Localização de Dispositivos Maliciosos usando Veículos Aéreos não Tripulados
Resumo
O acesso a internet em ambientes públicos permite que os usuários acessem seus dados online de qualquer lugar. Entretanto, esses ambientes podem facilitar a ação de agentes maliciosos interessados em promover ataques a rede e, por serem locais públicos, isso dificulta a localização desses atacantes. Hoje em dia é possível detectar um ataque e, com isso, um conjunto de informações sobre o dispositivo usado nesta ação pode ser identificado, o que permite que o acesso seja bloqueado. Essas informações não são suficientes para determinar a localização física do atacante, o que é essencial para impedi-lo de realizar ataques futuros, mas podem auxiliar nessa tarefa. Sendo assim, este trabalho propõe uma técnica de localização aproximada de um dispositivo malicioso em uma rede, utilizando trilateração para localizar o alvo, com o auxílio de um veículo aéreo não tripulado. Os resultados indicam que a solução proposta é capaz de encontrar o dispositivo malicioso
Palavras-chave:
Localização de dispositivos maliciosos, drone, veículos aéreos não tripulados, sistemas de detecção de intrusão, trilateração
Referências
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Al-Hourani, A. and Gomez, K. (2017). Modeling cellular-to-uav path-loss for suburbanenvironments.IEEE Wireless Communications Letters, 7(1):82–85.
Alotaibi, B. and Elleithy, K. (2016). Rogue access point detection: Taxonomy, challenges,and future directions.Wireless Personal Communications, 90(3):1261–1290.
Brutch, P. and Ko, C. (2003). Challenges in intrusion detection for wireless ad-hocnetworks. In2003 Symposium on Applications and the Internet Workshops, 2003.Proceedings., pages 368–373. IEEE.
Chamoso, P., Gonz ález-Briones, A., Rodríguez, S., and Corchado, J. M. (2018). Tendencies of technologies and platforms in smart cities: a state-of-the-art review.Wireless Communications and Mobile Computing, 2018.
Cirqueira, A. C., Andrade, R. M. C., and CASTRO, M. F. (2011). Um mecanismo de segurança com adaptação dinâmica em tempo de execução para dispositivos móveis.XXXVIII Seminário Integrado de Software e Hardware (SEMISH)
Courtay, A., Le Gentil, M., Berder, O., Scalart, P., Fontaine, S., and Carer, A. (2019).Anchor selection algorithm for mobile indoor positioning using wsn with uwb radio.In2019 IEEE Sensors Applications Symposium (SAS), pages 1–5. IEEE.
Halder, S. and Ghosal, A. (2016). A survey on mobile anchor assisted localization techniques in wireless sensor networks.Wireless Networks, 22(7):2317–2336.
Koutsonikolas, D., Das, S. M., and Hu, Y. C. (2007). Path planning of mobile land marks for localization in wireless sensor networks.Computer Communications, 30(13).
Meng, Y., Li, J., Zhu, H., Liang, X., Liu, Y., and Ruan, N. (2019). Revealing your mobile password via wifi signals: Attacks and counter measures.IEEE Transactions on Mobile Computing.
Mozaffari, M., Saad, W., Bennis, M., Nam, Y.-H., and Debbah, M. (2019). A tutorial on uavs for wireless networks: Applications, challenges, and open problems. IEEE Communications Surveys & Tutorials.
Murphy, W. and Hereman, W. (1995). Determination of a position in three dimensions using trilateration and approximate distances.Department of Mathematical and Com-puter Sciences, Colorado School of Mines, Golden, Colorado, MCS-95, 7:19.
Nobles, P., Ali, S., and Chivers, H. (2011). Improved estimation of trilateration distancesfor indoor wireless intrusion detection.JoWUA, 2(1):93–102.
Riley, G. F. and Henderson, T. R. (2010). The ns-3 network simulator. InModeling andtools for network simulation, pages 15–34. Springer.
Sorbelli, F. B., Das, S. K., Pinotti, C. M., and Silvestri, S. (2018). Range based algorithmsfor precise localization of terrestrial objects using a drone.Pervasive and Mobile Com-puting, 48:20–42.
Sun, Y., Wen, X., Lu, Z., Lei, T., and Jiang, S. (2018). Localization of wifi devicesusing unmanned aerial vehicles in search and rescue. In2018 IEEE/CIC International Conference on Communications in China (ICCC Workshops), pages 147–152. IEEE.
