Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features

Şahin, Canan Batur; Dinler, Özlem Batur; Abualigah, Laith

doi:10.1007/s10489-021-02324-3

Cited by 38 publications

(22 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thereafter, computing the classification error after removing the irrelevant features that corresponding to zeros in BU. This is performed by using Equation (12).…”

Section: Proposed Aoaga Feature Selectionmentioning

confidence: 99%

“…In Equation ( 12), λ ∈ [0, 1] refers to the weight applied to balance between the two sides of Equation (12). N F refers to the number of features, and |BU i | is the number of selected features corresponding to ones inside U i .…”

Section: Proposed Aoaga Feature Selectionmentioning

confidence: 99%

“…The backward selection strategy collects all characteristics; it then eliminates one attribute at each step (whose removal reduces the error the most). This method is replicated until some further elimination increases the fault [11,12]. The forward selection strategy starts with a blank set; it adds one element at each stage that reduces the error the most before another addition does not mainly reduce it [13,14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model

et al. 2021

Self Cite

View full text Add to dashboard Cite

Feature selection is a well-known prepossessing procedure, and it is considered a challenging problem in many domains, such as data mining, text mining, medicine, biology, public health, image processing, data clustering, and others. This paper proposes a novel feature selection method, called AOAGA, using an improved metaheuristic optimization method that combines the conventional Arithmetic Optimization Algorithm (AOA) with the Genetic Algorithm (GA) operators. The AOA is a recently proposed optimizer; it has been employed to solve several benchmark and engineering problems and has shown a promising performance. The main aim behind the modification of the AOA is to enhance its search strategies. The conventional version suffers from weaknesses, the local search strategy, and the trade-off between the search strategies. Therefore, the operators of the GA can overcome the shortcomings of the conventional AOA. The proposed AOAGA was evaluated with several well-known benchmark datasets, using several standard evaluation criteria, namely accuracy, number of selected features, and fitness function. Finally, the results were compared with the state-of-the-art techniques to prove the performance of the proposed AOAGA method. Moreover, to further assess the performance of the proposed AOAGA method, two real-world problems containing gene datasets were used. The findings of this paper illustrated that the proposed AOAGA method finds new best solutions for several test cases, and it got promising results compared to other comparative methods published in the literature.

show abstract

“…Thereafter, computing the classification error after removing the irrelevant features that corresponding to zeros in BU. This is performed by using Equation (12).…”

Section: Proposed Aoaga Feature Selectionmentioning

confidence: 99%

Section: Proposed Aoaga Feature Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Image segmentation has been tackled using a variety of approaches and algorithms [20]. Examples of the used optimization algorithms are the Bat Algorithm (BA) [21], Firefly Algorithm (FA) [22], Genetic Algorithm (GA) [23], Gray Wolf Optimizer (GWO) [24,25], Dragonfly Algorithm (DA) [26], Moth-Flame Optimization Algorithm (MFO) [27], Marine Predators Algorithm (MPA) [28], Arithmetic Optimization Algorithm (AOA) [29], Aquila Optimizer (AO) [30], Krill Herd Optimizer (KHO) [31], Harris Hawks Optimizer (HHO) [32], Red Fox Optimization Algorithm (RFOA) [33], Artificial Bee Colony Algorithm (ABC) [34], and Artificial Ecosystem-based Optimization [35]. Many other optimizers can be found in [36,37].…”

Section: Introductionmentioning

confidence: 99%

A Novel Evolutionary Arithmetic Optimization Algorithm for Multilevel Thresholding Segmentation of COVID-19 CT Images

et al. 2021

Self Cite

View full text Add to dashboard Cite

One of the most crucial aspects of image segmentation is multilevel thresholding. However, multilevel thresholding becomes increasingly more computationally complex as the number of thresholds grows. In order to address this defect, this paper proposes a new multilevel thresholding approach based on the Evolutionary Arithmetic Optimization Algorithm (AOA). The arithmetic operators in science were the inspiration for AOA. DAOA is the proposed approach, which employs the Differential Evolution technique to enhance the AOA local research. The proposed algorithm is applied to the multilevel thresholding problem, using Kapur’s measure between class variance functions. The suggested DAOA is used to evaluate images, using eight standard test images from two different groups: nature and CT COVID-19 images. Peak signal-to-noise ratio (PSNR) and structural similarity index test (SSIM) are standard evaluation measures used to determine the accuracy of segmented images. The proposed DAOA method’s efficiency is evaluated and compared to other multilevel thresholding methods. The findings are presented with a number of different threshold values (i.e., 2, 3, 4, 5, and 6). According to the experimental results, the proposed DAOA process is better and produces higher-quality solutions than other comparative approaches. Moreover, it achieved better-segmented images, PSNR, and SSIM values. In addition, the proposed DAOA is ranked the first method in all test cases.

show abstract

“…Vulnerability detection play a significant part in software security and quality [1]. This paper discussed the subject of detecting the correlation of the most relevant vulnerability metrics in software maintainability by utilizing deep learning.…”

Section: Introductionmentioning

confidence: 99%

Yazılım Sürdürülebilirlik Tahmininde Güvenlik Açığı Yazılım Metriklerinin Rolü

Şahin¹

2021

European Journal of Science and Technology

View full text Add to dashboard Cite

Software maintainability is among the basic quality features of software engineering. Vulnerability prediction is crucial to protect software maintainability from attacks for cybersecurity. Hence, managing vulnerability in an accurate way is an important phase for the efficient prediction of software maintenance. The existing technologies have achieved many good results in vulnerability detection, but no significant results have been obtained on how effective vulnerability metrics for software maintainability prediction is. As far as we know, this paper is the first study that applies the Deep Learning-based Symbiotic Immune Network Model to develop a software maintainability prediction model using vulnerability software metrics. This study proposes a novel methodology capable of discovering software maintainability metrics in open-source software programs efficiently and accurately. The current study also tries to identify vulnerability metrics frequently utilized in software maintainability. In this paper, five commonly employed open-source projects subjected to attacks, such as Mozilla, Linux Kernel, Xen Hypervisor, glibc, and httpd, are used. In the scope of this research, mentioned five open-source software projects were used as datasets, and they were analyzed with their effect on software maintainability prediction. The analysis of the software metrics was performed, and the descriptive statistics of the software metrics were presented. The current research obtained results of software metrics that accurately predicting software maintenance. Furthermore, the experimental findings confirm the effectiveness of the obtained vulnerability metrics for predicting software maintainability. Our experimental results claim that the proposed Deep Learning-based Symbiotic Immune Network Model enables the prediction of software maintainability to be substantially more effective.

show abstract

Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features

Cited by 38 publications

References 34 publications

Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model

Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model

A Novel Evolutionary Arithmetic Optimization Algorithm for Multilevel Thresholding Segmentation of COVID-19 CT Images

Yazılım Sürdürülebilirlik Tahmininde Güvenlik Açığı Yazılım Metriklerinin Rolü

Contact Info

Product

Resources

About