importance to the user. The weights may correspond to special
promotions on some products, or the profitability of different items. This
research work first focused on a weight assignment based on a directed graph
where nodes denote items and links represent association rules. A generalized
version of HITS is applied to the graph to rank the items, where all nodes and
links are allowed to have weights. This research then uses enhanced HITS
algorithm by developing an online eigenvector calculation method that can
compute the results of mutual reinforcement voting in case of frequent updates.
For Example in Share Market Shares price may go down or up. So we need to
carefully watch the market and our association rule mining has to produce the
items that have undergone frequent changes. These are done by estimating the
upper bound of perturbation and postponing of the updates whenever possible.
Next we prove that enhanced algorithm is more efficient than the original HITS
under the context of dynamic data.Comment: International Journal of Computer Science Issues online at
http://ijcsi.org/articles/An-Optimized-Weighted-Association-Rule-Mining-On-Dynamic-Content.ph
A Survey on optimization approaches to text document clustering
Text Document Clustering is one of the fastest growing research areas because
of availability of huge amount of information in an electronic form. There are
several number of techniques launched for clustering documents in such a way
that documents within a cluster have high intra-similarity and low
inter-similarity to other clusters. Many document clustering algorithms provide
localized search in effectively navigating, summarizing, and organizing
information. A global optimal solution can be obtained by applying high-speed
and high-quality optimization algorithms. The optimization technique performs a
globalized search in the entire solution space. In this paper, a brief survey
on optimization approaches to text document clustering is turned out.Comment: 14 page
Intelligent Approaches to interact with Machines using Hand Gesture Recognition in Natural way: A Survey
Hand gestures recognition (HGR) is one of the main areas of research for the
engineers, scientists and bioinformatics. HGR is the natural way of Human
Machine interaction and today many researchers in the academia and industry are
working on different application to make interactions more easy, natural and
convenient without wearing any extra device. HGR can be applied from games
control to vision enabled robot control, from virtual reality to smart home
systems. In this paper we are discussing work done in the area of hand gesture
recognition where focus is on the intelligent approaches including soft
computing based methods like artificial neural network, fuzzy logic, genetic
algorithms etc. The methods in the preprocessing of image for segmentation and
hand image construction also taken into study. Most researchers used fingertips
for hand detection in appearance based modeling. Finally the comparison of
results given by different researchers is also presented
Intelligent Processing in Vehicular Ad hoc Networks: a Survey
The intelligent Processing technique is more and more attractive to
researchers due to its ability to deal with key problems in Vehicular Ad hoc
networks. However, several problems in applying intelligent processing
technologies in VANETs remain open. The existing applications are
comprehensively reviewed and discussed, and classified into different
categories in this paper. Their strategies, advantages/disadvantages, and
performances are elaborated. By generalizing different tactics in various
applications related to different scenarios of VANETs and evaluating their
performances, several promising directions for future research have been
suggested.Comment: 11pages, 5 figure
A Distributed and Cooperative Approach to Botnet Detection Using Gossip Protocol
Bots, in recent times, have posed a major threat to enterprise networks. With
the distributed nature of the way in which botnets operate, the problems faced
by enterprises have become acute. A bot is a program that operates as an agent
for a user and runs automated tasks over the internet, at a much higher rate
than would be possible for a human alone. A collection of bots in a network,
used for malicious purposes, is referred to as a botnet. In this paper we
suggested a distributed, co-operative approach towards detecting botnets is a
given network which is inspired by the gossip protocol. Each node in a given
network runs a standalone agent that computes a suspicion value for that node
after regular intervals. Each node in the network exchanges its suspicion
values with every other node in the network at regular intervals. The use of
gossip protocol ensures that if a node in the network is compromised, all other
nodes in the network are informed about it as soon as possible. Each node also
ensures that at any instance, by means of the gossip protocol, it maintains the
latest suspicion values of all the other nodes in the network.Comment: 8 pages, 1 figur
A Survey on Social Media Anomaly Detection
Social media anomaly detection is of critical importance to prevent malicious
activities such as bullying, terrorist attack planning, and fraud information
dissemination. With the recent popularity of social media, new types of
anomalous behaviors arise, causing concerns from various parties. While a large
amount of work have been dedicated to traditional anomaly detection problems,
we observe a surge of research interests in the new realm of social media
anomaly detection. In this paper, we present a survey on existing approaches to
address this problem. We focus on the new type of anomalous phenomena in the
social media and review the recent developed techniques to detect those special
types of anomalies. We provide a general overview of the problem domain, common
formulations, existing methodologies and potential directions. With this work,
we hope to call out the attention from the research community on this
challenging problem and open up new directions that we can contribute in the
future.Comment: 23 page
MOANOFS: Multi-Objective Automated Negotiation based Online Feature Selection System for Big Data Classification
Feature Selection (FS) plays an important role in learning and classification
tasks. The object of FS is to select the relevant and non-redundant features.
Considering the huge amount number of features in real-world applications, FS
methods using batch learning technique can't resolve big data problem
especially when data arrive sequentially. In this paper, we propose an online
feature selection system which resolves this problem. More specifically, we
treat the problem of online supervised feature selection for binary
classification as a decision-making problem. A philosophical vision to this
problem leads to a hybridization between two important domains: feature
selection using online learning technique (OFS) and automated negotiation (AN).
The proposed OFS system called MOANOFS (Multi-Objective Automated Negotiation
based Online Feature Selection) uses two levels of decision. In the first
level, from n learners (or OFS methods), we decide which are the k trustful
ones (with high confidence or trust value). These elected k learners will
participate in the second level. In this level, we integrate our proposed
Multilateral Automated Negotiation based OFS (MANOFS) method to decide finally
which is the best solution or which are relevant features. We show that MOANOFS
system is applicable to different domains successfully and achieves high
accuracy with several real-world applications.
Index Terms: Feature selection, online learning, multi-objective automated
negotiation, trust, classification, big data.Comment: 15 pages, 8 figures, journal pape
Dynamic Decision Support System Based on Bayesian Networks Application to fight against the Nosocomial Infections
The improvement of medical care quality is a significant interest for the
future years. The fight against nosocomial infections (NI) in the intensive
care units (ICU) is a good example. We will focus on a set of observations
which reflect the dynamic aspect of the decision, result of the application of
a Medical Decision Support System (MDSS). This system has to make dynamic
decision on temporal data. We use dynamic Bayesian network (DBN) to model this
dynamic process. It is a temporal reasoning within a real-time environment; we
are interested in the Dynamic Decision Support Systems in healthcare domain
(MDDSS).Comment: 8 pages, 6 figures, 43 reference
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