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Makine Öğrenmesi Yöntemleri Kullanılarak Nesnelerin İnterneti Ağlarında DIS Flooding Saldırılarının Tespiti

Yıl 2021, Sayı: 28, 1317 - 1320, 30.11.2021
https://doi.org/10.31590/ejosat.1014917

Öz

Günümüzde Nesnelerin İnterneti (Internet of Things, IoT) geniş bir kullanım alanına sahip olup insan müdahalesi olmaksızın birbirleriyle haberleşebilen akıllı nesnelerle hayatımızı kolaylaştırmaktadır. Ancak Kablosuz Algılayıcı Ağlar’da olduğu gibi, IoT ağları da yeni riskleri beraberinde getirmektedir. Endişe verici boyutlara ulaşan bu riskler, ağ topolojisinde güvenlik, gizlilik ve enerji gibi bazı önemli sorunlara neden olmaktadır. Düşük Güç ve Kayıplı Ağlar için IPv6 Yönlendirme Protokolü (RPL), IoT ağlarındaki kaynak kısıtlı cihazlar için bir yönlendirme protokolüdür. Düğümler arasında iletilen paketler bir dizi saldırıya maruz kalabilir. DODAG Information Solicitation (DIS) Flooding saldırısı, bu protokole karşı en etkili saldırı türlerinden biridir ve ağ içerisinde yer alan düğümlerin enerji seviyesini ve işlem kapasitelerini olumsuz etkiler. IoT güvenliğinde saldırıları tespit etmek için birçok saldırı tespit yöntemi kullanılsa da yenilikçi ve enerji korunumlu yöntemlere ihtiyaç duyulmaktadır. DIS Flooding saldırılarını tespit etme ve önleme yöntemleri literatürde yeterince ele alınmamıştır. Söz konusu eksikliği gidermek için bu çalışmada, Lojistik Regresyon (LR) ve Destek Vektör Makinesi yöntemleri kullanılarak DIS Flooding saldırılarının yüksek doğruluk oranı ile tespiti gerçekleştirilmiştir. Çalışmada Contiki-Cooja simülasyon ortamı kullanılmış ve deneysel sonuçlar çeşitli performans ölçütleri kullanılarak değerlendirilmiştir. Değerlendirme sonucuna göre, LR yöntemi DIS Flooding saldırı tespitini daha yüksek başarım ile gerçekleştirmiştir.

