Um Mapeamento Sistemático sobre Detecção de Ataques em Redes de Computadores
Resumo
Durante a pandemia de COVID-19, houve uma grande repercussão de notícias sobre empresas sendo atacadas por cibercriminosos. Nesse contexto, cresceram as pesquisas que propunham diminuir o impacto dos ataques à rede com algoritmos de Inteligência Artificial (IA). Este trabalho apresenta um mapeamento sistemático no âmbito da detecção de ataques às redes de computadores. Inicialmente, são identificados os algoritmos e os bancos de dados mais utilizados, além disso, os tipos de ataques, assim como a quantidade de amostras. Posteriormente, expõe-se a ausência de bancos de dados com ataques atuais, o desequilíbrio de amostras e soluções de arquitetura com mais de um algoritmo de IA.Referências
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Almseidin, M., Alzubi, M., Kovacs, S., and Alkasassbeh, M. (2017). Evaluation of machine learning algorithms for intrusion detection system. In 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY), pages 000277–000282.
Aminanto, M. E., Wicaksono, R. S. H., Aminanto, A. E., Tanuwidjaja, H. C., Yola, L., and Kim, K. (2022). Multi-class intrusion detection using two-channel color mapping in ieee 802.11 wireless network. IEEE Access, 10:36791–36801.
Attou, H., Guezzaz, A., Benkirane, S., Azrour, M., and Farhaoui, Y. (2023). Cloud-based intrusion detection approach using machine learning techniques. Big Data Mining and Analytics, 6(3):311–320.
Ayala, C., Jimenez, K., Loza-Aguirre, E., and Andrade, R. O. (2021). A hybrid recommender system for cybersecurity based on a rating approach. In Daimi, K., Arabnia, H. R., Deligiannidis, L., Hwang, M.-S., and Tinetti, F. G., editors, Advances in Security, Networks, and Internet of Things, pages 397–409, Cham. Springer International Publishing.
Bentes, E., Figueiredo, Y., and Campos, L. (2021). Aplicação de algoritmos de aprendizado de máquina para detecção de intrusão. In Anais Estendidos do XXXIX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 209–216, Porto Alegre, RS, Brasil. SBC.
Beran, M., Hrdina, F., Kouřil, D., Ošlejšek, R., and Zákopčanová, K. (2020). Exploratory analysis of file system metadata for rapid investigation of security incidents. In 2020 IEEE Symposium on Visualization for Cyber Security (VizSec), pages 11–20, Salt Lake City, UT, USA,. Institute of Electrical and Electronics Engineers Inc.
Brisse, R., Boche, S., Majorczyk, F., Lalande, J.-F., and Lalande, J.-F. (2021). Kraken: A knowledge-based recommender system for analysts, to kick exploration up a notch. Technical report.
Cerda, B. M., Yuan, S., and Chen, L. (2021). Phishing detection using deep learning. In Daimi, K., Arabnia, H. R., Deligiannidis, L., Hwang, M.-S., and Tinetti, F. G.,editors, Advances in Security, Networks, and Internet of Things, pages 117–128, Cham. Springer International Publishing.
da Costa Santos, C. M., de Mattos Pimenta, C. A., and Nobre, M. R. C. (2007). The pico strategy for the research question construction and evidence search. Revista Latino-Americana de Enfermagem, 15:508–511.
Dhanya, K., Vajipayajula, S., Srinivasan, K., Tibrewal, A., Kumar, T. S., and Kumar, T. G. (2023). Detection of network attacks using machine learning and deep learning models. Procedia Computer Science, 218:57–66.
Gaber, T., El-Ghamry, A., and Hassanien, A. E. (2022). Injection attack detection using machine learning for smart iot applications. Physical Communication, 52:101685.
Gatefy (2021). Como o covid-19 impactou os crimes cibernéticos, segundo a europol. Acesso em: 05 mar. 2023.
Hnamte, V. and Hussain, J. (2023). Dcnnbilstm: An efficient hybrid deep learning-based intrusion detection system. Telematics and Informatics Reports, 10:100053.
Jain, S., Pawar, P. M., and Muthalagu, R. (2022). Hybrid intelligent intrusion detection system for internet of things. Telematics and Informatics Reports, 8:100030.
Kanimozhi, V. and Jacob, T. P. (2021). Artificial intelligence outflanks all other machine learning classifiers in network intrusion detection system on the realistic cyber dataset cse-cic-ids2018 using cloud computing. ICT Express, 7(3):366–370.
Karthika, R. and Maheswari, M. (2022). Detection analysis of malicious cyber attacks using machine learning algorithms. Materials Today: Proceedings, 68:26–34. 6th International Conference on Recent Advances in Material Chemistry.
Kilincer, I. F., Ertam, F., and Sengur, A. (2021). Machine learning methods for cyber security intrusion detection: Datasets and comparative study. Computer Networks, 188:107840.
Kim, J., Shin, N., Jo, S. Y., and Kim, S. H. (2017). Method of intrusion detection using deep neural network. In 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), pages 313–316, Jeju. IEEE.
