How to cite this paper
Ramaiah, M., Chandrasekaran, V., Adla, P., Vasudevan, A., Hunitie, M & Mohammad, S. (2024). Optimal feature selection based on OCS for improved malware detection in IoT networks using an ensemble classifier.International Journal of Data and Network Science, 8(4), 2127-2140.
Refrences
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Al-Kasassbeh, M., Mohammed, S., Alauthman, M., & Almomani, A. (2020). Feature selection using machine learning to classify malware. In Handbook of computer networks and cyber security (pp. 889-904). Springer, Cham.
Almin, S. B., & Chatterjee, M. (2015). A novel approach to detect android malware. Procedia Computer Science, 45, 407– 417.
Aslan, Ö., & Yilmaz, A. A. (2021). A new malware classification framework based on deep learning algorithms. IEEE Ac-cess, 9, 87936-87951.
Azad, M. A., Riaz, F., Aftab, A., Rizvi, S. K. J., Arshad, J., & Atlam, H. F. (2022). DEEPSEL: A novel feature selection for early identification of malware in mobile applications. Future Generation Computer Systems, 129, 54-63.
Babaagba, K. O., & Adesanya, S. O. (2019, March). A study on the effect of feature selection on malware analysis using machine learning. Proceedings of the 2019 8th international conference on educational and information technology (pp. 51-55).
Baptista, I., Shiaeles, S., & Kolokotronis, N. (2019, May). A novel malware detection system based on machine learning and binary visualisation. In 2019 IEEE International Conference on Communications Workshops (ICC Workshops) (pp. 1-6). IEEE.
Bendiab, G., Shiaeles, S., Alruban, A., & Kolokotronis, N. (2020, June). IoT malware network traffic classification using visual representation and deep learning. In 2020 6th IEEE Conference on Network Softwarization (NetSoft) (pp. 444-449). IEEE.
Canfora, G., Medvet, E., Mercaldo, F., & Visaggio, C. A. (2015). Detecting android malware using sequences of system calls. Proceedings of the 3rd International Workshop on Software Development Lifecycle for Mobile, 13–20.
Chakravarthy, S. J. (2021). Wrapper-based metaheuristic optimization algorithms for android malware detection: a correl-ative analysis of firefly, bat & whale optimization. J Hunan Univ, 48(10).
D’Angelo, G., Farsimadan, E., Ficco, M., Palmieri, F., & Robustelli, A. (2023). Privacy-preserving malware detection in Android-based IoT devices through federated Markov chains. Future Generation Computer Systems, 148, 93-105.
Dawra, B., Chauhan, A. N., Rani, R., Dev, A., Bansal, P., & Sharma, A. (2023, February). Malware Classification using Deep Learning Techniques. In 2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON) (pp. 1-7). IEEE.
Fang, Z., Wang, J., Geng, J., & Kan, X. (2019). Feature selection for malware detection based on reinforcement learning. IEEE Access, 7, 176177-176187.
Huang, W., & Stokes, J. W. (2016). MtNet: a multi-task neural network for dynamic malware classification. In Detection of Intrusions and Malware, and Vulnerability Assessment: 13th International Conference, DIMVA 2016, San Sebas-tián, Spain, July 7-8, 2016, Proceedings 13 (pp. 399-418). Springer International Publishing.
Hublikar, S., & Shet, N. S. V. (2022). Hybrid Malicious Encrypted Network Traffic Flow Detection Model. In Computer Networks and Inventive Communication Technologies: Proceedings of Fifth ICCNCT 2022 (pp. 357-375). Singapore: Springer Nature Singapore.
Kalash, M., Rochan, M., Mohammed, N., Bruce, N. D., Wang, Y., & Iqbal, F. (2018, February). Malware classification with deep convolutional neural networks. In 2018 9th IFIP international conference on new technologies, mobility and security (NTMS) (pp. 1-5). IEEE
Kavitha, P. M., & Muruganantham, B. (2021). An Extensive Review on Malware Classification Based on Classifiers. In-telligent Computing and Innovation on Data Science, 371-381.
Kim, C., Chang, S. Y., Kim, J., Lee, D., & Kim, J. (2023). Automated, reliable zero-day malware detection based on autoencoding architecture. IEEE Transactions on Network and Service Management.
Kim, D. W., Shin, G. Y., & Han, M. M. (2020). Analysis of feature importance and interpretation for malware classifica-tion. Computers, Materials & Continua, 65(3), 1891-1904.
