Abstract: Intrusion detection systems (IDS) are the main components of network security. These systems analyze the network events for intrusion detection. The design of an IDS is through the training of normal traffic data or attack. The methods of machine learning are the best ways to design IDSs. In the method presented in this article, the pruning algorithm of C5.0 decision tree is being used to reduce the features of traffic data used and training IDS by the least square vector algorithm (LS-SVM). Then, the remaining features are arranged according to the predictor importance criterion. The least important features are eliminated in the order. The remaining features of this stage, which have created the highest level of accuracy in LS-SVM, are selected as the final features. The features obtained, compared to other similar articles which have examined the selected features in the least squared support vector machine model, are better in the accuracy, true positive rate, and false positive. The results are tested by the UNSW-NB15 dataset.
Abstract: To evaluate the factors which predetermine the
coronary artery disease in patients having positive Exercise Tolerance
Test (ETT) that is treadmill results and coronary artery findings. This
descriptive study was conducted at Department of Cardiology,
Ibrahim Cardiac Hospital & Research Institute, Dhaka, Bangladesh
from 1st January, 2014 to 31st August, 2014. All patients who had
done ETT (treadmill) for chest pain diagnosis were studied. One
hundred and four patients underwent coronary angiogram after
positive treadmill result. Patients were divided into two groups
depending upon the angiographic findings, i.e. true positive and false
positive. Positive treadmill test patients who have coronary artery
involvement these are called true positive and who have no
involvement they are called false positive group. Both groups were
compared with each other. Out of 104 patients, 81 (77.9%) patients
had true positive ETT and 23 (22.1%) patients had false positive
ETT. The mean age of patients in positive ETT was 53.46± 8.06
years and male mean age was 53.63±8.36 years and female was
52.87±7.0 years. Sixty nine (85.19%) male patients and twelve
(14.81%) female patients had true positive ETT, whereas 15
(65.21%) males and 8 (34.79%) females had false positive ETT, this
was statistically significant (p
Abstract: In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion detection system (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw dataset for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle component analysis (PCA), Linear Discriminant Analysis (LDA) and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. This optimal feature subset is used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) are used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.