Kitchenham, B. A. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering (ebse 2007-001). Technical report, Keele University and Durham University Joint Report.
Leevy, J. L., Hancock, J., Zuech, R., and Khoshgoftaar, T. M. (2021). Detecting cybersecurity attacks across different network features and learners. Journal of Big Data, 8(1):38.
Lucas, T., Costa, K., Moraes, E., Júnior, P. H., and Neves, M. (2021). Stacking-based committees para detecção de ataques em redes de computadores - uma abordagem por exaustão. In Anais do XXXIX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 644–657, Porto Alegre, RS, Brasil. SBC.
Mushtaq, E., Zameer, A., and Khan, A. (2022). A two-stage stacked ensemble intrusion detection system using five base classifiers and mlp with optimal feature selection. Microprocessors and Microsystems, 94:104660.
Patgiri, R., Varshney, U., Akutota, T., and Kunde, R. (2018). An investigation on intrusion detection system using machine learning. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1684–1691.
Report, S. (2023). Ataques cibernéticos no trabalho remoto mais que triplicaram durante a pandemia.
Sarhan, M., Layeghy, S., Moustafa, N., and Portmann, M. (2022). Towards a standard feature set of nids datasets. Mobile Networks and Applications.
Sayed, M. S. E., Le-Khac, N.-A., Azer, M. A., and Jurcut, A. D. (2022). A flow-based anomaly detection approach with feature selection method against ddos attacks in sdns. IEEE Transactions on Cognitive Communications and Networking, 8(4):1862–1880.
Siddiqi, M. A. and Pak, W. (2022). Tier-based optimization for synthesized network intrusion detection system. IEEE Access, 10:108530–108544.
Sousa, W. T. M. and Silva, C. A. (2022). Análise de desempenho em algoritmos de aprendizagem de máquina na detecção de intrusão baseada em fluxo de rede usando o conjunto de dados unsw-nb15. Revista de Sistemas e Computação, 12(2):51–57.
Vishwakarma, M. and Kesswani, N. (2023). A new two-phase intrusion detection system with naı̈ve bayes machine learning for data classification and elliptic envelop method for anomaly detection. Decision Analytics Journal, 7:100233.
Wang, J. A., Guo, M., Wang, H., and Zhou, L. (2012). Measuring and ranking attacks based on vulnerability analysis. Information Systems and e-Business Management, 10(4):455–490.
Wu, Z., Zhang, H., Wang, P., and Sun, Z. (2022). Rtids: A robust transformer-based approach for intrusion detection system. IEEE Access, 10:64375–64387.
Advisor, C. (2021). Alerta causa fadiga e queda de produtividade de equipes no soc.
Almseidin, M., Alzubi, M., Kovacs, S., and Alkasassbeh, M. (2017). Evaluation of machine learning algorithms for intrusion detection system. In 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY), pages 000277–000282.
Aminanto, M. E., Wicaksono, R. S. H., Aminanto, A. E., Tanuwidjaja, H. C., Yola, L., and Kim, K. (2022). Multi-class intrusion detection using two-channel color mapping in ieee 802.11 wireless network. IEEE Access, 10:36791–36801.
Attou, H., Guezzaz, A., Benkirane, S., Azrour, M., and Farhaoui, Y. (2023). Cloud-based intrusion detection approach using machine learning techniques. Big Data Mining and Analytics, 6(3):311–320.
Ayala, C., Jimenez, K., Loza-Aguirre, E., and Andrade, R. O. (2021). A hybrid recommender system for cybersecurity based on a rating approach. In Daimi, K., Arabnia, H. R., Deligiannidis, L., Hwang, M.-S., and Tinetti, F. G., editors, Advances in Security, Networks, and Internet of Things, pages 397–409, Cham. Springer International Publishing.
Bentes, E., Figueiredo, Y., and Campos, L. (2021). Aplicação de algoritmos de aprendizado de máquina para detecção de intrusão. In Anais Estendidos do XXXIX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 209–216, Porto Alegre, RS, Brasil. SBC.
Beran, M., Hrdina, F., Kouřil, D., Ošlejšek, R., and Zákopčanová, K. (2020). Exploratory analysis of file system metadata for rapid investigation of security incidents. In 2020 IEEE Symposium on Visualization for Cyber Security (VizSec), pages 11–20, Salt Lake City, UT, USA,. Institute of Electrical and Electronics Engineers Inc.
Brisse, R., Boche, S., Majorczyk, F., Lalande, J.-F., and Lalande, J.-F. (2021). Kraken: A knowledge-based recommender system for analysts, to kick exploration up a notch. Technical report.
Cerda, B. M., Yuan, S., and Chen, L. (2021). Phishing detection using deep learning. In Daimi, K., Arabnia, H. R., Deligiannidis, L., Hwang, M.-S., and Tinetti, F. G.,editors, Advances in Security, Networks, and Internet of Things, pages 117–128, Cham. Springer International Publishing.
da Costa Santos, C. M., de Mattos Pimenta, C. A., and Nobre, M. R. C. (2007). The pico strategy for the research question construction and evidence search. Revista Latino-Americana de Enfermagem, 15:508–511.