Letteri, I., Di Cecco, A., & Della Penna, G. (2020). Dataset Optimization Strategies for Malware Traffic Detection. arXiv preprint arXiv:2009.11347
Lin, K. Y., & Huang, W. R. (2020, February). Using federated learning on malware classification. In 2020 22nd Interna-tional Conference on Advanced Communication Technology (ICACT) (pp. 585-589). IEEE.
Mahajan, G., Saini, B., & Anand, S. (2019, February). Malware classification using machine learning algorithms and tools. In 2019 Second international conference on advanced computational and communication paradigms (ICACCP) (pp. 1-8). IEEE
Mangayarkarasi, R., Vanmathi, C., & Ravi, V. (2023). A robust malware traffic classifier to combat security breaches in industry 4.0 applications. Concurrency and Computation: Practice and Experience, 35(23), e7772.
Manzano, C., Meneses, C., Leger, P., & Fukuda, H. (2022). An Empirical Evaluation of Supervised Learning Methods for Network Malware Identification Based on Feature Selection. Complexity, 2022.
Marín, G., Caasas, P., & Capdehourat, G. (2021). DeepMAL-deep learning models for malware traffic detection and clas-sification. In Data Science–Analytics and Applications (pp. 105-112). Springer Vieweg, Wiesbaden.
Mohy-eddine, M., Guezzaz, A., Benkirane, S., & Azrour, M. (2023). An effective intrusion detection approach based on ensemble learning for IIoT edge computing. Journal of Computer Virology and Hacking Techniques, 19(4), 469-481.
Narayanan, M. E. (2021). Malware Classification Using Xgboost With Vote Based Backward Feature Elimination Tech-nique. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(10), 5915-5923.
Nugraha, U. (2021). Malware Classification Using Machine Learning Algorithm. Turkish Journal of Computer and Math-ematics Education (TURCOMAT), 12(8), 1834-1844
Pant, D., & Bista, R. (2021, November). Image-based Malware Classification using Deep Convolutional Neural Network and Transfer Learning. In 2021 3rd International Conference on Advanced Information Science and System (AISS 2021) (pp. 1-6).
Pascanu, R., Stokes, J. W., Sanossian, H., Marinescu, M., & Thomas, A. (2015, April). Malware classification with recur-rent networks. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1916-1920). IEEE.
Raff, E., Barker, J., Sylvester, J., Brandon, R., Catanzaro, B., & Nicholas, C. K. (2018, June). Malware detection by eating a whole exe. In Workshops at the thirty-second AAAI conference on artificial intelligence.
Raff, E., Zak, R., Cox, R., Sylvester, J., Yacci, P., Ward, R., ... & Nicholas, C. (2018). An investigation of byte n-gram features for malware classification. Journal of Computer Virology and Hacking Techniques, 14, 1-20.
Ramaiah, M., Chandrasekaran, V., Ravi, V., & Kumar, N. (2021). An intrusion detection system using optimized deep neural network architecture. Transactions on Emerging Telecommunications Technologies, 32(4), e4221.
Rao, V., & Hande, K. (2017). A comparative study of static, dynamic and hybrid analysis techniques for android malware detection. International Journal of Engineering Development and Research, 5(2), 1433-1436.
Rey, V., Sánchez, P. M. S., Celdrán, A. H., & Bovet, G. (2022). Federated learning for malware detection in IoT devices. Computer Networks, 204, 108693.
Romli, R. N., Zolkipli, M. F., & Osman, M. Z. (2021, June). Efficient feature selection analysis for accuracy mal-ware classification. In Journal of Physics: Conference Series (Vol. 1918, No. 4, p. 042140). IOP Publishing.
Saxe, J., & Berlin, K. (2015, October). Deep neural network based malware detection using two dimensional binary pro-gram features. In 2015 10th international conference on malicious and unwanted software (MALWARE) (pp. 11-20). IEEE.
Shibahara, T., Yagi, T., Akiyama, M., Chiba, D., & Yada, T. (2016, December). Efficient dynamic malware analysis based on network behavior using deep learning. In 2016 IEEE Global Communications Conference (GLOBECOM) (pp. 1-7). IEEE.
Venkatasubramanian, M., Habibi Lashkari, A., & Hakak, S. (2022, December). Federated Learning Assisted IoT Malware Detection Using Static Analysis. In Proceedings of the 2022 12th International Conference on Communication and Network Security (pp. 191-198).
Vishnukumar, R., & Ramaiah, M. (2024). Optimized deep learning-based intrusion detection framework for vehicular network. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-18.
Yoo, S., Kim, S., Kim, S., & Kang, B. B. (2021). AI-HydRa: Advanced hybrid approach using random forest and deep learning for malware classification. Information Sciences, 546, 420-435.
