ORCID Profile
0000-0002-9265-9819
Current Organisation
University of Jordan
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Publisher: Springer International Publishing
Date: 02-02-2019
Publisher: Springer International Publishing
Date: 02-02-2019
Publisher: MDPI AG
Date: 21-03-2022
DOI: 10.3390/MATH10060999
Abstract: Cyber-attacks and unauthorized application usage have increased due to the extensive use of Internet services and applications over computer networks, posing a threat to the service’s availability and consumers’ privacy. A network Intrusion Detection System (IDS) aims to detect aberrant traffic behavior that firewalls cannot detect. In IDSs, dimension reduction using the feature selection strategy has been shown to be more efficient. By reducing the data dimension and eliminating irrelevant and noisy data, several bio-inspired algorithms have been employed to improve the performance of an IDS. This paper discusses a modified bio-inspired algorithm, which is the Grey Wolf Optimization algorithm (GWO), that enhances the efficacy of the IDS in detecting both normal and anomalous traffic in the network. The main improvements cover the smart initialization phase that combines the filter and wrapper approaches to ensure that the informative features will be included in early iterations. In addition, we adopted a high-speed classification method, the Extreme Learning Machine (ELM), and used the modified GWO to tune the ELM’s parameters. The proposed technique was tested against various meta-heuristic algorithms using the UNSWNB-15 dataset. Because the generic attack is the most common attack type in the dataset, the primary goal of this paper was to detect generic attacks in network traffic. The proposed model outperformed other methods in minimizing the crossover error rate and false positive rate to less than 30%. Furthermore, it obtained the best results with 81%, 78%, and 84% for the accuracy, F1-score, and G-mean measures, respectively.
Publisher: Springer International Publishing
Date: 02-02-2019
Publisher: Springer Science and Business Media LLC
Date: 29-01-2019
Publisher: Springer International Publishing
Date: 02-02-2019
Publisher: Springer Science and Business Media LLC
Date: 02-01-2018
Publisher: Elsevier BV
Date: 02-2020
Publisher: Elsevier BV
Date: 06-2018
Publisher: Springer Science and Business Media LLC
Date: 19-01-2018
Publisher: Elsevier BV
Date: 03-2019
Publisher: Springer Science and Business Media LLC
Date: 09-03-2019
Publisher: Springer Science and Business Media LLC
Date: 15-02-2019
Publisher: Elsevier BV
Date: 08-2018
Publisher: Springer Science and Business Media LLC
Date: 05-03-2016
Publisher: Elsevier BV
Date: 04-2018
Publisher: Cambridge University Press (CUP)
Date: 24-07-2019
DOI: 10.1017/S1047951119001549
Abstract: Recurrent aortic arch obstruction following the Norwood procedure is recognised as an important complication. Balloon arch angioplasty is associated with a high recoarctation rate. We sought to evaluate the prevalence and outcome of stent implantation for recoarctation in children following Norwood or Damus–Kaye–Stansel procedure over the past decade at a single national cardiology centre. Of 114 children who underwent Norwood procedure or Damus–Kaye–Stansel procedure between January 2003 and June 2013, 80 patients survived. Of these 15 children underwent stent implantation for recoarctation. Six of these patients had previous balloon angioplasty. The median age at stent implantation was 4.4 months (range 2–82 months). The median peak aortic arch gradient at catheterisation decreased from 26mmHg (range 10–70mmHg) to 2mmHg (range 0–20mmHg). The median luminal diameter increased from 4.7 mm (range 3.2–7.9 mm) to 8.6 mm (range 6.2–10.9 mm). The median coarctation index increased by 0.49 (range = 0.24–0.64). A Valeo stent was employed in 11 children, a Palmaz Genesis stent in 2 patients, a MultiLink stent in 1 child, and a Jomed covered stent in 1 child. Two factors were associated with the need for stent placement: previous arch angioplasty (p valve & 0.001, χ -square 11.5) and borderline left ventricle (p = 0.04, χ -square = 4.1). Stent migration occurred in one child. There were two deaths related to poor right ventricular systolic function and severe tricuspid regurgitation. Six patients underwent redilation of the stent with no complications. The prevalence of recurrent aortic arch obstruction following Norwood/Damus–Kaye–Stansel procedure was 18%. Stent implantation is safe and reliably eliminates the aortic obstruction. Redilation can be successfully achieved to accommodate somatic growth or development of stent recoarctation.
Publisher: World Scientific Pub Co Pte Lt
Date: 27-10-2016
DOI: 10.1142/S0218213016500330
Abstract: Evolutionary Neural Networks are proven to be beneficial in solving challenging datasets mainly due to the high local optima avoidance. Stochastic operators in such techniques reduce the probability of stagnation in local solutions and assist them to supersede conventional training algorithms such as Back Propagation (BP) and Levenberg-Marquardt (LM). According to the No-Free-Lunch (NFL), however, there is no optimization technique for solving all optimization problems. This means that a Neural Network trained by a new algorithm has the potential to solve a new set of problems or outperform the current techniques in solving existing problems. This motivates our attempts to investigate the efficiency of the recently proposed Evolutionary Algorithm called Lightning Search Algorithm (LSA) in training Neural Network for the first time in the literature. The LSA-based trainer is benchmarked on 16 popular medical diagnosis problems and compared to BP, LM, and 6 other evolutionary trainers. The quantitative and qualitative results show that the LSA algorithm is able to show not only better local solutions avoidance but also faster convergence speed compared to the other algorithms employed. In addition, the statistical test conducted proves that the LSA-based trainer is significantly superior in comparison with the current algorithms on the majority of datasets.
Publisher: Springer Science and Business Media LLC
Date: 25-11-2018
Publisher: Springer Science and Business Media LLC
Date: 29-07-2019
Publisher: Elsevier BV
Date: 10-2018
Publisher: Elsevier BV
Date: 2023
Publisher: Elsevier BV
Date: 09-2019
Publisher: Elsevier BV
Date: 08-2019
Publisher: Elsevier BV
Date: 09-2020
Publisher: Springer Science and Business Media LLC
Date: 21-11-2016
Publisher: Springer Science and Business Media LLC
Date: 05-02-2021
Publisher: Elsevier
Date: 2017
Publisher: Elsevier BV
Date: 12-2018
Publisher: Springer Science and Business Media LLC
Date: 30-07-2018
Publisher: Springer Science and Business Media LLC
Date: 04-08-2018
Publisher: Springer International Publishing
Date: 02-02-2019
Publisher: Springer Science and Business Media LLC
Date: 17-07-2018
Publisher: Springer Science and Business Media LLC
Date: 24-08-2016
Publisher: Springer Science and Business Media LLC
Date: 02-04-2019
Publisher: Springer Science and Business Media LLC
Date: 27-02-2023
No related grants have been discovered for Ibrahim Aljarah.