MATERIALS AND METHODS: We retrospectively reviewed our experience of pEVAR between April 2013 and July 2014. Primary success of the procedure was defined as closure of a common femoral artery (CFA) arteriotomy without the need for any secondary surgical or endovascular procedure within 30 days.
RESULTS: In total there were 10 pEVAR cases performed in the study period, one case in Queen Elizabeth Hospital during visiting vascular service. Patients have a mean age of 73.4 year old (66-77 year old) The mean abdominal aortic size was 7.2 cm (5.6-10.0cm). Mean femoral artery diameter was 9.0 mm on the right and 8.9 mm on the left. Mean duration of surgery was 119 minutes (98- 153 minutes). 50% of patients were discharged at post-operative day one, 30%- day two and 20%- day three. Primary success was achieved in 9 patients (90%) or in 19 CFA closures (95%). No major complication was reported.
DISCUSSION: We believe that with proper selection of patients undergoing EVAR, pEVAR offers a better option of vascular access with shorter operative time, less post- operative pain, shorter hospital stay and minimises the potential complications of a conventional femoral cutdown.
PROBLEM: Most of the existing solutions use the traditional term of frequency-inverse document frequency (TF-IDF) technique or its concept to represent malware behaviors. However, the traditional TF-IDF and the developed techniques represent the features, especially the shared ones, inaccurately because those techniques calculate a weight for each feature without considering its distribution in each class; instead, the generated weight is generated based on the distribution of the feature among all the documents. Such presumption can reduce the meaning of those features, and when those features are used to classify malware, they lead to a high false alarms.
METHOD: This study proposes a Kullback-Liebler Divergence-based Term Frequency-Probability Class Distribution (KLD-based TF-PCD) algorithm to represent the extracted features based on the differences between the probability distributions of the terms in malware and benign classes. Unlike the existing solution, the proposed algorithm increases the weights of the important features by using the Kullback-Liebler Divergence tool to measure the differences between their probability distributions in malware and benign classes.
RESULTS: The experimental results show that the proposed KLD-based TF-PCD algorithm achieved an accuracy of 0.972, the false positive rate of 0.037, and the F-measure of 0.978. Such results were significant compared to the related work studies. Thus, the proposed KLD-based TF-PCD algorithm contributes to improving the security of cyberspace.
CONCLUSION: New meaningful characteristics have been added by the proposed algorithm to promote the learned knowledge of the classifiers, and thus increase their ability to classify malicious behaviors accurately.