Yuan, Z., Lu, Y., Wang, Z., & Xue, Y. (2014, August). Droid-sec: deep learning in android malware detection. In Proceed-ings of the 2014 ACM conference on SIGCOMM (pp. 371-372).
Al-Kasassbeh, M., Mohammed, S., Alauthman, M., & Almomani, A. (2020). Feature selection using machine learning to classify malware. In Handbook of computer networks and cyber security (pp. 889-904). Springer, Cham.
Almin, S. B., & Chatterjee, M. (2015). A novel approach to detect android malware. Procedia Computer Science, 45, 407– 417.
Aslan, Ö., & Yilmaz, A. A. (2021). A new malware classification framework based on deep learning algorithms. IEEE Ac-cess, 9, 87936-87951.
Azad, M. A., Riaz, F., Aftab, A., Rizvi, S. K. J., Arshad, J., & Atlam, H. F. (2022). DEEPSEL: A novel feature selection for early identification of malware in mobile applications. Future Generation Computer Systems, 129, 54-63.
Babaagba, K. O., & Adesanya, S. O. (2019, March). A study on the effect of feature selection on malware analysis using machine learning. Proceedings of the 2019 8th international conference on educational and information technology (pp. 51-55).
Baptista, I., Shiaeles, S., & Kolokotronis, N. (2019, May). A novel malware detection system based on machine learning and binary visualisation. In 2019 IEEE International Conference on Communications Workshops (ICC Workshops) (pp. 1-6). IEEE.
Bendiab, G., Shiaeles, S., Alruban, A., & Kolokotronis, N. (2020, June). IoT malware network traffic classification using visual representation and deep learning. In 2020 6th IEEE Conference on Network Softwarization (NetSoft) (pp. 444-449). IEEE.
Canfora, G., Medvet, E., Mercaldo, F., & Visaggio, C. A. (2015). Detecting android malware using sequences of system calls. Proceedings of the 3rd International Workshop on Software Development Lifecycle for Mobile, 13–20.
Chakravarthy, S. J. (2021). Wrapper-based metaheuristic optimization algorithms for android malware detection: a correl-ative analysis of firefly, bat & whale optimization. J Hunan Univ, 48(10).
D’Angelo, G., Farsimadan, E., Ficco, M., Palmieri, F., & Robustelli, A. (2023). Privacy-preserving malware detection in Android-based IoT devices through federated Markov chains. Future Generation Computer Systems, 148, 93-105.
Dawra, B., Chauhan, A. N., Rani, R., Dev, A., Bansal, P., & Sharma, A. (2023, February). Malware Classification using Deep Learning Techniques. In 2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON) (pp. 1-7). IEEE.
Fang, Z., Wang, J., Geng, J., & Kan, X. (2019). Feature selection for malware detection based on reinforcement learning. IEEE Access, 7, 176177-176187.
Huang, W., & Stokes, J. W. (2016). MtNet: a multi-task neural network for dynamic malware classification. In Detection of Intrusions and Malware, and Vulnerability Assessment: 13th International Conference, DIMVA 2016, San Sebas-tián, Spain, July 7-8, 2016, Proceedings 13 (pp. 399-418). Springer International Publishing.
Hublikar, S., & Shet, N. S. V. (2022). Hybrid Malicious Encrypted Network Traffic Flow Detection Model. In Computer Networks and Inventive Communication Technologies: Proceedings of Fifth ICCNCT 2022 (pp. 357-375). Singapore: Springer Nature Singapore.
Kalash, M., Rochan, M., Mohammed, N., Bruce, N. D., Wang, Y., & Iqbal, F. (2018, February). Malware classification with deep convolutional neural networks. In 2018 9th IFIP international conference on new technologies, mobility and security (NTMS) (pp. 1-5). IEEE
Kavitha, P. M., & Muruganantham, B. (2021). An Extensive Review on Malware Classification Based on Classifiers. In-telligent Computing and Innovation on Data Science, 371-381.
Kim, C., Chang, S. Y., Kim, J., Lee, D., & Kim, J. (2023). Automated, reliable zero-day malware detection based on autoencoding architecture. IEEE Transactions on Network and Service Management.
Kim, D. W., Shin, G. Y., & Han, M. M. (2020). Analysis of feature importance and interpretation for malware classifica-tion. Computers, Materials & Continua, 65(3), 1891-1904.
Letteri, I., Di Cecco, A., & Della Penna, G. (2020). Dataset Optimization Strategies for Malware Traffic Detection. arXiv preprint arXiv:2009.11347
Lin, K. Y., & Huang, W. R. (2020, February). Using federated learning on malware classification. In 2020 22nd Interna-tional Conference on Advanced Communication Technology (ICACT) (pp. 585-589). IEEE.