Kaynakça

  • A. Rayes, S. Salam, Chapter 1 Internet of Things (IoT) Overview, Internet of Things From Hype to Reality, Springer Nature Switzerland AG, pp. 1-35, 2019. https://doi.org/10.1007/978-3-319-99516-8_1
  • A. Verma and V. Ranga, “Mitigation of DIS flooding attacks in RPL-based 6LoWPAN net-works,” Trans Emerging Tel Tech., vol. 31(2), e3802, pp. 1-25, 2020. https://doi.org/10.1002/ett.3802
  • V. Odumuyiwa and R. Alabi, “DDOS Detection on Internet of Things Using Unsupervised Algorithms”, Journal of Cyber Security and Mobility, vol. 10, no. 3, pp. 569-592, 2021. https://doi.org/10.13052/ jcsm2245-1439.1034
  • S. Cakir, S. Toklu, and N. Yalcin, “RPL Attack Detection and Prevention in the Internet of Things Networks Using a GRU Based Deep Learning,” IEEE Access, vol. 8, pp. 183678-183689, 2020. https://doi.org/10.1109/ACCESS.2020.3029191
  • F. S. De Lima Filho, F. A. F. Silveira, A. De Medeiros Brito Junior, G. Vargas-Solar, and L. F. Silveira, “Smart Detection: An Online Approach for DoS/DDoS Attack Detection Using Machine Learning,” Security and Communication Networks, vol. 2019, pp. 1-15, 2019. https://doi.org/10.1155/2019/1574749
  • R. Abubakar, A. Aldegheishem, M. Majeed, A. Mehmood, N. Alrajeh, and M. Carsten, “An Effective Mechanism to Mitigate Real-time DDoS Attack Using Dataset”, IEEE Access, vol. 8, pp. 126215-126227, 2020. https://doi.org/10.1109/ACCESS.2020.2995820
  • S. Sambangi and L. Gondi, “A Machine Learning Approach for DDoS (Distributed Denial of Service) Attack Detection Using Multiple Linear Regression,” in Multidisciplinary Digital Publishing Institute Proc., vol. 63, p. 51, https://doi.org/10.3390/proceedings2020063051
  • A. Saied, R. E. Overill, and T. Radzik, “Detection of known and unknown DDoS attacks using Artificial Neural Networks,” Neurocomputing, vol. 172, pp. 385-393, 2016. https://doi.org/ 10.1016/j.neucom.2015.04.101
  • M. Sharma, H. Elmiligi, F. Gebali, and A. Verma, “Simulating Attacks for RPL and Generating Multi-class Dataset for Supervised Machine Learning,” in 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 20-26, 2019. https://doi.org/10.1109 /IEMCON.2019.8936142
  • M. Mounica, R. Vijayasaraswathi, and R. Vasavi, “Detecting Sybil Attack in Wireless Sensor Networks Using Machine Learning Algorithms,” in IOP Conference Series: Materials Science and Engineering, vol. 1042, no. 1, p. 012029, 2021. https://doi.org/ 10.1088/ 1757-899X/1042/1/012029
  • S. Murali and A. Jamalipour, “A Lightweight Intrusion Detection for Sybil Attack Under Mobile RPL in the Internet of Things,” in IEEE Internet of Things Journal, vol. 7, no. 1, pp. 379-388, Jan. 2020, https://doi.org/10.1109/JIOT.2019.2948149
  • N. Müller, P. Debus, D. Kowatsch, and K. Böttinger, ‘‘Distributed anomaly detection of single mote attacks in RPL networks,’’ in Proc. 16th Int. Joint Conf. e-Bus. Telecommun., vol. 2, pp. 378-385, 2019. https://doi.org/10.5220/0007836003780385
  • A. Mayzaud, R. Badonnel, and I. Chrisment, “A taxonomy of attacks in RPL-based Internet of Things”, International Journal of Network Security, vol. 18, no. 3, pp. 459-473, 2016. https://doi.org/10.6633/ IJNS.201605.18(3).07

Detection of DIS Flooding Attacks in IoT Networks Using Machine Learning Methods

Yıl 2021, Sayı: 28, 1317 - 1320, 30.11.2021
https://doi.org/10.31590/ejosat.1014917

Öz

In today, Internet of Things (IoT) has a wide usage area and makes easier our lives with smart objects that can communicate with each other without human intervention. However, as with Wireless Sensor Networks, IoT networks bring new risks. These risks reaching worrying levels cause some significant issues such as security, privacy, and energy in the network topology. The IPv6 Routing Protocol for Low-Power and Lossy Network (RPL) is a routing protocol for resource-constrained devices in IoT networks. When it transmits packets between nodes, the nodes can be exposed to a series of attacks. DODAG Information Solicitation (DIS) Flooding attack is one of the most effective types of attacks against this protocol and negatively affects the energy level of the node and its limited processing capacities. Although many intrusion detection methods are used to detect attacks in IoT security, innovative and energy-saving methods are needed. DIS Flooding attacks detection and prevention methods have not been adequately presented in the literature. To address the mentioned need, this study provides high-performance detection of DIS Flooding attacks by applying Logical Regression (LR) and Support Vector Machine machine learning methods. The experiments are implemented by using the Contiki-Cooja simulation environment and the experimental results have been evaluated using various performance metrics. It can be concluded that LR achieves higher attack detection in terms of accuracy.