Dhanya, K., Vajipayajula, S., Srinivasan, K., Tibrewal, A., Kumar, T. S., and Kumar, T. G. (2023). Detection of network attacks using machine learning and deep learning models. Procedia Computer Science, 218:57–66.
Gaber, T., El-Ghamry, A., and Hassanien, A. E. (2022). Injection attack detection using machine learning for smart iot applications. Physical Communication, 52:101685.
Gatefy (2021). Como o covid-19 impactou os crimes cibernéticos, segundo a europol. Acesso em: 05 mar. 2023.
Hnamte, V. and Hussain, J. (2023). Dcnnbilstm: An efficient hybrid deep learning-based intrusion detection system. Telematics and Informatics Reports, 10:100053.
Jain, S., Pawar, P. M., and Muthalagu, R. (2022). Hybrid intelligent intrusion detection system for internet of things. Telematics and Informatics Reports, 8:100030.
Kanimozhi, V. and Jacob, T. P. (2021). Artificial intelligence outflanks all other machine learning classifiers in network intrusion detection system on the realistic cyber dataset cse-cic-ids2018 using cloud computing. ICT Express, 7(3):366–370.
Karthika, R. and Maheswari, M. (2022). Detection analysis of malicious cyber attacks using machine learning algorithms. Materials Today: Proceedings, 68:26–34. 6th International Conference on Recent Advances in Material Chemistry.
Kilincer, I. F., Ertam, F., and Sengur, A. (2021). Machine learning methods for cyber security intrusion detection: Datasets and comparative study. Computer Networks, 188:107840.
Kim, J., Shin, N., Jo, S. Y., and Kim, S. H. (2017). Method of intrusion detection using deep neural network. In 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), pages 313–316, Jeju. IEEE.
Kitchenham, B. A. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering (ebse 2007-001). Technical report, Keele University and Durham University Joint Report.
Leevy, J. L., Hancock, J., Zuech, R., and Khoshgoftaar, T. M. (2021). Detecting cybersecurity attacks across different network features and learners. Journal of Big Data, 8(1):38.
Lucas, T., Costa, K., Moraes, E., Júnior, P. H., and Neves, M. (2021). Stacking-based committees para detecção de ataques em redes de computadores - uma abordagem por exaustão. In Anais do XXXIX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 644–657, Porto Alegre, RS, Brasil. SBC.
Mushtaq, E., Zameer, A., and Khan, A. (2022). A two-stage stacked ensemble intrusion detection system using five base classifiers and mlp with optimal feature selection. Microprocessors and Microsystems, 94:104660.
Patgiri, R., Varshney, U., Akutota, T., and Kunde, R. (2018). An investigation on intrusion detection system using machine learning. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1684–1691.
Report, S. (2023). Ataques cibernéticos no trabalho remoto mais que triplicaram durante a pandemia.
Sarhan, M., Layeghy, S., Moustafa, N., and Portmann, M. (2022). Towards a standard feature set of nids datasets. Mobile Networks and Applications.
Sayed, M. S. E., Le-Khac, N.-A., Azer, M. A., and Jurcut, A. D. (2022). A flow-based anomaly detection approach with feature selection method against ddos attacks in sdns. IEEE Transactions on Cognitive Communications and Networking, 8(4):1862–1880.
Siddiqi, M. A. and Pak, W. (2022). Tier-based optimization for synthesized network intrusion detection system. IEEE Access, 10:108530–108544.
Sousa, W. T. M. and Silva, C. A. (2022). Análise de desempenho em algoritmos de aprendizagem de máquina na detecção de intrusão baseada em fluxo de rede usando o conjunto de dados unsw-nb15. Revista de Sistemas e Computação, 12(2):51–57.
Vishwakarma, M. and Kesswani, N. (2023). A new two-phase intrusion detection system with naı̈ve bayes machine learning for data classification and elliptic envelop method for anomaly detection. Decision Analytics Journal, 7:100233.
Wang, J. A., Guo, M., Wang, H., and Zhou, L. (2012). Measuring and ranking attacks based on vulnerability analysis. Information Systems and e-Business Management, 10(4):455–490.
Wu, Z., Zhang, H., Wang, P., and Sun, Z. (2022). Rtids: A robust transformer-based approach for intrusion detection system. IEEE Access, 10:64375–64387.
Publicado
23/11/2023
Como Citar
DA SILVA, Gabrielly; OLIVEIRA, Carina; BRAGA, Reinaldo.
Um Mapeamento Sistemático sobre Detecção de Ataques em Redes de Computadores. In: ESCOLA REGIONAL DE COMPUTAÇÃO DO CEARÁ, MARANHÃO E PIAUÍ (ERCEMAPI), 11. , 2023, Aracati/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 11-20.
DOI: https://doi.org/10.5753/ercemapi.2023.236238.
