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Defending SDN against packet injection attacks using deep learning

- Phu, Anh, Li, Bo, Ullah, Faheem, Ul Huque, Tanvir, Naha, Ranesh, Babar, Muhammad, Nguyen, Hung

  • Authors: Phu, Anh , Li, Bo , Ullah, Faheem , Ul Huque, Tanvir , Naha, Ranesh , Babar, Muhammad , Nguyen, Hung
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Computer Networks Vol. 234, no. (2023), p.
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  • Description: The (logically) centralized architecture of software-defined networks makes them an easy target for packet injection attacks. In these attacks, the attacker injects malicious packets into the SDN network to affect the services and performance of the SDN controller and overflows the capacity of the SDN switches. Such attacks have been shown to ultimately stop the network functioning in real-time, leading to network breakdowns. There have been significant works on detecting and defending against similar DoS attacks in non-SDN networks, but detection and protection techniques for SDN against packet injection attacks are still in their infancy. Furthermore, many of the proposed solutions have been shown to be easily bypassed by simple modifications to the attacking packets or by altering the attacking profile. In this paper, we develop novel Graph Convolutional Neural Network models and algorithms for grouping network nodes/users into security classes by learning from network data. We start with two simple classes — nodes that engage in suspicious packet injection attacks and nodes that are not. From these classes, we then partition the network into separate segments with different security policies using distributed Ryu controllers in an SDN network. We show in experiments on an emulated SDN that our detection solution outperforms alternative approaches with above 99% detection accuracy for various types (both old and new) of injection attacks. More importantly, our mitigation solution maintains continuous functions of non-compromised nodes while isolating compromised/suspicious nodes in real-time. All code and data are publicly available for the reproducibility of our results. © 2023 The Author(s)

Securing internet of medical things with friendly-jamming schemes

- Li, Xuran, Dai, Hong-Ning, Wang, Qubeijian, Imran, Muhammad, Li, Dengwang

  • Authors: Li, Xuran , Dai, Hong-Ning , Wang, Qubeijian , Imran, Muhammad , Li, Dengwang
  • Date: 2020
  • Type: Text , Journal article , Review
  • Relation: Computer Communications Vol. 160, no. (2020), p. 431-442
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  • Description: The Internet of Medical Things (IoMT)-enabled e-healthcare can complement traditional medical treatments in a flexible and convenient manner. However, security and privacy become the main concerns of IoMT due to the limited computational capability, memory space and energy constraint of medical sensors, leading to the in-feasibility for conventional cryptographic approaches, which are often computationally-complicated. In contrast to cryptographic approaches, friendly jamming (Fri-jam) schemes will not cause extra computing cost to medical sensors, thereby becoming potential countermeasures to ensure security of IoMT. In this paper, we present a study on using Fri-jam schemes in IoMT. We first analyze the data security in IoMT and discuss the challenges. We then propose using Fri-jam schemes to protect the confidential medical data of patients collected by medical sensors from being eavesdropped. We also discuss the integration of Fri-jam schemes with various communication technologies, including beamforming, Simultaneous Wireless Information and Power Transfer (SWIPT) and full duplexity. Moreover, we present two case studies of Fri-jam schemes in IoMT. The results of these two case studies indicate that the Fri-jam method will significantly decrease the eavesdropping risk while leading to no significant influence on legitimate transmission. © 2020
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