Abstract: It is important problems to increase the detection rates
and reduce false positive rates in Intrusion Detection System (IDS).
Although preventative techniques such as access control and
authentication attempt to prevent intruders, these can fail, and as a
second line of defence, intrusion detection has been introduced. Rare
events are events that occur very infrequently, detection of rare
events is a common problem in many domains. In this paper we
propose an intrusion detection method that combines Rough set and
Fuzzy Clustering. Rough set has to decrease the amount of data and
get rid of redundancy. Fuzzy c-means clustering allow objects to
belong to several clusters simultaneously, with different degrees of
membership. Our approach allows us to recognize not only known
attacks but also to detect suspicious activity that may be the result of
a new, unknown attack. The experimental results on Knowledge
Discovery and Data Mining-(KDDCup 1999) Dataset show that the
method is efficient and practical for intrusion detection systems.
Abstract: Nowadays wireless technology plays an important
role in public and personal communication. However, the growth of
wireless networking has confused the traditional boundaries between
trusted and untrusted networks. Wireless networks are subject to a
variety of threats and attacks at present. An attacker has the ability to
listen to all network traffic which becoming a potential intrusion.
Intrusion of any kind may lead to a chaotic condition. In addition,
improperly configured access points also contribute the risk to
wireless network. To overcome this issue, a security solution that
includes an intrusion detection and prevention system need to be
implemented. In this paper, first the security drawbacks of wireless
network will be analyzed then investigate the characteristics and also
the limitations on current wireless intrusion detection and prevention
system. Finally, the requirement of next wireless intrusion prevention
system will be identified including some key issues which should be
focused on in the future to overcomes those limitations.
Abstract: Partial discharge (PD) detection is an important
method to evaluate the insulation condition of metal-clad apparatus.
Non-intrusive sensors which are easy to install and have no
interruptions on operation are preferred in onsite PD detection.
However, it often lacks of accuracy due to the interferences in PD
signals. In this paper a novel PD extraction method that uses frequency
analysis and entropy based time-frequency (TF) analysis is introduced.
The repetitive pulses from convertor are first removed via frequency
analysis. Then, the relative entropy and relative peak-frequency of
each pulse (i.e. time-indexed vector TF spectrum) are calculated and
all pulses with similar parameters are grouped. According to the
characteristics of non-intrusive sensor and the frequency distribution
of PDs, the pulses of PD and interferences are separated. Finally the
PD signal and interferences are recovered via inverse TF transform.
The de-noised result of noisy PD data demonstrates that the
combination of frequency and time-frequency techniques can
discriminate PDs from interferences with various frequency
distributions.
Abstract: As the Internet continues to grow at a rapid pace as
the primary medium for communications and commerce and as
telecommunication networks and systems continue to expand their
global reach, digital information has become the most popular and
important information resource and our dependence upon the
underlying cyber infrastructure has been increasing significantly.
Unfortunately, as our dependency has grown, so has the threat to the
cyber infrastructure from spammers, attackers and criminal
enterprises. In this paper, we propose a new machine learning based
network intrusion detection framework for cyber security. The
detection process of the framework consists of two stages: model
construction and intrusion detection. In the model construction stage,
a semi-supervised machine learning algorithm is applied to a
collected set of network audit data to generate a profile of normal
network behavior and in the intrusion detection stage, input network
events are analyzed and compared with the patterns gathered in the
profile, and some of them are then flagged as anomalies should these
events are sufficiently far from the expected normal behavior. The
proposed framework is particularly applicable to the situations where
there is only a small amount of labeled network training data
available, which is very typical in real world network environments.
Abstract: The security of computer networks plays a strategic
role in modern computer systems. Intrusion Detection Systems (IDS)
act as the 'second line of defense' placed inside a protected
network, looking for known or potential threats in network traffic
and/or audit data recorded by hosts. We developed an Intrusion
Detection System using LAMSTAR neural network to learn patterns
of normal and intrusive activities, to classify observed system
activities and compared the performance of LAMSTAR IDS with
other classification techniques using 5 classes of KDDCup99 data.
LAMSAR IDS gives better performance at the cost of high
Computational complexity, Training time and Testing time, when
compared to other classification techniques (Binary Tree classifier,
RBF classifier, Gaussian Mixture classifier). we further reduced the
Computational Complexity of LAMSTAR IDS by reducing the
dimension of the data using principal component analysis which in
turn reduces the training and testing time with almost the same
performance.
Abstract: In this paper, we present a new learning algorithm for
anomaly based network intrusion detection using improved self
adaptive naïve Bayesian tree (NBTree), which induces a hybrid of
decision tree and naïve Bayesian classifier. The proposed approach
scales up the balance detections for different attack types and keeps
the false positives at acceptable level in intrusion detection. In
complex and dynamic large intrusion detection dataset, the detection
accuracy of naïve Bayesian classifier does not scale up as well as
decision tree. It has been successfully tested in other problem
domains that naïve Bayesian tree improves the classification rates in
large dataset. In naïve Bayesian tree nodes contain and split as
regular decision-trees, but the leaves contain naïve Bayesian
classifiers. The experimental results on KDD99 benchmark network
intrusion detection dataset demonstrate that this new approach scales
up the detection rates for different attack types and reduces false
positives in network intrusion detection.