Kaynakça

  • A. Rayes, S. Salam, Chapter 1 Internet of Things (IoT) Overview, Internet of Things From Hype to Reality, Springer Nature Switzerland AG, pp. 1-35, 2019. https://doi.org/10.1007/978-3-319-99516-8_1
  • A. Verma and V. Ranga, “Mitigation of DIS flooding attacks in RPL-based 6LoWPAN net-works,” Trans Emerging Tel Tech., vol. 31(2), e3802, pp. 1-25, 2020. https://doi.org/10.1002/ett.3802
  • V. Odumuyiwa and R. Alabi, “DDOS Detection on Internet of Things Using Unsupervised Algorithms”, Journal of Cyber Security and Mobility, vol. 10, no. 3, pp. 569-592, 2021. https://doi.org/10.13052/ jcsm2245-1439.1034
  • S. Cakir, S. Toklu, and N. Yalcin, “RPL Attack Detection and Prevention in the Internet of Things Networks Using a GRU Based Deep Learning,” IEEE Access, vol. 8, pp. 183678-183689, 2020. https://doi.org/10.1109/ACCESS.2020.3029191
  • F. S. De Lima Filho, F. A. F. Silveira, A. De Medeiros Brito Junior, G. Vargas-Solar, and L. F. Silveira, “Smart Detection: An Online Approach for DoS/DDoS Attack Detection Using Machine Learning,” Security and Communication Networks, vol. 2019, pp. 1-15, 2019. https://doi.org/10.1155/2019/1574749
  • R. Abubakar, A. Aldegheishem, M. Majeed, A. Mehmood, N. Alrajeh, and M. Carsten, “An Effective Mechanism to Mitigate Real-time DDoS Attack Using Dataset”, IEEE Access, vol. 8, pp. 126215-126227, 2020. https://doi.org/10.1109/ACCESS.2020.2995820
  • S. Sambangi and L. Gondi, “A Machine Learning Approach for DDoS (Distributed Denial of Service) Attack Detection Using Multiple Linear Regression,” in Multidisciplinary Digital Publishing Institute Proc., vol. 63, p. 51, https://doi.org/10.3390/proceedings2020063051
  • A. Saied, R. E. Overill, and T. Radzik, “Detection of known and unknown DDoS attacks using Artificial Neural Networks,” Neurocomputing, vol. 172, pp. 385-393, 2016. https://doi.org/ 10.1016/j.neucom.2015.04.101
  • M. Sharma, H. Elmiligi, F. Gebali, and A. Verma, “Simulating Attacks for RPL and Generating Multi-class Dataset for Supervised Machine Learning,” in 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 20-26, 2019. https://doi.org/10.1109 /IEMCON.2019.8936142
  • M. Mounica, R. Vijayasaraswathi, and R. Vasavi, “Detecting Sybil Attack in Wireless Sensor Networks Using Machine Learning Algorithms,” in IOP Conference Series: Materials Science and Engineering, vol. 1042, no. 1, p. 012029, 2021. https://doi.org/ 10.1088/ 1757-899X/1042/1/012029
  • S. Murali and A. Jamalipour, “A Lightweight Intrusion Detection for Sybil Attack Under Mobile RPL in the Internet of Things,” in IEEE Internet of Things Journal, vol. 7, no. 1, pp. 379-388, Jan. 2020, https://doi.org/10.1109/JIOT.2019.2948149
  • N. Müller, P. Debus, D. Kowatsch, and K. Böttinger, ‘‘Distributed anomaly detection of single mote attacks in RPL networks,’’ in Proc. 16th Int. Joint Conf. e-Bus. Telecommun., vol. 2, pp. 378-385, 2019. https://doi.org/10.5220/0007836003780385
  • A. Mayzaud, R. Badonnel, and I. Chrisment, “A taxonomy of attacks in RPL-based Internet of Things”, International Journal of Network Security, vol. 18, no. 3, pp. 459-473, 2016. https://doi.org/10.6633/ IJNS.201605.18(3).07
Toplam 13 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Semih Çakır 0000-0003-3072-9532

Nesibe Yalçın 0000-0003-0324-9111

Yayımlanma Tarihi 30 Kasım 2021
Yayımlandığı Sayı Yıl 2021 Sayı: 28

Kaynak Göster

APA Çakır, S., & Yalçın, N. (2021). Detection of DIS Flooding Attacks in IoT Networks Using Machine Learning Methods. Avrupa Bilim Ve Teknoloji Dergisi(28), 1317-1320. https://doi.org/10.31590/ejosat.1014917