An Ensemble-Based Parallel Deep Learning Classifier With PSO-BP Optimization for Malware Detection
Digital networks and systems are susceptible to malicious software (malware) attacks. Deep learning (DL) models have recently emerged as effective methods to classify and detect malware. However, DL models often relies on gradient descent optimization in learning, i.e., the Back-Propagation (BP) alg...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10187128/ |
| _version_ | 1797768435794444288 |
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| author | Mohammed Nasser Al-Andoli Kok Swee Sim Shing Chiang Tan Pey Yun Goh Chee Peng Lim |
| author_facet | Mohammed Nasser Al-Andoli Kok Swee Sim Shing Chiang Tan Pey Yun Goh Chee Peng Lim |
| author_sort | Mohammed Nasser Al-Andoli |
| collection | DOAJ |
| description | Digital networks and systems are susceptible to malicious software (malware) attacks. Deep learning (DL) models have recently emerged as effective methods to classify and detect malware. However, DL models often relies on gradient descent optimization in learning, i.e., the Back-Propagation (BP) algorithm; therefore, their training and optimization procedures suffer from several limitations, such as high computational cost and local suboptimal solutions. On the other hand, ensemble methods overcome the shortcomings of individual models by consolidating their strengths to increase performance. In this paper, we propose an ensemble-based parallel DL classifier for malware detection. In particular, a stacked ensemble learning method is developed, which leverages five DL base models and a neural network as a meta model. The DL models are trained and optimized with a hybrid optimization method based on BP and Particle Swarm Optimization (PSO) algorithms. To improve scalability and efficiency of the ensemble method, a parallel computing framework is exploited. The proposed ensemble method is evaluated using five malware datasets (namely, Drebin, NTAM, TOP-PE, DikeDataset, and ML_Android), and high accuracy rates of 99.2%, 99.3%, 98.7%, 100%, and 100% have been achieved, respectively. Its parallel implementation also significantly enhances the computational speed by a factor up to 6.75 times. These results ascertain that the proposed ensemble method is effective, efficient, and scalable, outperforming many other compared methods in malware detection. |
| first_indexed | 2024-03-12T20:54:03Z |
| format | Article |
| id | doaj.art-c76c8fe1b7294a65b403bd096fa9ff3b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T20:54:03Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c76c8fe1b7294a65b403bd096fa9ff3b2023-07-31T23:00:15ZengIEEEIEEE Access2169-35362023-01-0111763307634610.1109/ACCESS.2023.329678910187128An Ensemble-Based Parallel Deep Learning Classifier With PSO-BP Optimization for Malware DetectionMohammed Nasser Al-Andoli0https://orcid.org/0000-0001-6491-9938Kok Swee Sim1https://orcid.org/0000-0003-2976-8825Shing Chiang Tan2https://orcid.org/0000-0002-1267-1894Pey Yun Goh3https://orcid.org/0000-0003-2060-3223Chee Peng Lim4https://orcid.org/0000-0003-4191-9083Faculty of Engineering and Technology, Multimedia University, Melaka, MalaysiaFaculty of Engineering and Technology, Multimedia University, Melaka, MalaysiaFaculty of Information Science and Technology, Multimedia University, Melaka, MalaysiaFaculty of Information Science and Technology, Multimedia University, Melaka, MalaysiaInstitute for Intelligent Systems Research and Innovation, Deakin University, Geelong Waurn Ponds, VIC, AustraliaDigital networks and systems are susceptible to malicious software (malware) attacks. Deep learning (DL) models have recently emerged as effective methods to classify and detect malware. However, DL models often relies on gradient descent optimization in learning, i.e., the Back-Propagation (BP) algorithm; therefore, their training and optimization procedures suffer from several limitations, such as high computational cost and local suboptimal solutions. On the other hand, ensemble methods overcome the shortcomings of individual models by consolidating their strengths to increase performance. In this paper, we propose an ensemble-based parallel DL classifier for malware detection. In particular, a stacked ensemble learning method is developed, which leverages five DL base models and a neural network as a meta model. The DL models are trained and optimized with a hybrid optimization method based on BP and Particle Swarm Optimization (PSO) algorithms. To improve scalability and efficiency of the ensemble method, a parallel computing framework is exploited. The proposed ensemble method is evaluated using five malware datasets (namely, Drebin, NTAM, TOP-PE, DikeDataset, and ML_Android), and high accuracy rates of 99.2%, 99.3%, 98.7%, 100%, and 100% have been achieved, respectively. Its parallel implementation also significantly enhances the computational speed by a factor up to 6.75 times. These results ascertain that the proposed ensemble method is effective, efficient, and scalable, outperforming many other compared methods in malware detection.https://ieeexplore.ieee.org/document/10187128/Ensemble methodmalware detectiondeep learningparallel processingbackpropagation algorithmparticle swarm optimization |
| spellingShingle | Mohammed Nasser Al-Andoli Kok Swee Sim Shing Chiang Tan Pey Yun Goh Chee Peng Lim An Ensemble-Based Parallel Deep Learning Classifier With PSO-BP Optimization for Malware Detection IEEE Access Ensemble method malware detection deep learning parallel processing backpropagation algorithm particle swarm optimization |
| title | An Ensemble-Based Parallel Deep Learning Classifier With PSO-BP Optimization for Malware Detection |
| title_full | An Ensemble-Based Parallel Deep Learning Classifier With PSO-BP Optimization for Malware Detection |
| title_fullStr | An Ensemble-Based Parallel Deep Learning Classifier With PSO-BP Optimization for Malware Detection |
| title_full_unstemmed | An Ensemble-Based Parallel Deep Learning Classifier With PSO-BP Optimization for Malware Detection |
| title_short | An Ensemble-Based Parallel Deep Learning Classifier With PSO-BP Optimization for Malware Detection |
| title_sort | ensemble based parallel deep learning classifier with pso bp optimization for malware detection |
| topic | Ensemble method malware detection deep learning parallel processing backpropagation algorithm particle swarm optimization |
| url | https://ieeexplore.ieee.org/document/10187128/ |
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