Abstract: Construction projects can be implemented under various contractual and organizational systems. They can be divided into two groups: systems without the managing company where the Client manages the process, and systems with the managing company, where management is entrusted to an external company. In the public sector of the Polish market there are two ways of delivery of construction projects with the participation of the manager: one is to assign operations to another party, the so called Project Supervisor, whilst the other results from the application of FIDIC conditions of contract, which entail appointment of the Engineer. The decision is to be made by the Client and depends on various factors. On the public procurement market in Poland the selection of construction project manager boils down to awarding the contract for such a service. The selection can be done by one of eight public procurement procedures identified by the procurement law. The paper provides the analysis of 96 contracts for services awarded in 2011, which employed construction management. The study aimed to investigate the methods and criteria for selecting managers, applied in practice by the Polish public Clients.
Abstract: This paper describes a new approach of classification
using genetic programming. The proposed technique consists of
genetically coevolving a population of non-linear transformations on
the input data to be classified, and map them to a new space with a
reduced dimension, in order to get a maximum inter-classes
discrimination. The classification of new samples is then performed
on the transformed data, and so become much easier. Contrary to the
existing GP-classification techniques, the proposed one use a
dynamic repartition of the transformed data in separated intervals, the
efficacy of a given intervals repartition is handled by the fitness
criterion, with a maximum classes discrimination. Experiments were
first performed using the Fisher-s Iris dataset, and then, the KDD-99
Cup dataset was used to study the intrusion detection and
classification problem. Obtained results demonstrate that the
proposed genetic approach outperform the existing GP-classification
methods [1],[2] and [3], and give a very accepted results compared to
other existing techniques proposed in [4],[5],[6],[7] and [8].
Abstract: In recent years, the underground water sources in
southern Taiwan have become salinized because of saltwater
intrusions. This study explores the adsorption characteristics of
activated carbon on salinizing inorganic salts using isothermal
adsorption experiments and provides a model analysis. The
temperature range for the isothermal adsorption experiments ranged
between 5 to 45 ℃, and the amount adsorbed varied between 28.21 to
33.87 mg/g. All experimental data of adsorption can be fitted to both
the Langmuir and the Freundlich models. The thermodynamic
parameters for per chlorate onto granular activated carbon were
calculated as -0.99 to -1.11 kcal/mol for DG°, -0.6 kcal/mol for DH°,
and 1.21 to 1.84 kcal/mol for DS°. This shows that the adsorption
process of granular activated carbon is spontaneously exothermic. The
observation of adsorption behaviors under low ionic strength, low pH
values, and low temperatures is beneficial to the adsorption removal of
perchlorate with granular activated carbon.
Abstract: Recently, many existing partially blind signature scheme based on a single hard problem such as factoring, discrete logarithm, residuosity or elliptic curve discrete logarithm problems. However sooner or later these systems will become broken and vulnerable, if the factoring or discrete logarithms problems are cracked. This paper proposes a secured partially blind signature scheme based on factoring (FAC) problem and elliptic curve discrete logarithms (ECDL) problem. As the proposed scheme is focused on factoring and ECDLP hard problems, it has a solid structure and will totally leave the intruder bemused because it is very unlikely to solve the two hard problems simultaneously. In order to assess the security level of the proposed scheme a performance analysis has been conducted. Results have proved that the proposed scheme effectively deals with the partial blindness, randomization, unlinkability and unforgeability properties. Apart from this we have also investigated the computation cost of the proposed scheme. The new proposed scheme is robust and it is difficult for the malevolent attacks to break our scheme.
Abstract: A mobile agent is a software which performs an
action autonomously and independently as a person or an
organizations assistance. Mobile agents are used for searching
information, retrieval information, filtering, intruder recognition in
networks, and so on. One of the important issues of mobile agent is
their security. It must consider different security issues in effective
and secured usage of mobile agent. One of those issues is the
integrity-s protection of mobile agents.
In this paper, the advantages and disadvantages of each method,
after reviewing the existing methods, is examined. Regarding to this
matter that each method has its own advantage or disadvantage, it
seems that by combining these methods, one can reach to a better
method for protecting the integrity of mobile agents. Therefore, this
method is provided in this paper and then is evaluated in terms of
existing method. Finally, this method is simulated and its results are
the sign of improving the possibility of integrity-s protection of
mobile agents.
Abstract: This paper proposes a technique to block adult images displayed in websites. The filter is designed so as to perform even in exceptional cases such as, where face detection is not possible or improper face visibility. This is achieved by using an alternative phase to extract the MFC (Most Frequent Color) from the Human Body regions estimated using a biometric of anthropometric distances between fixed rigidly connected body locations. The logical results generated can be protected from overriding by a firewall or intrusion, by encrypting the result in a SSH data packet.