Abstract: The aim of this research is to design a collaborative
framework that integrates risk analysis activities into the geospatial
database design (GDD) process. Risk analysis is rarely undertaken
iteratively as part of the present GDD methods in conformance to
requirement engineering (RE) guidelines and risk standards.
Accordingly, when risk analysis is performed during the GDD, some
foreseeable risks may be overlooked and not reach the output
specifications especially when user intentions are not systematically
collected. This may lead to ill-defined requirements and ultimately in
higher risks of geospatial data misuse. The adopted approach consists
of 1) reviewing risk analysis process within the scope of RE and
GDD, 2) analyzing the challenges of risk analysis within the context
of GDD, and 3) presenting the components of a risk-based
collaborative framework that improves the collection of the
intended/forbidden usages of the data and helps geo-IT experts to
discover implicit requirements and risks.
Abstract: The aim of this article is to explain how features of attacks could be extracted from the packets. It also explains how vectors could be built and then applied to the input of any analysis stage. For analyzing, the work deploys the Feedforward-Back propagation neural network to act as misuse intrusion detection system. It uses ten types if attacks as example for training and testing the neural network. It explains how the packets are analyzed to extract features. The work shows how selecting the right features, building correct vectors and how correct identification of the training methods with nodes- number in hidden layer of any neural network affecting the accuracy of system. In addition, the work shows how to get values of optimal weights and use them to initialize the Artificial Neural Network.
Abstract: This paper presents an intrusion detection system of hybrid neural network model based on RBF and Elman. It is used for anomaly detection and misuse detection. This model has the memory function .It can detect discrete and related aggressive behavior effectively. RBF network is a real-time pattern classifier, and Elman network achieves the memory ability for former event. Based on the hybrid model intrusion detection system uses DARPA data set to do test evaluation. It uses ROC curve to display the test result intuitively. After the experiment it proves this hybrid model intrusion detection system can effectively improve the detection rate, and reduce the rate of false alarm and fail.
Abstract: Intrusion Detection Systems are increasingly a key
part of systems defense. Various approaches to Intrusion Detection
are currently being used, but they are relatively ineffective. Artificial
Intelligence plays a driving role in security services. This paper
proposes a dynamic model Intelligent Intrusion Detection System,
based on specific AI approach for intrusion detection. The
techniques that are being investigated includes neural networks and
fuzzy logic with network profiling, that uses simple data mining
techniques to process the network data. The proposed system is a
hybrid system that combines anomaly, misuse and host based
detection. Simple Fuzzy rules allow us to construct if-then rules that
reflect common ways of describing security attacks. For host based
intrusion detection we use neural-networks along with self
organizing maps. Suspicious intrusions can be traced back to its
original source path and any traffic from that particular source will
be redirected back to them in future. Both network traffic and system
audit data are used as inputs for both.
Abstract: Privacy issues commonly discussed among
researchers, practitioners, and end-users in pervasive healthcare.
Pervasive healthcare systems are applications that can support
patient-s need anytime and anywhere. However, pervasive healthcare
raises privacy concerns since it can lead to situations where patients
may not be aware that their private information is being shared and
becomes vulnerable to threat. We have systematically analyzed the
privacy issues and present a summary in tabular form to show the
relationship among the issues. The six issues identified are medical
information misuse, prescription leakage, medical information
eavesdropping, social implications for the patient, patient difficulties
in managing privacy settings, and lack of support in designing
privacy-sensitive applications. We narrow down the issues and chose
to focus on the issue of 'lack of support in designing privacysensitive
applications' by proposing a privacy-sensitive architecture
specifically designed for pervasive healthcare monitoring systems.
Abstract: Social networking is one of the most successful and popular tools to emerge from the Web 2.0 era. However, the increased interconnectivity and access to peoples- personal lives and information has created a plethora of opportunities for the nefarious side of human nature to manifest. This paper categorizes and describes the major types of anti-social behavior and criminal activity that can arise through undisciplined use and/or misuse of social media. We specifically address identity theft, misrepresentation of information posted, cyber bullying, children and social networking, and social networking in the work place. Recommendations are provided for how to reduce the risk of being the victim of a crime or engaging in embarrassing behavior that could irrevocably harm one-s reputation either professionally or personally. We also discuss what responsibilities social networking companies have to protect their users and also what law enforcement and policy makers can do to help alleviate the problems.
Abstract: In this paper we used data mining techniques to
identify outlier patients who are using large amount of drugs over a
long period of time. Any healthcare or health insurance system
should deal with the quantities of drugs utilized by chronic diseases
patients. In Kingdom of Bahrain, about 20% of health budget is spent
on medications. For the managers of healthcare systems, there is no
enough information about the ways of drug utilization by chronic
diseases patients, is there any misuse or is there outliers patients. In
this work, which has been done in cooperation with information
department in the Bahrain Defence Force hospital; we select the data
for Cardiac patients in the period starting from 1/1/2008 to
December 31/12/2008 to be the data for the model in this paper. We
used three techniques for finding the drug utilization for cardiac
patients. First we applied a clustering technique, followed by
measuring of clustering validity, and finally we applied a decision
tree as classification algorithm. The clustering results is divided into
three clusters according to the drug utilization, for 1603 patients, who
received 15,806 prescriptions during this period can be partitioned
into three groups, where 23 patients (2.59%) who received 1316
prescriptions (8.32%) are classified to be outliers. The classification
algorithm shows that the use of average drug utilization and the age,
and the gender of the patient can be considered to be the main
predictive factors in the induced model.
Abstract: In recent years, everything is trending toward digitalization
and with the rapid development of the Internet technologies,
digital media needs to be transmitted conveniently over the network.
Attacks, misuse or unauthorized access of information is of great
concern today which makes the protection of documents through
digital media a priority problem. This urges us to devise new data
hiding techniques to protect and secure the data of vital significance.
In this respect, steganography often comes to the fore as a tool for
hiding information. Steganography is a process that involves hiding
a message in an appropriate carrier like image or audio. It is of
Greek origin and means "covered or hidden writing". The goal of
steganography is covert communication. Here the carrier can be sent
to a receiver without any one except the authenticated receiver only
knows existence of the information. Considerable amount of work
has been carried out by different researchers on steganography. In this
work the authors propose a novel Steganographic method for hiding
information within the spatial domain of the gray scale image. The
proposed approach works by selecting the embedding pixels using
some mathematical function and then finds the 8 neighborhood of
the each selected pixel and map each bit of the secret message in
each of the neighbor pixel coordinate position in a specified manner.
Before embedding a checking has been done to find out whether the
selected pixel or its neighbor lies at the boundary of the image or not.
This solution is independent of the nature of the data to be hidden
and produces a stego image with minimum degradation.