Mahajan, G., Saini, B., & Anand, S. (2019, February). Malware classification using machine learning algorithms and tools. In 2019 Second international conference on advanced computational and communication paradigms (ICACCP) (pp. 1-8). IEEE
Mangayarkarasi, R., Vanmathi, C., & Ravi, V. (2023). A robust malware traffic classifier to combat security breaches in industry 4.0 applications. Concurrency and Computation: Practice and Experience, 35(23), e7772.
Manzano, C., Meneses, C., Leger, P., & Fukuda, H. (2022). An Empirical Evaluation of Supervised Learning Methods for Network Malware Identification Based on Feature Selection. Complexity, 2022.
Marín, G., Caasas, P., & Capdehourat, G. (2021). DeepMAL-deep learning models for malware traffic detection and clas-sification. In Data Science–Analytics and Applications (pp. 105-112). Springer Vieweg, Wiesbaden.
Mohy-eddine, M., Guezzaz, A., Benkirane, S., & Azrour, M. (2023). An effective intrusion detection approach based on ensemble learning for IIoT edge computing. Journal of Computer Virology and Hacking Techniques, 19(4), 469-481.
Narayanan, M. E. (2021). Malware Classification Using Xgboost With Vote Based Backward Feature Elimination Tech-nique. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(10), 5915-5923.
Nugraha, U. (2021). Malware Classification Using Machine Learning Algorithm. Turkish Journal of Computer and Math-ematics Education (TURCOMAT), 12(8), 1834-1844
Pant, D., & Bista, R. (2021, November). Image-based Malware Classification using Deep Convolutional Neural Network and Transfer Learning. In 2021 3rd International Conference on Advanced Information Science and System (AISS 2021) (pp. 1-6).
Pascanu, R., Stokes, J. W., Sanossian, H., Marinescu, M., & Thomas, A. (2015, April). Malware classification with recur-rent networks. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1916-1920). IEEE.
Raff, E., Barker, J., Sylvester, J., Brandon, R., Catanzaro, B., & Nicholas, C. K. (2018, June). Malware detection by eating a whole exe. In Workshops at the thirty-second AAAI conference on artificial intelligence.
Raff, E., Zak, R., Cox, R., Sylvester, J., Yacci, P., Ward, R., ... & Nicholas, C. (2018). An investigation of byte n-gram features for malware classification. Journal of Computer Virology and Hacking Techniques, 14, 1-20.
Ramaiah, M., Chandrasekaran, V., Ravi, V., & Kumar, N. (2021). An intrusion detection system using optimized deep neural network architecture. Transactions on Emerging Telecommunications Technologies, 32(4), e4221.
Rao, V., & Hande, K. (2017). A comparative study of static, dynamic and hybrid analysis techniques for android malware detection. International Journal of Engineering Development and Research, 5(2), 1433-1436.
Rey, V., Sánchez, P. M. S., Celdrán, A. H., & Bovet, G. (2022). Federated learning for malware detection in IoT devices. Computer Networks, 204, 108693.
Romli, R. N., Zolkipli, M. F., & Osman, M. Z. (2021, June). Efficient feature selection analysis for accuracy mal-ware classification. In Journal of Physics: Conference Series (Vol. 1918, No. 4, p. 042140). IOP Publishing.
Saxe, J., & Berlin, K. (2015, October). Deep neural network based malware detection using two dimensional binary pro-gram features. In 2015 10th international conference on malicious and unwanted software (MALWARE) (pp. 11-20). IEEE.
Shibahara, T., Yagi, T., Akiyama, M., Chiba, D., & Yada, T. (2016, December). Efficient dynamic malware analysis based on network behavior using deep learning. In 2016 IEEE Global Communications Conference (GLOBECOM) (pp. 1-7). IEEE.
Venkatasubramanian, M., Habibi Lashkari, A., & Hakak, S. (2022, December). Federated Learning Assisted IoT Malware Detection Using Static Analysis. In Proceedings of the 2022 12th International Conference on Communication and Network Security (pp. 191-198).
Vishnukumar, R., & Ramaiah, M. (2024). Optimized deep learning-based intrusion detection framework for vehicular network. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-18.
Yoo, S., Kim, S., Kim, S., & Kang, B. B. (2021). AI-HydRa: Advanced hybrid approach using random forest and deep learning for malware classification. Information Sciences, 546, 420-435.
Yuan, Z., Lu, Y., Wang, Z., & Xue, Y. (2014, August). Droid-sec: deep learning in android malware detection. In Proceed-ings of the 2014 ACM conference on SIGCOMM (pp. 371-372).