Abstract: Second line antiretroviral therapy (ART) regimen is
used when patients fail their first line regimen. There are many
factors such as non-adherence, drug resistance as well as virological
and immunological failure that lead to second line highly active
antiretroviral therapy (HAART) regimen treatment failure. This study
was aimed at determining predictor factors to treatment failure with
second line HAART and analyzing median survival time.
An observational, retrospective study was conducted in Sungai
Buloh Hospital (HSB) to assess current status of HIV patients treated
with second line HAART regimen. Convenience sampling was used
and 104 patients were included based on the study’s inclusion and
exclusion criteria. Data was collected for six months i.e. from July
until December 2013. Data was then analysed using SPSS version 18.
Kaplan-Meier and Cox regression analyses were used to measure
median survival times and predictor factors for treatment failure.
The study population consisted mainly of male subjects, aged 30-
45 years, who were heterosexual, and had HIV infection for less than
6 years. The most common second line HAART regimen given was
lopinavir/ritonavir (LPV/r)-based combination. Kaplan-Meier
analysis showed that patients on LPV/r demonstrated longer median
survival times than patients on indinavir/ritonavir (IDV/r) based
combination (p
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: 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.