DCW-RNN: Improving Class Level Metrics for Software Vulnerability Detection Using Artificial Immune System with Clock-Work Recurrent Neural Network
As the defenses evolve, so do the solutions to a software vulnerability. The primary reason for security incidents, e.g., cyber-attacks, originates from software vulnerabilities. It is challenging to enhance the performance of software processes and determine and eliminate software vulnerabilities....
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| Vydané v: | 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) s. 1 - 8 |
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| Jazyk: | English |
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IEEE
25.08.2021
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| Abstract | As the defenses evolve, so do the solutions to a software vulnerability. The primary reason for security incidents, e.g., cyber-attacks, originates from software vulnerabilities. It is challenging to enhance the performance of software processes and determine and eliminate software vulnerabilities. Thus, the development of algorithms with higher security to be applied to possible security issues in software represents a significant research subject for researchers in the domain of software security. The basis of the Dendritic Cell Algorithm (DCA), which is an emerging evolutionary algorithm, constitutes the behavior of specific immune agents, called dendritic cells (DCs). Till now, no strategy or idea has already been adopted on the Clock-Work Recurrent Neural Network (RNN) based Dendritic cell algorithm on vulnerability detection problems. In the present research, the first Clock-Work RNN based Dendritic Cell Algorithm (DCA) was suggested to identify complex dependencies between vulnerable object-oriented software metrics. The suggested method establishes immunity in software vulnerability prediction models to analyze the comparison of the Artificial Immune System Algorithms. The current paper involves the enhanced Clock-Work RNN based Dendritic Cell Algorithm, Genetic Algorithm (GA), and Clonal Selection Algorithm (CLONALG). Furthermore, comparison some studies was made on the basis Artificial Immune System (AIS) algorithms, such as Negative Selection Algorithm (NSA), Cellular Automata (CA), Membrane Computing (P-Systems). The experimental findings of our study demonstrate that our approach was computationally efficient on three different Java projects: Apache Tomcat (releases 6 and 7), Apache CXF, and the Stanford SecuriBench datasets. |
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| AbstractList | As the defenses evolve, so do the solutions to a software vulnerability. The primary reason for security incidents, e.g., cyber-attacks, originates from software vulnerabilities. It is challenging to enhance the performance of software processes and determine and eliminate software vulnerabilities. Thus, the development of algorithms with higher security to be applied to possible security issues in software represents a significant research subject for researchers in the domain of software security. The basis of the Dendritic Cell Algorithm (DCA), which is an emerging evolutionary algorithm, constitutes the behavior of specific immune agents, called dendritic cells (DCs). Till now, no strategy or idea has already been adopted on the Clock-Work Recurrent Neural Network (RNN) based Dendritic cell algorithm on vulnerability detection problems. In the present research, the first Clock-Work RNN based Dendritic Cell Algorithm (DCA) was suggested to identify complex dependencies between vulnerable object-oriented software metrics. The suggested method establishes immunity in software vulnerability prediction models to analyze the comparison of the Artificial Immune System Algorithms. The current paper involves the enhanced Clock-Work RNN based Dendritic Cell Algorithm, Genetic Algorithm (GA), and Clonal Selection Algorithm (CLONALG). Furthermore, comparison some studies was made on the basis Artificial Immune System (AIS) algorithms, such as Negative Selection Algorithm (NSA), Cellular Automata (CA), Membrane Computing (P-Systems). The experimental findings of our study demonstrate that our approach was computationally efficient on three different Java projects: Apache Tomcat (releases 6 and 7), Apache CXF, and the Stanford SecuriBench datasets. |
| Author | Sahin, Canan Batur |
| Author_xml | – sequence: 1 givenname: Canan Batur surname: Sahin fullname: Sahin, Canan Batur email: canan.batur@ozal.edu.tr organization: Malatya Turgut Ozal University,Faculty of Engineering and Natural Sciences,Malatya,Turkey |
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| PublicationTitle | 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) |
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| Snippet | As the defenses evolve, so do the solutions to a software vulnerability. The primary reason for security incidents, e.g., cyber-attacks, originates from... |
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| SubjectTerms | clock-work dendritic cell Measurement object-oriented metrics optimization Prediction algorithms recurrent neural network Recurrent neural networks Software Software algorithms Software metrics software vulnerability Technological innovation |
| Title | DCW-RNN: Improving Class Level Metrics for Software Vulnerability Detection Using Artificial Immune System with Clock-Work Recurrent Neural Network |
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