DCW-RNN: Improving Class Level Metrics for Software Vulnerability Detection Using Artificial Immune System with Clock-Work Recurrent Neural Network

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dc.authorid0000-0002-2131-6368en_US
dc.contributor.authorBatur Şahin, Canan
dc.date.accessioned2021-11-26T06:35:15Z
dc.date.available2021-11-26T06:35:15Z
dc.date.issued2021en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.description2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021Kocaeli25 August 2021 through 27 August 2021Code 172175. Date of Conference: 25-27 Aug. 2021en_US
dc.description.abstractAs 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.en_US
dc.identifier.citationŞahin, C. B. (2021, August). DCW-RNN: Improving Class Level Metrics for Software Vulnerability Detection Using Artificial Immune System with Clock-Work Recurrent Neural Network. In 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) (pp. 1-8). IEEE.en_US
dc.identifier.doi10.1109/INISTA52262.2021.9548609
dc.identifier.endpage8en_US
dc.identifier.isbn978-166543603-8
dc.identifier.scopus2-s2.0-85116610484en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1109/INISTA52262.2021.9548609
dc.identifier.urihttps://hdl.handle.net/20.500.12899/494
dc.indekslendigikaynakScopusen_US
dc.institutionauthorBatur Şahin, Canan
dc.language.isoenen_US
dc.publisherIEEE (Institute of Electrical and Electronics Engineers)en_US
dc.relation.ispartof2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClock-worken_US
dc.subjectDendritic cellen_US
dc.subjectOptimizationen_US
dc.subjectSoftware vulnerabilityen_US
dc.subjectRecurrent neural networken_US
dc.subjectObjectoriented metricsen_US
dc.titleDCW-RNN: Improving Class Level Metrics for Software Vulnerability Detection Using Artificial Immune System with Clock-Work Recurrent Neural Networken_US
dc.typeConference Objecten_US

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