Tidjon, L. N., Frappier, M., and Mammar, A. (2019). Intrusion detection systems: Across-domain overview.IEEE Communications Surveys & Tutorials.
Watts, S. (2016). Secure authentication is the only solution for vulnerable public wifi.Computer Fraud & Security, 2016(1):18–20.
Acuna, V., Kumbhar, A., Vattapparamban, E., Rajabli, F., and Guvenc, I. (2017). Lo-calization of wifi devices using probe requests captured at unmanned aerial vehicles.In2017 IEEE Wireless Communications and Networking Conference (WCNC), pages1–6. IEEE.
Al-Hourani, A. and Gomez, K. (2017). Modeling cellular-to-uav path-loss for suburbanenvironments.IEEE Wireless Communications Letters, 7(1):82–85.
Alotaibi, B. and Elleithy, K. (2016). Rogue access point detection: Taxonomy, challenges,and future directions.Wireless Personal Communications, 90(3):1261–1290.
Brutch, P. and Ko, C. (2003). Challenges in intrusion detection for wireless ad-hocnetworks. In2003 Symposium on Applications and the Internet Workshops, 2003.Proceedings., pages 368–373. IEEE.
Chamoso, P., Gonz ález-Briones, A., Rodríguez, S., and Corchado, J. M. (2018). Tendencies of technologies and platforms in smart cities: a state-of-the-art review.Wireless Communications and Mobile Computing, 2018.
Cirqueira, A. C., Andrade, R. M. C., and CASTRO, M. F. (2011). Um mecanismo de segurança com adaptação dinâmica em tempo de execução para dispositivos móveis.XXXVIII Seminário Integrado de Software e Hardware (SEMISH)
Courtay, A., Le Gentil, M., Berder, O., Scalart, P., Fontaine, S., and Carer, A. (2019).Anchor selection algorithm for mobile indoor positioning using wsn with uwb radio.In2019 IEEE Sensors Applications Symposium (SAS), pages 1–5. IEEE.
Halder, S. and Ghosal, A. (2016). A survey on mobile anchor assisted localization techniques in wireless sensor networks.Wireless Networks, 22(7):2317–2336.
Koutsonikolas, D., Das, S. M., and Hu, Y. C. (2007). Path planning of mobile land marks for localization in wireless sensor networks.Computer Communications, 30(13).
Meng, Y., Li, J., Zhu, H., Liang, X., Liu, Y., and Ruan, N. (2019). Revealing your mobile password via wifi signals: Attacks and counter measures.IEEE Transactions on Mobile Computing.
Mozaffari, M., Saad, W., Bennis, M., Nam, Y.-H., and Debbah, M. (2019). A tutorial on uavs for wireless networks: Applications, challenges, and open problems. IEEE Communications Surveys & Tutorials.
Murphy, W. and Hereman, W. (1995). Determination of a position in three dimensions using trilateration and approximate distances.Department of Mathematical and Com-puter Sciences, Colorado School of Mines, Golden, Colorado, MCS-95, 7:19.
Nobles, P., Ali, S., and Chivers, H. (2011). Improved estimation of trilateration distancesfor indoor wireless intrusion detection.JoWUA, 2(1):93–102.
Riley, G. F. and Henderson, T. R. (2010). The ns-3 network simulator. InModeling andtools for network simulation, pages 15–34. Springer.
Sorbelli, F. B., Das, S. K., Pinotti, C. M., and Silvestri, S. (2018). Range based algorithmsfor precise localization of terrestrial objects using a drone.Pervasive and Mobile Com-puting, 48:20–42.
Sun, Y., Wen, X., Lu, Z., Lei, T., and Jiang, S. (2018). Localization of wifi devicesusing unmanned aerial vehicles in search and rescue. In2018 IEEE/CIC International Conference on Communications in China (ICCC Workshops), pages 147–152. IEEE.
Tidjon, L. N., Frappier, M., and Mammar, A. (2019). Intrusion detection systems: Across-domain overview.IEEE Communications Surveys & Tutorials.
Watts, S. (2016). Secure authentication is the only solution for vulnerable public wifi.Computer Fraud & Security, 2016(1):18–20.
Publicado
30/06/2020
Como Citar
COSTA JUNIOR, Evilasio; GOMES, Rafael; ROCHA, Leonardo; ANDRADE, Rossana.
Localização de Dispositivos Maliciosos usando Veículos Aéreos não Tripulados. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 12. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 141-150.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2020.11220.
